Development of a metabolic pathway-based pseudo-targeted metabolomics method using liquid chromatography coupled with mass spectrometry

Strategy for characterization and quantification of fatty acids in plasma by parallel d0/d6-dansylhydrazine derivatization combined with UPLC-MS/MS

Fatty acids (FAs) play a vital physiological role in lipid metabolism, which is reported as potential diagnostic biomarker for various diseases. Thus, it is urgent to develop a credible method that can profile FA metabolism with a holistic view. Here, a targeted strategy to screen FAs was developed by parallel labeling with d0/d6-dansylhydrazine (d0/d6-DnsHz) and using ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-MS/MS) in data-dependent MS/MS (ddMS2) mode. The simple and mild derivatization procedure within 3 h allowed for a significant improvement in sensitivity. Additionally, the characteristic product ions introduced by the derivatization reagent assist to identify the unknown FA species. A quantitation method was established by multiple reaction monitoring (MRM) and the d6-DnsHz tagged standards for each analyte were used as internal standards to overcome the matrix effects. By applying the method to determine FA levels in plasma collected from the esophageal squamous cell carcinoma (ESCC) patients and healthy controls, 65 FA metabolites were characterized and six FAs were found to be altered by the invasion of tumors. The parallel derivatization strategy provides insights into the identification of unknown FAs and paves a new way for targeted metabolomics. Also, this novel method is a powerful tool for characterization and quantification of FAs in biological samples, which shows a great potential application in clinical diagnosis and investigation of disease mechanisms.

An Individualized Prognostic Model in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma Based on Serum Metabolomic Profiling

PURPOSE

This study aims to evaluate the value of a serum metabolomics-based metabolic signature for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, thereby assisting clinical decisions.

METHODS

In this retrospective study, a total of 320 LA-NPC patients were randomly divided into a training set (ca. 70%; n = 224) and a validation set (ca. 30%; n = 96). Serum samples were analyzed using widely targeted metabolomics. Univariate and multivariate Cox regression analyses were used to identify candidate metabolites related to progression-free survival (PFS). Patients were categorized into high-risk and low-risk groups based on the median metabolic risk score (Met score), and the PFS difference between the two groups was compared using Kaplan-Meier curves. The predictive performance of the metabolic signature was evaluated using the concordance index (C-index) and the time-dependent receiver operating characteristic (ROC), and a comprehensive nomogram was constructed using the Met score and other clinical factors.

RESULTS

Nine metabolites were screened to build the metabolic signature and generate the Met score, which effectively separated patients into low- and high-risk groups. The C-index in the training and validation sets was 0.71 and 0.73, respectively. The 5-year PFS was 53.7% (95% CI, 45.12-63.86) in the high-risk group and 83.0% (95%CI, 76.31-90.26) in the low-risk group. During the construction of the nomogram, Met score, clinical stage, pre-treatment EBV DNA level, and gender were identified as independent prognostic factors for PFS. The predictive performance of the comprehensive model was better than that of the traditional model.

CONCLUSION

The metabolic signature developed through serum metabolomics is a reliable prognostic indicator of PFS in LA-NPC patients and has important clinical significance.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Identification of Metabolite Interference Is Necessary for Accurate LC-MS Targeted Metabolomics Analysis

Targeted metabolomics has been broadly used for metabolite measurement due to its good quantitative linearity and simple metabolite annotation workflow. However, metabolite interference, the phenomenon where one metabolite generates a peak in another metabolite's MRM setting (Q1/Q3) with a close retention time (RT), may lead to inaccurate metabolite annotation and quantification. Besides isomeric metabolites having the same precursor and product ions that may interfere with each other, we found other metabolite interferences as the result of inadequate mass resolution of triple-quadruple mass spectrometry and in-source fragmentation of metabolite ions. Characterizing the targeted metabolomics data using 334 metabolite standards revealed that about 75% of the metabolites generated measurable signals in at least one other metabolite's MRM setting. Different chromatography techniques can resolve 65-85% of these interfering signals among standards. Metabolite interference analysis combined with the manual inspection of cell lysate and serum data suggested that about 10% out of ∼180 annotated metabolites were mis-annotated or mis-quantified. These results highlight that a thorough investigation of metabolite interference is necessary for accurate metabolite measurement in targeted metabolomics.

Study on mechanism of hepatoprotective effect of Chrysanthemum morifolium Ramat. based on metabolomics with network analysis and network pharmacology

Hangju (HJ), the dried flower heads of Chrysanthemum morifolium Ramat., has a significant hepatoprotective effect. However, its underlying protection mechanism against acute liver injury (ALI) has been unclear. An integrated strategy based on metabolomics with network analysis and network pharmacology was developed to explore the potential molecular mechanism of HJ on ALI protection. Firstly, differential endogenous metabolites were screened and identified by metabolomics approach and metabolic pathway analysis was performed by MetaboAnalyst. Secondly, marker metabolites were used to construct metabolite-response-enzyme-gene networks and discover hub metabolites and potential gene targets in network analysis. Thirdly, hub genes through the protein-protein interaction (PPI) network were acquired by the aid of network pharmacology. Finally, the gene targets were taken to intersect with the relevant active ingredients for validation by molecular docking. In total, 48 flavonoids were identified in HJ, which were associated with 8 potential therapeutic targets in network pharmacological analysis. Biochemistry and histopathology analysis demonstrated that HJ exerted hepatoprotective effects. 28 biomarkers were successfully identified as possible biomarkers for the prevention of ALI. The sphingolipid metabolic pathway and the glycerophospholipid metabolic pathway was considered a crucial signaling pathway by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In addition, phosphatidylcholine and sphingomyelin were considered as hub metabolites. Twelve enzymes and 38 genes were considered as potential targets in the network analysis. Based on the combined analysis above, HJ was shown to modulate 2 key upstream targets, including PLA2G2A and PLA2G4A. Molecular docking showed that active compounds of HJ had high binding affinity with these key targets. In conclusion, the flavonoid components of HJ can inhibit PLA2 and regulate glycerophospholipid and sphingolipid metabolism pathway to delay the pathological process of ALI, which may be a potential mechanism of HJ against ALI.

Development of Pseudo-targeted Profiling of Isotopic Metabolomics using Combined Platform of High Resolution Mass Spectrometry and Triple Quadrupole Mass Spectrometry with Application of 13C6-Glucose Tracing in HepG2 Cells

Isotope tracing assisted metabolic analysis is becoming a unique tool to understand metabolic regulation in cell biology and biomedical research. Targeted mass spectrometry analysis based on selected reaction monitoring (SRM) has been widely applied in isotope tracing experiment with the advantages of high sensitivity and broad linearity. However, its application for new pathway discovery is largely restrained by molecular coverage. To overcome this limitation, we describe a strategy called pseudo-targeted profiling of isotopic metabolomics (PtPIM) to expand the analysis of isotope labeled metabolites beyond the limit of known pathways and chemical standards. Pseudo-targeted metabolomics was first established with ion transitions and retention times transformed from high resolution (orbitrap) mass spectrometry. Isotope labeled MRM transitions were then generated according to chemical formulas of fragments, which were derived from accurate ion masses acquired by HRMS. An in-house software "PseudoIsoMRM" was developed to simulate isotope labeled ion transitions in batch mode and correct the interference of natural isotopologues. This PtPIM strategy was successfully applied to study ¹³C₆-glucose traced HepG2 cells. As 313 molecules determined as analysis targets, a total of 4104 ion transitions were simulated to monitor ¹³C labeled metabolites in positive-negative switching mode of QQQ mass spectrometer with minimum dwell time of 0.3 ms achieved. A total of 68 metabolites covering glycolysis, TCA cycle, nucleotide biosynthesis, one-carbon metabolism and related derivatives were found to be labeled (> 2%) in HepG2 cells. Active pentose phosphate pathway was observed with diverse labeling status of glycolysis intermediates. Meanwhile, our PtPIM strategy revealed that rotenone severely suppressed mitochondrial function e.g. oxidative phosphorylation and fatty acid beta-oxidation. In this case, anaerobic respiration became the major source of energy metabolism by producing abundant lactate. Conclusively, the simulation based PtPIM method demonstrates a strategy to broaden metabolite coverage in isotope tracing analysis independent of standard chemicals.

Ultra-HPLC-MS pseudo-targeted metabolomic profiling reveals metabolites and associated metabolic pathway alterations in Asian plum (Prunus salicina) fruits in response to gummosis disease

Plum (Prunus spp.) is an economically and nutritionally important stone fruit that is grown worldwide. Gummosis disease (GD) is one of the most common limiting factors that adversely affects the yield and quality of stone fruits such as plum. Elucidating plum fruit metabolomics responses is essential to develop sustainable agricultural practices to combat GD in the future. Herein, an ultra-high-performance liquid chromatography coupled to mass-spectrometry (UHPLC-MS) pseudo-targeted metabolomic profiling was first performed to elucidate the overall metabolic alterations in Asian plum (Prunus salicina Lindl.) fruit in response to GD. The most pivotal differential metabolites, including certain amino acids and proanthocyanidins, in GD and control groups were identified by combining multivariate data analysis with strict statistical criteria. Metabolic pathway enrichment analysis showed that GD induced a series of coordinated defence responses and reprogramming of various metabolic pathways, including glucosinolate biosynthesis, 2-oxocarboxylic acid metabolism, valine, leucine and isoleucine degradation, and isoquinoline alkaloid biosynthesis pathways. Using UHPLC-MS-based pseudo-targeted metabolomic profiling, we systematically evaluated overall metabolic modifications in Asian plum fruits in response to GD for the first time. The identified metabolic pathway alterations helped to better understand the internal relationships and related metabolic networks.

Targeting Strategies for Aberrant Lipid Metabolism Reprogramming and the Immune Microenvironment in Esophageal Cancer: A Review

Esophageal cancer is of high importance to occurrence, development, and treatment resistance. As evidenced by recent studies, pathways (e.g., Wnt/β-catenin, AMPK, and Hippo) are critical to the proliferation, differentiation, and self-renewal of esophageal cancer. In addition, the above pathways play a certain role in regulating esophageal cancer and act as potential therapeutic targets. Over the past few years, the function of lipid metabolism in controlling tumor cells and immune cells has aroused extensive attention. It has been reported that there are intricate interactions between lipid metabolism reprogramming between immune and esophageal cancer cells, whereas molecular mechanisms should be studied in depth. Immune cells have been commonly recognized as a vital player in the esophageal cancer microenvironment, having complex crosstalk with cancer cells. It is increasingly evidenced that the function of immune cells in the tumor microenvironment (TME) is significantly correlated with abnormal lipid metabolism. In this review, the latest findings in lipid metabolism reprogramming in TME are summarized, and the above findings are linked to esophageal cancer progression. Aberrant lipid metabolism and associated signaling pathways are likely to serve as a novel strategy to treat esophageal cancer through lipid metabolism reprogramming.

Comprehensive pseudotargeted metabolomics analysis based on two-phase liquid extraction-UHPLC-MS/MS for the investigation of depressive rats

Pseudotargeted analysis combines the advantages of untargeted and targeted metabolomics methods. This study proposed a comprehensive pseudotargeted metabolomics method based on two-phase liquid extraction using ultra-high-performance liquid chromatography-tandem mass spectrometry. Two-phase liquid extraction, composed of both aqueous and organic phases, extracted a wide range of metabolites from polar to nonpolar in plasma samples. Besides, the two phases were combined and detected in a single injection to save analytical time. A total of 486 potential metabolites were detected by the developed approach. Compared with the conventional methanol-based protein precipitation method, the two-phase liquid extraction method significantly increased the metabolite coverage by 20.29%. Besides, the proposed pseudotargeted metabolomics method exhibited higher sensitivity and better repeatability than the untargeted method. Finally, we applied the established pseudotargeted method to the metabolomics study of depressive rats and screened 53 differential variables. Sixteen determined differential metabolites were mainly in four metabolic pathways, including glycerophospholipid, arachidonic acid, sphingolipid metabolisms, pentose and glucuronate interconversions. The results indicated that the pseudotargeted method based on two-phase liquid extraction broadened the metabolite coverage with good sensitivity and repeatability, exhibiting significant potential for discovering differential metabolites in metabolomics studies. This article is protected by copyright. All rights reserved.

Quantitative structure-retention relationship for reliable metabolite identification and quantification in metabolomics using ion-pair reversed-phase chromatography coupled with tandem mass spectrometry

Hydrophilic metabolites are essential for all biological systems with multiple functions and their quantitative analysis forms an important part of metabolomics. However, poor retention of these metabolites on reversed-phase (RP) chromatographic column hinders their effective analysis with RPLC-MS methods. Herein, we developed a method for detecting hydrophilic metabolites using the ion-pair reversed-phase liquid-chromatography coupled with mass spectrometry (IPRP-LC-MS/MS) in scheduled multiple-reaction-monitoring (sMRM) mode. We first developed a hexylamine-based IPRP-UHPLC-QTOFMS method and experimentally measured retention time (t_R) for 183 hydrophilic metabolites. We found that t_Rs of these metabolites were dominated by their electrostatic potential depending upon the numbers and types of their ionizable groups. We then systematically investigated the quantitative structure-retention relationship (QSRR) and constructed QSRR models using the measured t_R. Subsequently, we developed a retention time predictive model using the random-forest regression algorithm (r² = 0.93, q² = 0.70, MAE = 1.28 min) for predicting metabolite retention time, which was applied in IPRP-UHPLC-MS/MS method in sMRM mode for quantitative metabolomic analysis. Our method can simultaneously quantify more than 260 metabolites. Moreover, we found that this method was applicable for multiple major biological matrices including biofluids and tissues. This approach offers an efficient method for large-scale quantitative hydrophilic metabolomic profiling even when metabolite standards are unavailable.

FROM A NON-TARGETED METABOLOMICS APPROACH TO A TARGETED BIOMARKERS STRATEGY TO HIGHLIGHT TESTOSTERONE ABUSE IN EQUINE. ILLUSTRATION OF A METHODOLOGICAL TRANSFER BETWEEN PLATFORMS AND LABORATORIES

In order to overcome the challenge associated with the screening of Anabolic-Androgenic Steroids (AAS) abuses in animal competitions, a non-targeted LC-HRMS based metabolomics approach was implemented on equine urine samples to highlight potential biomarkers associated with the administration of such compounds, using testosterone esters as model steroids. A statistical model relying on 4 potential biomarkers intensity could be defined to predict the status of the samples. With a routine application perspective, the monitoring of the highlighted potential biomarkers was first transferred into high-throughput LC-SRM. The model's performances and robustness of the approach were preserved and providing a first demonstration of metabolomics-based biomarkers integration within a targeted workflow using common benchtop MS instrumentation. In addition, with a view to the widespread implementation of such biomarker-based tools, we have transferred the method to a second laboratory with similar instrumentation. This proof of concept allows the development and application of biomarker-based strategies to meet current doping control needs.

Memory enhancement effect of saponins from Eleutherococcus senticosus leaves and blood-brain barrier-permeated saponins profiling using a pseudotargeted monitoring strategy

Dried Eleutherococcus senticosus leaves (ESL), also known as Siberian ginseng tea, are beneficial for human neural disorders. Our previous studies showed that the aqueous extract of ESL enhanced memory in...

A pseudotargeted method based on sequential window acquisition of all theoretical spectra mass spectrometry acquisition and its application in quality assessment of traditional Chinese medicine preparation-Yuanhu Zhitong tablet

Quality control plays a key role in the application of Chinese materia medica, especially in the preparation of traditional Chinese medicine. A pseudotargeted analysis method using an ultra-high-performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry that was operated in the sequential window acquisition of all theoretical spectra mode was proposed to explore the chemical markers of traditional Chinese medicine preparation. Full-scan-based untargeted analysis was applied to extract the target ions. After data preprocessing, 302 target ions were extracted and used for the subsequent sequential window acquisition of all theoretical spectra analyses. The established sequential window acquisition of all theoretical spectra-based pseudotargeted approaches exhibited good repeatability and a wide linear range. The established method was successfully applied to discover analytical markers for the Yuanhu Zhitong tablet. After multivariate statistical analysis, 94 potential markers were identified. Ten markers were annotated by matching accurate m/z and product ion information obtained from previous reports. It is clearly indicated that the pseudotargeted analysis could make a great contribution to the quality assessment of traditional Chinese medicine preparation as a newly emerging technique.

Identification of Key Modules and Hub Genes Involved in Esophageal Squamous Cell Carcinoma Tumorigenesis Using WCGNA

Introduction:

The mechanistic basis for the development of esophageal squamous cell carcinoma (ESCC) remains poorly understood. The goal of the present study was thus to characterize mRNA and long noncoding RNA (lncRNA) expression profiles associated with ESCC in order to identify key hub genes associated with the pathogenesis of this cancer.

Materials and Methods:

The GSE26866 and GSE45670 datasets from the Gene Expression Omnibus (GEO) database were used to conduct a weighted gene co-expression network analysis (WGCNA), after which Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Cytoscape was additionally used to construct lncRNA-mRNA networks, after which hub genes were identified and validated through the assessment of TCGA datasets and clinical samples.

Results:

Two gene modules were found to be closely linked to ESCC tumorigenesis. These genes were enriched in cell cycle, MAPK signaling, JAK-STAT signaling, pyrimidine metabolism, arachidonic acid metabolism, and P53 signaling pathway activity, all of which are directly linked with the development of cancer. In total, we identified and validated 9 hub genes associated with ESCC (DDX18, DNMT1, NCAPG, WDHD1, PRR11, VOPP1, ZKSCAN5, LC35C2, and PHACTR2).

Conclusion:

In summary, we identified key gene modules and hub genes associated with ESCC development, and we constructed a lncRNA-mRNA network pertaining to this cancer type. These results provide a foundation for future research regarding the mechanistic basis of ESCC.

A Workflow to Perform Targeted Metabolomics at the Untargeted Scale on a Triple Quadrupole Mass Spectrometer

The thousands of features commonly observed when performing untargeted metabolomics with quadrupole time-of-flight (QTOF) and Orbitrap mass spectrometers often correspond to only a few hundred unique metabolites of biological origin, which is in the range of what can be assayed in a single targeted metabolomics experiment by using a triple quadrupole (QqQ) mass spectrometer. A major benefit of performing targeted metabolomics with QqQ mass spectrometry is the affordability of the instruments relative to high-resolution QTOF and Orbitrap platforms. Optimizing targeted methods to profile hundreds of metabolites on a QqQ mass spectrometer, however, has historically been limited by the availability of authentic standards, particularly for "unknowns" that have yet to be structurally identified. Here, we report a strategy to develop multiple reaction monitoring (MRM) methods for QqQ instruments on the basis of high-resolution spectra, thereby enabling us to use data from untargeted metabolomics to design targeted experiments without the need for authentic standards. We demonstrate that using high-resolution fragmentation data alone to design MRM methods results in the same quantitative performance as when methods are optimized by measuring authentic standards on QqQ instruments, as is conventionally done. The approach was validated by showing that Orbitrap ID-X data can be used to establish MRM methods on a Thermo TSQ Altis and two Agilent QqQs for hundreds of metabolites, including unknowns, without a dependence on standards. Finally, we highlight an application where metabolite profiling was performed on an ID-X and a QqQ by using the strategy introduced here, with both data sets yielding the same result. The described approach therefore allows us to use QqQ instruments, which are often associated with targeted metabolomics, to profile knowns and unknowns at a comprehensive scale that is typical of untargeted metabolomics.

Metabolomics of Oral/Head and Neck Cancer

Oral/head and neck cancer is the sixth most common human malignancies in the world. Despite the treatment advances in surgery, chemotherapy, and radiotherapy, the patient survival has not been significantly improved in the past several decades. As a new methodological approach, metabolomics may help reveal the metabolic reprogramming mechanisms underlying head and neck cancer cell proliferation, invasion, and metastasis and may be used to identify metabolite biomarkers for clinical applications of the disease. In this chapter, we briefly review recent metabolomic applications in head and neck cancer.

Research status and prospects of biomarkers for nasopharyngeal carcinoma in the era of high‑throughput omics (Review)

As a malignant tumor type, nasopharyngeal carcinoma (NPC) is characterized by distinct geographical, ethnic and genetic differences; presenting a major threat to human health in many countries, especially in Southern China. At present, no accurate and effective methods are available for the early diagnosis, efficacious evaluation or prognosis prediction for NPC. As such, a large number of patients have locoregionally advanced NPC at the time of initial diagnosis. Many patients show toxic reactions to overtreatment and have risks of cancer recurrence and distant metastasis owing to insufficient treatment. To solve these clinical problems, high‑throughput '‑omics' technologies are being used to screen and identify specific molecular biomarkers for NPC. Because of the lack of comprehensive descriptions regarding NPC biomarkers, the present study summarized the research progress that has been made in recent years to discover NPC biomarkers, highlighting the existing problems that require exploration. In view of the lack of authoritative reports at present, study design factors that affect the screening of biomarkers are also discussed here and prospects for future research are proposed to provide references for follow‑up studies of NPC biomarkers.

Bridging Targeted and Untargeted Mass Spectrometry-Based Metabolomics via Hybrid Approaches

This mini-review aims to discuss the development and applications of mass spectrometry (MS)-based hybrid approaches in metabolomics. Several recently developed hybrid approaches are introduced. Then, the overall workflow, frequently used instruments, data handling strategies, and applications are compared and their pros and cons are summarized. Overall, the improved repeatability and quantitative capability in large-scale MS-based metabolomics studies are demonstrated, in comparison to either targeted or untargeted metabolomics approaches alone. In summary, we expect this review to serve as a first attempt to highlight the development and applications of emerging hybrid approaches in metabolomics, and we believe that hybrid metabolomics approaches could have great potential in many future studies.

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.

Analytical techniques for metabolomic studies: a review

Metabolomics is the comprehensive study of small-molecule metabolites. Obtaining a wide coverage of the metabolome is challenging because of the broad range of physicochemical properties of the small molecules. To study the compounds of interest spectroscopic (NMR), spectrometric (MS) and separation techniques (LC, GC, supercritical fluid chromatography, CE) are used. The choice for a given technique is influenced by the sample matrix, the concentration and properties of the metabolites, and the amount of sample. This review discusses the most commonly used analytical techniques for metabolomic studies, including their advantages, drawbacks and some applications.

Untargeted Metabolomics Analysis of Esophageal Squamous Cell Carcinoma Discovers Dysregulated Metabolic Pathways and Potential Diagnostic Biomarkers

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most fatal diseases worldwide. Because early diagnosis is difficult, ESCC is mostly diagnosed at an advanced stage, leading to a poor overall prognosis. The purpose of this study was to explore the differences between plasma metabolic profiles in ESCC patients and healthy controls and to establish a diagnostic model of ESCC.

Methods: In this study, a cohort of 310 subjects, containing 140 ESCC patients and 170 healthy controls (HC), was recruited. Participants were randomly separated into a training set (80 ESCCs, 80 HCs) and a validation set (60 ESCCs, 90 HCs) and their plasma metabolomics profiles were analyzed by ultra-performance liquid chromatography-tandem quadruple time-of-flight mass spectrometry (UPLC-QTOF/MS) technique. Univariate statistical analysis and multivariate analysis (MVA) methods were used to identify differential metabolites. Finally, the dysregulated pathways associated with ESCC were further explored and the diagnostic performance of the biomarker panel was evaluated.

Results: Metabolic analyses identified 34 significant metabolites involved in the metabolism of amino acids, phospholipids, fatty acids, purine, and choline. Farthermore, an effective diagnostic model for ESCC was constructed based on eight metabolites. This panel of biomarkers consisted of hypoxanthine, proline betaine, indoleacrylic acid, inosine, 9-decenoylcarnitine, tetracosahexaenoic acid, LPE (20:4), and LPC (20:5). The model was verified and evaluated in the validation set. The AUC value of the ROC curve was 0.991(95% CI: 0.981-1.000, CI, Confidence interval), with a sensitivity (SE) of 98.8% and a specificity (SP) of 94.9% for the training set and 0.965(95% CI: 0.936-0.993), with a SE of 88.3% and a SP of 88.9% for the validation set. Among them, three biomarkers, indoleacrylic acid, LPC (20:5), and LPE (20:4), exhibited a trend associated with the ESCC progression.

Conclusions: Our study identified a novel plasma biomarker panel, which clearly distinguishes ESCC patients and provides insight into the mechanisms of ESCC. This finding may form the basis for the development of a minimally invasive method for ESCC detection.

Strategy for Global Profiling and Identification of 2- and 3-Hydroxy Fatty Acids in Plasma by UPLC-MS/MS

2-Hydroxy fatty acids (2-OHFAs) and 3-hydroxy fatty acids (3-OHFAs) with the same carbon backbone are isomers, both of which are closely related to diseases involving fatty acid oxidation disorder. However, the comprehensive profiling of 2- and 3-OHFAs remains an ongoing challenge due to their high structure similarity, few structure-informative product ions, and limited availability of standards. Here, we developed a new strategy to profile and identify 2- and 3-OHFAs according to structure-dependent retention time prediction models using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Both accurate MS and MS/MS spectra were collected for peak annotation by comparison with an in-house database of theoretically possible 2- and 3-OHFAs. The structures were further confirmed by the validated structure-dependent retention time prediction models, taking advantage of the correlation between the retention time, carbon chain length and number of double bonds, as well as the hydroxyl position-induced isomeric retention time shift rule. With the use of this strategy, 18 2-OHFAs and 32 3-OHFAs were identified in the pooled plasma, of which 7 2-OHFAs and 20 3-OHFAs were identified for the first time in this work, furthering our understanding of OHFA metabolism. Subsequent quantitation method was developed by scheduled multiple reaction monitoring (MRM) and then applied to investigate the alteration of 2- and 3-OHFAs in esophageal squamous cell carcinoma (ESCC) patients. Finally, a potential biomarker panel consisting of six OHFAs with good diagnostic performance was achieved. Our study provides a new strategy for isomer identification and analysis, showing great potential for targeted metabolomics in clinical biomarker discovery.

Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology

Screening diagnostic biomarkers can be challenging due to the complexity of traditional Chinese medicine (TCM) and ambiguous pharmacological mechanisms. In this study, we reported an integrated strategy for accurately screening diagnostic biomarkers based on metabolomics coupled with network pharmacology. First, a feasible pharmacological model was established through systems pharmacology and based on metabolomics-based techniques to explore diagnostic biomarkers. While the components satisfying the q-value < 0.05, fold change (FC) ≥ 1.2 or FC ≤ 0.8, coefficient of variance (CV) ≤ 30%(QC) and the variable importance in the project (VIP) value > 1 are considered to be diagnostic biomarkers. Second, the ingredients were retained only when oral bioavailability (OB), Caco-2 permeability, drug half-life, TPSA and drug likeness (DL) satisfied the criteria (OB ≥ 40%; Caco-2 ≥ -0.4; HL ≥ 4 h; TPSA˂140; DL ≥ 0.18) suggested by the TCMSP database. Moreover, ingredients that exhibit extensive biological activity in TCM are also retained. Third, the effect targets of TCM were screened using the TCMSP database, Swiss Target Prediction and STICH online software. Disease targets were gathered from the therapeutic target database (TTD), PharmGkb and TCMSP database. Hub genes were screened by potential protein-protein interaction (PPI) network pharmacology analysis. Finally, a metabolic network pathway is established between the diagnostic biomarker and the hub gene. In the network analysis of metabolic pathways, most of the genes involved in this pathway are the second-step-obtained hub genes, which can explain the accuracy of the identified biomarkers. The proposed integrated strategy was successfully applied to explore the mechanism of action of Pulsatilla decoction (PD) in the treatment of acute ulcerative colitis (UC). Based on this integrated strategy, 23 potential biomarkers of acute UC treated with PD were identified. In conclusion, the integrated strategy provides novel insights into network pharmacology and metabolomics as effective tools to illuminate the mechanism of action of TCM.

Resolvin E1 in Follicular Fluid Acts as a Potential Biomarker and Improves Oocyte Developmental Competence by Optimizing Cumulus Cells

Metabolic profile of follicular fluid (FF) has been investigated to look for biomarkers for oocyte quality. Resolvin E1 (RvE1), a potent pro-resolving mediator, was reported to have protective action in cell function. The study aimed to examine the predictive value of RvE1 for oocyte quality and to explore the cellular mechanism of RvE1 in improving oocyte competence. Metabolic profiles of 80 FF samples showed a higher level of RvE1 in group A (blastocysts scored ≥ B3BC and B3CB according to Gardner's blastocyst scoring system, N = 36) than that of group B (blastocysts scored < B3BC and B3CB, N = 44, P = 0.0018). The receiver operating characteristic (ROC) curve analysis showed that RvE1 level in FF below 8.96 pg/ml (AUC:0.75; 95%CI: 0.64-0.86; P = 0.00012) could predict poor oocyte quality with specificity of 97.22%, suggesting RvE1 as a potential biomarker to exclude inferior oocytes. Besides, the level of RvE1 was found to be significantly lower in FF than in serum (57.49 to 17.62 pg/ml; P=.0037) and was gradually accumulated in the culture medium of cumulus cells (CCs) during cell culture, which indicated that RvE1 came from both blood exudates and local secretion. The in vitro experiment revealed thecellular mechanism of RvE1 in improvingoocyte qualityby decreasing the cumulus cellapoptotic rate and increasing cell viability and proliferation. It is the first time thatthe role of RvE1 in reproduction is explored. In conclusion, RvE1 is valuable as a potential exclusive biomarker for oocyte selection andplays a role in improving oocyte quality.

A strategy for quality evaluation of salt-treated Apocyni Veneti Folium and discovery of efficacy-associated markers by fingerprint-activity relationship modeling

In this study, a fingerprint-activity relationship between chemical fingerprints and hepatoprotective activity was established to evaluate the quality of salt-treated Apocyni Veneti Folium (AVF). Characteristic fingerprints of AVF samples exposed to different concentrations of salt were generated by ultrafast liquid chromatography tandem triple time-of-flight mass/mass spectrometry (UFLC-Triple TOF-MS/MS), and a similarity analysis was performed based on common characteristic peaks by hierarchical clustering analysis (HCA). Then, the hepatoprotective activity of AVF against CCl₄-induced acute liver damage in mice was investigated by assessing biochemical markers and histopathology, which showed that a high dose of AVF exposed to low levels of salt stress produced a marked amelioration of hepatic damage compared with the other salt-treated AVF. Finally, fingerprint-activity relationship modeling, which was capable of discovering the bioactive markers used in the quality evaluation, was investigated by the chemical fingerprints and the hepatoprotective activities utilizing multivariate statistical analysis, gray correlation analysis (GCA) and bivariate correlation analysis (BCA). The results showed that the accumulation of polyphenols, such as flavonoids and phenolic acids, in AVF subjected to low levels of salt stress could result in the effective scavenging of free radicals. Therefore, the present study may provide a powerful strategy to holistically evaluate the quality of salt-treated AVF in combination with chemical fingerprint and bioactivity evaluation.