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Large-scale and long-period metabolomics study is more susceptible to various sources of systematic errors, resulting in nonreproducibility and poor data quality. A reliable and robust batch correction method removes unwanted systematic variations and improves the statistical power of metabolomics data, which undeniably becomes an important issue for the quality control of metabolomics. This study proposed a novel data normalization and integration method, Norm ISWSVR. It is a two-step approach via combining the best-performance internal standard correction with support vector regression normalization, comprehensively removing the systematic and random errors and matrix effects. This method was investigated in three untargeted lipidomics or metabolomics datasets, and the performance was further evaluated systematically in comparison with that of 11 other normalization methods. As a result, Norm ISWSVR decreased the data's median cross-validated relative standard deviation (cvRSD), increased the correlation between QCs, improved the classification accuracy of biomarkers, and was well-compatible with quantitative data. More importantly, Norm ISWSVR also allows a low frequency of QCs, which could significantly decrease the burden of a large-scale experiment. Correspondingly, Norm ISWSVR favorably improves the data quality of large-scale metabolomics data.

Metabolic networks and their dysfunction in the brain are closely associated with central nervous function and many psychogenic diseases. Thus, it is of utmost importance to develop a high-throughput imaging method for metabolic network mapping. Here, we developed a metabolic network mapping method to discover the metabolic contexts and alterations with spatially resolved information from the microregion of the brain by ambient-air flow-assisted desorption electrospray ionization mass spectrometry imaging and metabolomics analysis, which can be performed without any chemical derivatization, labels, or complex sample pretreatment. This method can map hundreds of different polar functional metabolites involved in multiple metabolic pathways, including not only neurotransmitters but also purines, organic acids, polyamines, cholines, and carbohydrates, in the rat brain. These high-coverage metabolite profile and microregional distribution information constitute complex networks that regulate advanced functions in the central nervous system. Moreover, this methodology was further used to discover not only the dysregulated metabolites but also the brain microregions involved in the pathology of a scopolamine-treated Alzheimer's model. Furthermore, this methodology was demonstrated to be a powerful visualizing tool that could offer novel insight into the metabolic events and provide spatial information about these events in central nervous system diseases.

The development of quantitative metabolomics approaches for future standardized and translational applications has become increasingly important. Data-independent targeted quantitative metabolomics (DITQM) is a newly proposed method providing ion pair information on 1324 metabolites. However, the quantification of more than 1000 metabolites in large sample sizes has still not been implemented. In this study, on the basis of the DITQM concept, scheduled multiple reaction monitoring (MRM) methods for both high-abundant and low-abundant metabolites were established to broaden the quantification coverage, and an open-source program "Quanter_1.0" was coded to facilitate efficient data handling. Our results demonstrated that 1015 metabolites in human plasma met the quantitative requirements and could be relatively determined in an effective manner. The method was then applied to a large-scale sample study of lung cancer consisting of three distinct analytical batches. It was obvious that data quality that originated from quantitative metabolomics was improved, with substantially lower intra- and inter-batch data variation, resulting in a more effective multivariate statistical model. Finally, 26 potential biomarkers of lung cancer were discovered. Collectively, our approach provides a promising tool for quantitative metabolomics research involving large-scale sample sizes and clinical application., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Investigation of the in vivo drug action and metabolic differences of epimer drugs is challenging. Whole-body MSI analysis can visually present the stereoscopic distribution of molecules related to the interaction of drugs and organisms, and can provide more comprehensive organ-specific profiling information. Herein, we developed a whole-body spatially-resolved imaging metabolomics method based on an air flow-assisted ionisation desorption electrospray ionisation (AFADESI)-MSI system coupled with a high-resolution mass spectrometer and highly discriminating imaging software. The epimeric sedative-hypnotic drug candidates YZG-331 and YZG-330 were selected as examples, and rats administered normal or high oral doses were used. By performing multivariate statistical data-mining on the combined MSI data, organ-specific differential ions were screened. By comparing the variations in the relative contents of the drugs, their metabolites, and endogenous neurotransmitters throughout whole-body tissue sections of the rats, rich information that could potentially explain the more significant sedative-hypnotic effects of YZG-330 compared to YZG-331 was obtained. Such as the increased ratio of gamma-aminobutyric acid in the brain and stomach of the rats (0.25, 0.47, 0.68, 0.30, and 0.89 for the control and YZG-331-H, YZG-330-H, YZG-331-L, and YZG-330-L, respectively) were interesting. This study provided a convenient and visual method to investigate in vivo molecular metabolic differences and provide insight towards a better understanding of the pharmacodynamic mechanisms of these sedative-hypnotic drug-candidates., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Methylation of components involved in one-carbon metabolism is extremely important in cancer; comprehensive studies on methylation are essential and may provide us with a better understanding of tumorigenesis, and lead to the discovery of potential biomarkers. Here, we present an improved methodology for methylated metabolite profiling and its relative quantification in breast cancer cell lines by isotope dilution mass spectrometry based on 13 CD 3 -methionine metabolic labeling using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS). First, all the methylated metabolites related to methionine were first screened and profiled by introducing 13 CD 3 -methionine as the only medium into breast cancer cell growth cultures for both cellular polar metabolites and lipids. In total, we successfully found 20 labeled methylated metabolites and most of them were identified, some of which have not been reported before. We also developed a relative quantification method for all identified methylated metabolites based on isotope dilution mass spectrometry assays. Finally, the developed method was used for different breast cancer cells and mammary epithelial cells. Most methylated metabolites were disrupted in cancer cells. 1-Methyl-nicotinamide was decreased significantly, while trimethylglycine-glutamic acid-lysine and trimethyl-lysine were increased more than five times. This method offers a new insight into the methylation process, with several key pathways and important new metabolites being identified. Further investigation with biological assays should help to reveal the overall methylation metabolic network.

The pseudo-targeted metabolomics approach was developed recently which combined the advantages of untargeted and targeted analysis. However, the current pseudo-targeted analysis method has limitations due to the technical characteristics. In this study, a novel metabolic pathway-based pseudo-targeted approach was proposed for urine metabolomics analysis using an ultra-high-performance liquid chromatography (UPLC)-MS/MS system operated in the multiple reaction monitoring (MRM) mode. MRM ion pairs were acquired from urine samples through untargeted analysis using UPLC-HRMS, as well as by searching for metabolites in related pathways in relevant databases and from previous relevant research, including amino acids, fatty acids, nucleosides, carnitines, glycolysis metabolites, and steroids. This improved pseudo-targeted method exhibited good repeatability and precision, and no complicated peak alignment was required. As a proof of concept, the developed novel method was applied to the discovery of urine biomarkers for patients with esophageal squamous cell carcinoma (ESCC). The results showed that ESCC patients had altered acylcarnitines, amino acids, nucleosides, and steroid derivative levels et al. compared to those of healthy controls. The novelty of this study lies in the fact that it provides an approach for acquiring MRM ion pairs not only from untargeted MS analysis but also from targeted searching for metabolites in related metabolic pathways. By improving the detection limit of low-abundance metabolites, it enlarges the range for the discovery of potential biomarkers. Our work provides a foundation for achieving pseudo-targeted metabolomics analysis on the widely used LC-MS/MS MRM platform., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Characterization of tumor metabolism with spatial information contributes to our understanding of complex cancer metabolic reprogramming, facilitating the discovery of potential metabolic vulnerabilities that might be targeted for tumor therapy. However, given the metabolic variability and flexibility of tumors, it is still challenging to characterize global metabolic alterations in heterogeneous cancer. Here, we propose a spatially resolved metabolomics approach to discover tumor-associated metabolites and metabolic enzymes directly in their native state. A variety of metabolites localized in different metabolic pathways were mapped by airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) in tissues from 256 esophageal cancer patients. In combination with in situ metabolomics analysis, this method provided clues into tumor-associated metabolic pathways, including proline biosynthesis, glutamine metabolism, uridine metabolism, histidine metabolism, fatty acid biosynthesis, and polyamine biosynthesis. Six abnormally expressed metabolic enzymes that are closely associated with the altered metabolic pathways were further discovered in esophageal squamous cell carcinoma (ESCC). Notably, pyrroline-5-carboxylate reductase 2 (PYCR2) and uridine phosphorylase 1 (UPase1) were found to be altered in ESCC. The spatially resolved metabolomics reveal what occurs in cancer at the molecular level, from metabolites to enzymes, and thus provide insights into the understanding of cancer metabolic reprogramming., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

The discovery and identification of reliable disease biomarkers and relevant disrupted metabolic pathways is still a major challenge in metabolomics. Here, we proposed a biotransformation-based metabolomics profiling method to identify reliable disease biomarkers by simultaneous quantitation and qualification of cancer-related metabolites and their metabolic pathways via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The approach was based on selecting a subset of known cancer-related metabolites from our previous metabolomics work, cancer research literature and biological significance. The metabolic profiling of pathway-related metabolites was developed by predicted multiple reaction monitoring (MRM) of ion pairs based on their chemical structures and biotransformation. Then, a high-throughput quantitative method was established. Overall, this approach enables the sensitive and accurate detection of cancer-related metabolites and the identification of other relevant metabolites, which facilitates better data quality and in-depth investigation of dysregulated metabolic pathways. As a proof of concept, the approach was applied to a small-cell lung cancer (SCLC) study. The results showed that 43 metabolites were significantly changed, and arginine metabolism was apparently disturbed, which proved the proposed approach could be a powerful tool for discovering reliable disease biomarkers and aberrant metabolic pathways., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Targeted quantification and untargeted global profiling are the two mainstream approaches, a merging of which could provide enhanced analytical potential in metabolomics research. Here, a simultaneous targeted quantification and untargeted metabolomics (STQUM) strategy was developed for more efficient, accurate and comprehensive metabolomics research by using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS). First, we selected 110 cancer-related metabolites as targets and established a dual LC sequential separation method for simultaneous analysis of strong and weak polar metabolites. In order to achieve efficient acquisition for synchronous qualitative and quantitative analysis, high-resolution, data-dependent parallel reaction monitoring (PRM) method and data-independent all ion fragmentation (AIF) method were established. Their performance in targeted confirmation and quantification, and untargeted analysis were systematically investigated and assessed. In total, 78 metabolites were confidently confirmed in positive ion mode in both PRM and AIF assays, in which 73 metabolites can be accurately quantified. In addition, simultaneously untargeted profiling of 4651 features of high reliability and validity were achieved. Both AIF and PRM methods revealed high confidence, sensitivity and accuracy. In the STQUM approach, another 15 metabolites could be accurately quantified in negative ion mode. The method offers a new perspective for merging the hypothesis-based targeted quantitative validation and untargeted biomarkers discovery in one run for improved analysis efficiency and integrity., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Quantitative metabolomics approaches can significantly improve the repeatability and reliability of metabolomics investigations but face critical technical challenges, owing to the vast number of unknown endogenous metabolites and the lack of authentic standards. The present study contributes to the development of a novel method known as "data-independent targeted quantitative metabolomics" (DITQM), which was used to investigate the label-free quantitative metabolomics of multiple known and unknown metabolites in biofluid samples. This approach initially involved the acquisition of MS/MS data for all metabolites in biosamples using a sequentially stepped targeted MS/MS (sst-MS/MS) method, in which multiple product ion scans were performed by selecting all ions in the targeted mass ranges as the precursor ions. Subsequently, scheduled multiple reaction monitoring (MRM) by LC-MS/MS of the metabolome was established for 1658 characteristic ion pairs of 1324 metabolites. For sensitive and accurate quantification of these metabolites, mixed calibration curves were generated using sequentially diluted standard reference plasma samples using established MRM methods. Relative concentrations of all metabolites in each sample were calculated without using individual authentic standards. To evaluate the reliability and applicability of this new method, the performance of DITQM was validated by comparison to absolute quantification of 12 acylcarnitines using authentic standards and traditional metabolomics analysis for lung cancer. The results proved that the DITQM protocol is more reliable and can significantly improve clustering effects and repeatability in biomarker discovery. In this study, we established a novel methodology to standardize and quantify large-scale metabolome, providing a new choice for metabolomics research and its clinical applications.

Aim: In metabolomics research, the use of different blood collection methods may influence endogenous metabolites., Materials & Methods: Ultra HPLC coupled with MS/MS was applied together with multivariate statistics to investigate metabolomics differences in serum and plasma samples handled by different anticoagulants. A total of 135 known representative metabolites were assessed for comprehensive evaluation of the effects of anticoagulants., Results: Exogenous factors, including separation gel ingredients from the serum collection tubes and the anticoagulants, affected mass spectrometer detection. Heparin plasma yielded the best detection of different functional groups and is therefore the optimal blood specimen for metabolomics research, followed by potassium oxalate plasma.

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

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

Sample preparation is a critical step in tissue metabolomics. Therefore, a comprehensive and systematic strategy for the screening of tissue preparation protocols is highly desirable. In this study, we developed an Optimization and Evaluation Strategy based on LC-MS to screen for a high-extractive efficiency and reproducible esophageal tissue preparation protocol for different types of endogenous metabolites (amino acids, carnitines, cholines, etc.), with a special focus on low-level metabolites. In this strategy, we first selected a large number of target metabolites based on literature survey, previous work in our lab, and known metabolic pathways. For these target metabolites, we tested different solvent extraction methods (biphasic solvent extraction, two-step [TS], stepwise [SW], all-in one [AO]; single-phase solvent extraction, SP) and esophageal tissue disruption methods (homogenized wet tissue [HW], ground wet tissue [GW], and ground dry tissue [GD]). A protocol involving stepwise addition of solvents and a homogenized wet tissue protocol (SWHW) was superior to the others. Finally, we evaluated the stability of endogenous metabolites in esophageal tissues and the sensitivity, reproducibility, and recovery of the optimal protocol. The results proved that the SWHW protocol was robust and adequate for bioanalysis. This strategy will provide important guidance for the standardized and scientific investigation of tissue metabolomics.

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

Rationale: Acquiring global information on plasma-endogenous metabolites challenges metabolomics. This study has been designed to investigate the suitability of integrated ionization rapid-resolution liquid chromatography/tandem mass spectrometry (RRLC/MS/MS) for different kinds of metabolites in complex plasma, and provides an approach for plasma metabolomics in acquiring more comprehensive data of metabolites., Methods: Integrated ionization of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combined with RRLC/MS/MS has been carried out to perform analysis on the global plasma metabolome of healthy volunteers. The contributions to the total numbers of ion features by RRLC/MS with ESI, APCI, and APPI in positive and negative ion modes were calculated. Representative unique and identical ions were identified. The intensities of identical ions were compared., Results: Each of ESI, APCI, and APPI coupled with RRLC/MS has its own advantage over the other two techniques for certain types of metabolites in plasma. LC/ESI-MS is very sensitive for detecting glycerophosphocholines, glycerophosphoethanolamines, acyl carnitines, bile acids, sulfate, etc. LC/APCI-MS is suitable for analyzing cyclic alcohols, fatty acids, and linoleic acids. LC/APPI-MS proves to be appropriate in detecting steroids, sphingolipids, some amino acids, nucleosides, and purines in plasma., Conclusions: It is suggested that the integrated ionization LC/MS approach should be applied for global plasma metabolomics. Moreover, the results obtained demonstrate that it is preferable to choose certain techniques from LC/ESI-MS, LC/APCI-MS, and LC/APPI-MS for metabolite target analysis., (Copyright © 2013 John Wiley & Sons, Ltd.)

It is essential to choose one preprocessing method for liquid chromatography-mass spectrometry (LC-MS)-based metabolomics studies of urine samples in order to overcome their variability. However, the commonly used normalization methods do not substantially reduce the high variabilities arising from differences in urine concentration, especially for signal saturation (abundant metabolites exceed the dynamic range of the instrumentation) or missing values. Herein, a simple preacquisition strategy based on differential injection volumes calibrated by creatinine (to reduce the concentration differences between the samples), combined with normalization to "total useful MS signals" or "all MS signals", is proposed to overcome urine variabilities. This strategy was first systematically compared with other popular normalization methods by application to serially diluted urine samples. Then, the method has been verified using rat urine samples of pre- and postinoculation of Walker 256 carcinoma cells. The results showed that the calibration of injection volumes based on creatinine values could effectively eliminate intragroup differences caused by variations in the concentrations of urinary metabolites, thus giving better parallelism and clustering effects. In addition, peak area normalization could further eliminate intraclass differences. Therefore, the strategy of combining peak area normalization with calibration of injection volumes of urine samples based on their creatinine values is effective for solving problems associated with urinary metabolomics.

A metabolomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS) and multivariate statistics has been implemented to identify potential biomarkers in uterine cervix cancer. Due to the importance of the data pre-processing method, three popular software packages have been compared. Then they have been used to acquire respective data matrices from the same LC-MS/MS data. Multivariate statistics was subsequently used to identify significantly changed biomarkers for uterine cervix cancer from the resulting data matrices. The reliabilities of the identified discriminated metabolites have been further validated on the basis of manually extracted data and ROC curves. Nine potential biomarkers have been identified as having a close relationship with uterine cervix cancer. Considering these in combination as a biomarker group, the AUC amounted to 0.997, with a sensitivity of 92.9% and a specificity of 95.6%. The prediction accuracy was 96.6%. Among these potential biomarkers, the amounts of four purine derivatives were greatly decreased, which might be related to a P2 receptor that might lead to a decrease in cell number through apoptosis. Moreover, only two of them were identified simultaneously by all of the pre-processing tools. The results have demonstrated that the data pre-processing method could seriously bias the metabolomics results. Therefore, application of two or more data pre-processing methods would reveal a more comprehensive set of potential biomarkers in non-targeted metabolomics, before a further validation with LC-MS/MS based targeted metabolomics in MRM mode could be conducted.

Metabonomics is an important platform for investigating the metabolites of integrated living systems and their dynamic responses to changes caused by both endogenous and exogenous factors. A metabonomics strategy based on liquid chromatography-mass spectrometry/mass spectrometry in both positive and negative ion modes was applied to investigate the short-term and long-term stability of metabolites in plasma. Principal components analysis and ten types of identified metabolites were used to summarize the time-dependent change rules in metabolites systematically at different temperatures. The long-term stability of metabolites in plasma specimens stored at -80 °C for five years was also studied. Analysis of these subjects identified 36 metabolites with statistically significant changes in expression (p < 0.05) and found a kind of metabolite with a hundred-fold change. The stability of metabolites in blood at 4 °C for 24 h was also investigated. These studies show that a thorough understanding of the effects of metabolite stability are necessary for improving the reliability of potential biomarkers.

A metabonomic approach based on complementary hydrophilic interaction chromatography and reversed-phase liquid chromatography combined with tandem mass spectrometry and time-course analysis of metabolites was implemented to find more reliable potential biomarkers in urine of Walker 256 tumor-bearing rats. A major challenge in metabonomics is distinguishing reliable biomarkers that are closely associated with the genesis and progression of diseases from those that are unrelated but altered significantly. In this study, these biomarkers were selected according to the change trends of discriminating metabolites during the genesis and progression of cancer. Seven consecutive batches of urine samples from preinoculation to 16 days after were collected and analyzed. Multivariate analysis revealed 87 discriminating metabolites. Time-course analysis of discriminating metabolites was used to select more reliable biomarkers with regular and reasonable change trends. Finally, 47 were found and 15 were identified including 12 carnitine derivatives, 2 amino acid derivatives, 1 nucleoside. On the basis of time-course behaviors of these potential biomarkers, we hypothesize such disruption might result from elevated cell proliferation, reduced β-oxidation of fatty acids, and poor renal tubular reabsorption. These studies demonstrate that this method can help to find more reliable potential biomarkers and provide valuable biochemical insights into metabolic alterations in tumor-bearing biosystems.

An integrated ionization approach of electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI) combining with rapid resolution liquid chromatography mass spectrometry (RRLC-MS) has been developed for performing global metabonomic analysis on complex biological samples. This approach was designed to overcome the low ionization efficiencies of endogenous metabolites due to diverse physicochemical properties as well as ion suppression, and obtain comprehensive metabolite profiles in LC-MS analysis. Ionization capability and applicability were manifested by improved ionization efficiency and enlarged metabolite coverage in analysis on typical urinary metabolite standards and urine samples from healthy volunteers. The method was validated by the limit of detection and precision. When applied to the global metabonomic studies of lung cancer, more comprehensive biomarker candidates were obtained to reflect metabolic traits between healthy volunteers and lung cancer patients, including 74 potential biomarkers in positive ion mode and 59 in negative ion mode. Taking identical potential biomarkers of any two or three ionization methods into account, analysis using ESI-MS in positive (+) and negative (-) ion mode contributed to 70 and 64% of the total potential biomarkers, respectively. The biomarker discovery capability of (+/-) APCI-MS accounted for 45 and 42% of the overall; meanwhile (+/-) APPI-MS amounted for 39 and 54%. These results indicated that potential biomarkers with vital biological information could be missed if only a single ionization method was used. Furthermore, 11 potential biomarkers were identified including amino acids, nucleosides, and a metabolite of indole. They revealed elevated amino acid and nucleoside metabolism as well as protein degradation in lung cancer patients. This proposed approach provided a more comprehensive picture of the metabolic changes and further verified identical biomarkers that were obtained simultaneously using different ionization methods.

A metabonomics strategy based on rapid resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS), multivariate statistics and metabolic correlation networks has been implemented to find biologically significant metabolite biomarkers in breast cancer. RRLC-MS/MS analysis by electrospray ionization (ESI) in both positive and negative ion modes was employed to investigate human urine samples. The resulting data matrices were analyzed using multivariate analysis. Application of orthogonal projections to latent structures discriminate analysis (OPLS-DA) allowed us to extract several discriminated metabolites reflecting metabolic characteristics between healthy volunteers and breast cancer patients. Correlation network analysis between these metabolites has been further applied to select more reliable biomarkers. Finally, high resolution MS and MS/MS analyses were performed for the identification of the metabolites of interest. We identified 12 metabolites as potential biomarkers including amino acids, organic acids, and nucleosides. They revealed elevated tryptophan and nucleoside metabolism as well as protein degradation in breast cancer patients. These studies demonstrate the advantages of integrating metabolic correlation networks with metabonomics for finding significant potential biomarkers: this strategy not only helps identify potential biomarkers, it also further confirms these biomarkers and can even provide biochemical insights into changes in breast cancer.

Detailed knowledge on tissue-specific metabolic reprogramming in diabetic nephropathy (DN) is vital for more accurate understanding the molecular pathological signature and developing novel therapeutic strategies. In the present study, a spatial-resolved metabolomics approach based on air flow-assisted desorption electrospray ionization (AFADESI) and matrix-assisted laser desorption ionization (MALDI) integrated mass spectrometry imaging (MSI) was proposed to investigate tissue-specific metabolic alterations in the kidneys of high-fat diet-fed and streptozotocin (STZ)-treated DN rats and the therapeutic effect of astragaloside IV, a potential anti-diabetic drug, against DN. As a result, a wide range of functional metabolites including sugars, amino acids, nucleotides and their derivatives, fatty acids, phospholipids, sphingolipids, glycerides, carnitine and its derivatives, vitamins, peptides, and metal ions associated with DN were identified and their unique distribution patterns in the rat kidney were visualized with high chemical specificity and high spatial resolution. These region-specific metabolic disturbances were ameliorated by repeated oral administration of astragaloside IV (100 mg/kg) for 12 weeks. This study provided more comprehensive and detailed information about the tissue-specific metabolic reprogramming and molecular pathological signature in the kidney of diabetic rats. These findings highlighted the promising potential of AFADESI and MALDI integrated MSI based metabolomics approach for application in metabolic kidney diseases. The present study revealed region-specific metabolic reprogramming during diabetic nephropathy (DN) by using air flow-assisted desorption electrospray ionization (AFADESI) and matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI). [Display omitted] [ABSTRACT FROM AUTHOR]

Ginkgo biloba L. is a rare tree species unique to China. Ginkgo biloba is a traditional Chinese medicinal with a long history, acting on the heart and lung meridians, and has been reported to have a significant effect on non-small cell lung cancer. However, the mechanism underlying this metabolic effect is poorly understood. To identify the active components of Ginkgo biloba extract that may have effects on non-small cell lung cancer and their mechanisms of metabolic regulation. In this study, LC-MS/MS was used to investigate the chemical constituents of Ginkgo biloba extract. Network pharmacology was used to identify the active components potentially valuable in the treatment of non-small cell lung cancer. Antitumor activity was evaluated using CCK-8 and apoptosis assays. The mechanisms of metabolic regulation by the active components were further explored using untargeted metabolomics, targeted metabolomics, and western blot experiments. Network pharmacology and component analysis of Ginkgo biloba extract identified four ginkgolides that significantly affect non-small cell lung cancer. Their antiproliferative activity in A549 cells was evaluated using CCK-8 and apoptosis assays. The metabolomics results indicated that the ginkgolides had a significant regulatory effect on metabolic pathways related to one-carbon metabolisms, such as purine metabolism, glutathione metabolism, and the methionine cycle. Further targeted metabolomics analysis on one-carbon metabolism found that the ginkgolides may significantly affect the content of multiple metabolites in A549 cells, including purine, S-adenyl methionine, S-adenylyl homocysteine, and glutathione upregulated, and adenosine, tetrahydrofolate, and 10-Formyl-tetrahydrofolate significantly decreased. Notably, dihydrofolate reductase (DHFR) and methylenetetrahydrofolate dehydrogenases (MTHFR) were found to be altered after the treatment of ginkgolides. This in vitro study indicated that ginkgolides might inhibit the growth of A549 cells by targeting one-carbon metabolism. This study also demonstrated that metabolomics combined with network pharmacology is a powerful tool for identifying traditional Chinese medicines' active components and metabolic mechanisms. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • Metabolomics-transcriptomics integration strategy was established for comparison study. • Flower, stem, and leaf sample of Artemisia rupestris L. were analyzed separately. • Significant molecular differences between cultivated and wild plants were discovered. • Thirty-four chemical markers were screened out including 19 potential new compounds. • qRT-PCR confirmed twenty-four chemical makers were closely related to six genes. Various ecological environments affect the active ingredients and molecular content of medicinal plants. Artemisia rupestris L. is a traditional medicinal plant, and shortages of the wild resource has led to the increased use of artificial cultivated varieties. However, few investigations have studied the molecular differences between the cultivated and wild varieties in a systematic manner. In this study, two A. rupestris varieties were collected in the Altay–Fuyun region, Xinjiang, China. We used a liquid chromatography-mass spectrometry based untargeted metabolomics approach to profile the metabolome in the flower, stem, and leaf samples respectively, and simultaneously analyzed the levels of a panel of representative known metabolites in the plant. The genetic basis of these samples was explored using a de novo transcriptomics approach to investigate differentially expressed genes and their pathway annotations. The results indicated that the metabolic differences between the two varieties were mainly reflected in flavonoids and chlorogenic acid/caffeic acid derivatives. Thirty-four chemical markers, including 19 potentially new compounds, of these two structural categories were identified after validation using another batch of samples. Correlation analysis combined with quantitative real-time polymerase chain reaction revealed that six differentially expressed genes in different organs were closely correlated with 24 chemical markers. This study provides novel insight into the molecular landscape of this medicinal plant. [ABSTRACT FROM AUTHOR]