A novel approach to transforming a non-targeted metabolic profiling method to a pseudo-targeted method using the retention time locking gas chromatography/mass spectrometry-selected ions monitoring

Optimizing Skin Surface Metabolomics: A Comprehensive Evaluation of Sampling Methods, Extraction Solvents, and Analytical Techniques

Characterizing the metabolite fingerprint from the skin surface provides invaluable insights into skin biology and microbe-host interactions. To ensure data accuracy and reproducibility, it is essential to develop standard operating procedures for skin surface metabolomics. However, there is a notable lack of studies in this area. In this study, we thoroughly evaluated different sampling materials, extraction solvents, taping methods (frequency and number of tapes), and analytical techniques to optimize skin surface metabolomics. Our results showed that the combination of D-Squame D100 tape with a methyl tert-butyl ether/methanol extractant is optimal for skin surface lipidomics. Performing the skin-taping procedure 5 times with 1 tape yields sufficient biomass for lipid analysis, whereas the optimal taping procedure varies for water-soluble compounds. In addition, our study identified associations among the skin surface metabolites, some of which potentially underlie the formation of microbial cutotypes and offer insights into host-microbe interactions.

WTV2.0: A high-coverage plant volatilomics method with a comprehensive selective ion monitoring acquisition mode

Volatilomics is essential for understanding the biological functions and fragrance contributions of plant volatiles. However, the annotation coverage achieved using current untargeted and widely targeted volatomics (WTV) methods has been limited by low sensitivity and/or low acquisition coverage. Here, we introduce WTV 2.0, which enabled the construction of a high-coverage library containing 2111 plant volatiles, and report the development of a comprehensive selective ion monitoring (cSIM) acquisition method, including the selection of characteristic qualitative ions with the minimal ion number for each compound and an optimized segmentation method, that can acquire the smallest but sufficient number of ions for most plant volatiles, as well as the automatic qualitative and semi-quantitative analysis of cSIM data. Importantly, the library and acquisition method we developed can be self-expanded by incorporating compounds not present in the library, utilizing the obtained cSIM data. We showed that WTV 2.0 increases the median signal-to-noise ratio by 7.6-fold compared with the untargeted method, doubled the annotation coverage compared with the untargeted and WTV 1.0 methods in tomato fruit, and led to the discovery of menthofuran as a novel flavor compound in passion fruit. WTV 2.0 is a Python library with a user-friendly interface and is applicable to profiling of volatiles and primary metabolites in any species.

Innovative analytical methodologies for characterizing chemical exposure with a view to next-generation risk assessment

The chemical burden on the environment and human population is increasing. Consequently, regulatory risk assessment must keep pace to manage, reduce, and prevent adverse impacts on human and environmental health associated with hazardous chemicals. Surveillance of chemicals of known, emerging, or potential future concern, entering the environment-food-human continuum is needed to document the reality of risks posed by chemicals on ecosystem and human health from a one health perspective, feed into early warning systems and support public policies for exposure mitigation provisions and safe and sustainable by design strategies. The use of less-conventional sampling strategies and integration of full-scan, high-resolution mass spectrometry and effect-directed analysis in environmental and human monitoring programmes have the potential to enhance the screening and identification of a wider range of chemicals of known, emerging or potential future concern. Here, we outline the key needs and recommendations identified within the European Partnership for Assessment of Risks from Chemicals (PARC) project for leveraging these innovative methodologies to support the development of next-generation chemical risk assessment.

Development and application of a pseudotargeted lipidomics method for alkylglycerol analysis

Alkylglycerols (1-O-alkyl-sn-glycerols) are microscale but critical lipids in foods. Conventional lipidomics analysis often loses sight of alkylglycerol analysis. In this study, we developed a high coverage pseudotargeted lipidomics method for analyzing alkylglycerols. The developed method integrated the advantages of GC-MS and LC-MS to profile alkylglycerol-type ether lipids comprehensively, with the help of a data processing Dart package termed FFIMA (Feature Fragments Information Matching Algorithm). The developed method exhibited competitive superiority to conventional lipidomics, such as wider coverage and higher accuracy. The validated method was assessed by three aquatic products and three milks. A total of 25 alkylglycerols, 107 diacylglycerol ethers, 21 monoacylglycerol ethers, 28 alkylglycerol-type ether phospholipids, and 35 plasmalogens were identified in the six foods. The results demonstrated that this method offers a comprehensive analysis of a wide spectrum of alkylglycerols.

Exploring Metabolic Characteristics in Different Geographical Locations and Yields of Nicotiana tabacum L. Using Gas Chromatography-Mass Spectrometry Pseudotargeted Metabolomics Combined with Chemometrics

The quality of crops is closely associated with their geographical location and yield, which is reflected in the composition of their metabolites. Hence, we employed GC-MS pseudotargeted metabolomics to investigate the metabolic characteristics of high-, medium-, and low-yield Nicotiana tabacum (tobacco) leaves from the Bozhou (sweet honey flavour) and Shuicheng (light flavour) regions of Guizhou Province. A total of 124 metabolites were identified and classified into 22 chemical categories. Principal component analysis revealed that the geographical location exerted a greater influence on the metabolic profiling than the yield. Light-flavoured tobacco exhibited increased levels of sugar metabolism- and glycolysis-related intermediate products (trehalose, glucose-6-phosphate, and fructose-6-phosphate) and a few amino acids (proline and leucine), while sweet honey-flavoured tobacco exhibited increases in the tricarboxylic acid cycle (TCA cycle) and the phenylpropane metabolic pathway (p-hydroxybenzoic acid, caffeic acid, and maleic acid). Additionally, metabolite pathway enrichment analysis conducted at different yields and showed that both Shuicheng and Bozhou exhibited changes in six pathways and four of them were the same, mainly C/N metabolism. Metabolic pathway analysis revealed higher levels of intermediates related to glycolysis and sugar, amino acid, and alkaloid metabolism in the high-yield samples, while higher levels of phenylpropane in the low-yield samples. This study demonstrated that GC-MS pseudotargeted metabolomics-based metabolic profiling can be used to effectively discriminate tobacco leaves from different geographical locations and yields, thus facilitating a better understanding of the relationship between metabolites, yield, and geographical location. Consequently, metabolic profiles can serve as valuable indicators for characterizing tobacco yield and geographical location.

New Perspectives on the Role of Liquid Biopsy in Bladder Cancer: Applicability to Precision Medicine

Bladder cancer (BC) is one of the most common tumors in the world. Cystoscopy and tissue biopsy are the standard methods in screening and early diagnosis of suspicious bladder lesions. However, they are invasive procedures that may cause pain and infectious complications. Considering the limitations of both procedures, and the recurrence and resistance to BC treatment, it is necessary to develop a new non-invasive methodology for early diagnosis and multiple evaluations in patients under follow-up for bladder cancer. In recent years, liquid biopsy has proven to be a very useful diagnostic tool for the detection of tumor biomarkers. This non-invasive technique makes it possible to analyze single tumor components released into the peripheral circulation and to monitor tumor progression. Numerous biomarkers are being studied and interesting clinical applications for these in BC are being presented, with promising results in early diagnosis, detection of microscopic disease, and prediction of recurrence and response to treatment.

Pseudo-targeted metabolic profile differences between emergency patients with type 1 and type 2 myocardial infarction diagnosed by optical coherence tomography

BACKGROUND

It is difficult to distinguish type 2 myocardial infarction (T2MI) from type 1 myocardial infarction (T1MI), although their management varies.

OBJECTIVES

Using optical coherence tomography (OCT) and pseudo-targeted metabolomics to identify biomarkers, investigate metabolic differences, and establish a T2MI subclassification.

METHODS

Among 1519 patients with MI, 97 T2MI patients are identified who are 1:1 matched with 97 T1MI patients after considering age, gender, ST-segment elevation, time from onset to coronary angiography, and hs-cTnI on admission by propensity score matching. Plasma pseudo-targeted metabolomics at baseline was determined.

RESULTS

The clinical characteristics of the two groups were comparable, while the T1MI showed more severe coronary lesions than T2MI according to OCT imaging. 90 differential metabolites were identified between the two groups, among 1027 endogenous metabolites in 20 classes. N-Acetyl-L-Leucine, free fatty acid (15:1), Thymidine-5'-triphosphate, Mevalonic acid 5-pyrophosphate, and five oligopeptides were candidate biomarkers (AUC ≥ 0.85) distinguishing T2MI from T1MI. 12 KEGG pathways showed significant differences, mainly involving amino acid, nucleotide, and their derivatives metabolism, and signaling pathways such as mTOR, cGMP-PKG, and cAMP. Other differences were observed in TCA cycle (P = 0.08) and ROS (P = 0.05). Proteolysis and coronary heart disease risk lipid level were lower in T2MI. T2MI had a decrease of differential abundance score in almost all the KEGG enrichment pathways. Furthermore, T2MI can be subdivided into three subtypes by hierarchical cluster analysis of AUCs with causes/triggers of T2MI.

CONCLUSIONS

There are significant metabolic profile differences between T1MI and T2MI. Several candidate metabolic biomarkers can effectively distinguish the two groups.

CLINICAL TRIAL REGISTRATION

ClinicalTrials. gov NCT03297164.

Association of Placental Tissue Metabolite Levels with Gestational Diabetes Mellitus: a Metabolomics Study

The purpose of the study is to investigate the metabolic characteristics of placental tissue in patients diagnosed with gestational diabetes mellitus (GDM). Ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS) was employed to qualitatively and quantitatively analyze the metabolites in placental tissues obtained from 25 healthy pregnant women and 25 pregnant women diagnosed with GDM. Multilevel statistical methods are applied to process intricate metabolomics data. Meanwhile, we applied machine learning techniques to identify biomarkers that could potentially predict the risk of long-term complications in patients with GDM as well as their offspring. We identified 1902 annotated metabolites, out of which 212 metabolites exhibited significant differences in GDM placentas. In addition, the study identifies a set of risk biomarkers that effectively predict the likelihood of long-term complications in both pregnant women with GDM and their offspring. The accuracy of this panel was measured by the area under the receiver operating characteristic curve (ROC), which was found to be 0.992 and 0.960 in the training and validation sets, respectively. This study enhances our understanding of GDM pathogenesis through metabolomics. Furthermore, the panel of risk markers identified could prove to be a valuable tool in predicting potential long-term complications for both GDM patients and their offspring.

Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians

Hepatocellular carcinoma (HCC) is the most predominant primary liver cancer, causing many illnesses and deaths worldwide. The insidious clinical presentation, difficulty in early diagnosis, and the highly malignant nature make the prognosis of HCC extremely poor. The complex and heterogeneous pathogenesis of HCC poses significant challenges to developing therapies. Urine-based biomarkers for HCC, including diagnostic, prognostic, and monitoring markers, may be valuable supplements to current tools such as serum α-fetoprotein (AFP) and seem promising for progress in precision medicine. Herein, we reviewed the major urinary biomarkers for HCC and assessed their potential for clinical application. Molecular types, testing platforms, and methods for building multimolecule models in the included studies have shown great diversity, thus providing abundant novel tools for future clinical transformation and applications.

Recent Review on Selected Xenobiotics and Their Impacts on Gut Microbiome and Metabolome

As it is well known, the gut is one of the primary sites in any host for xenobiotics, and the many microbial metabolites responsible for the interactions between the gut microbiome and the host. However, there is a growing concern about the negative impacts on human health induced by toxic xenobiotics. Metabolomics, broadly including lipidomics, is an emerging approach to studying thousands of metabolites in parallel. In this review, we summarized recent advancements in mass spectrometry (MS) technologies in metabolomics. In addition, we reviewed recent applications of MS-based metabolomics for the investigation of toxic effects of xenobiotics on microbial and host metabolism. It was demonstrated that metabolomics, gut microbiome profiling, and their combination have a high potential to identify metabolic and microbial markers of xenobiotic exposure and determine its mechanism. Further, there is increasing evidence supporting that reprogramming the gut microbiome could be a promising approach to the intervention of xenobiotic toxicity.

Mass Spectrometry Detects Sphingolipid Metabolites for Discovery of New Strategy for Cancer Therapy from the Aspect of Programmed Cell Death

Sphingolipids, a type of bioactive lipid, play crucial roles within cells, serving as integral components of membranes and exhibiting strong signaling properties that have potential therapeutic implications in anti-cancer treatments. However, due to the diverse group of lipids and intricate mechanisms, sphingolipids still face challenges in enhancing the efficacy of different therapy approaches. In recent decades, mass spectrometry has made significant advancements in uncovering sphingolipid biomarkers and elucidating their impact on cancer development, progression, and resistance. Primary sphingolipids, such as ceramide and sphingosine-1-phosphate, exhibit contrasting roles in regulating cancer cell death and survival. The evasion of cell death is a characteristic hallmark of cancer cells, leading to treatment failure and a poor prognosis. The escape initiates with long-established apoptosis and extends to other programmed cell death (PCD) forms when patients experience chemotherapy, radiotherapy, and/or immunotherapy. Gradually, supportive evidence has uncovered the fundamental molecular mechanisms underlying various forms of PCD leading to the development of innovative molecular, genetic, and pharmacological tools that specifically target sphingolipid signaling nodes. In this study, we provide a comprehensive overview of the sphingolipid biomarkers revealed through mass spectrometry in recent decades, as well as an in-depth analysis of the six main forms of PCD (apoptosis, autophagy, pyroptosis, necroptosis, ferroptosis, and cuproptosis) in aspects of tumorigenesis, metastasis, and tumor response to treatments. We review the corresponding small-molecule compounds associated with these processes and their potential implications in cancer therapy.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

Blood-based liquid biopsy: insights into early detection, prediction, and treatment monitoring of bladder cancer

Bladder cancer (BC) is a clinical challenge worldwide with late clinical presentation, poor prognosis, and low survival rates. Traditional cystoscopy and tissue biopsy are routine methods for the diagnosis, prognosis, and monitoring of BC. However, due to the heterogeneity and limitations of tumors, such as aggressiveness, high cost, and limited applicability of longitudinal surveillance, the identification of tumor markers has attracted significant attention in BC. Over the past decade, liquid biopsies (e.g., blood) have proven to be highly efficient methods for the discovery of BC biomarkers. This noninvasive sampling method is used to analyze unique tumor components released into the peripheral circulation and allows serial sampling and longitudinal monitoring of tumor progression. Several liquid biopsy biomarkers are being extensively studied and have shown promising results in clinical applications of BC, including early detection, detection of microscopic residual disease, prediction of recurrence, and response to therapy. Therefore, in this review, we aim to provide an update on various novel blood-based liquid biopsy markers and review the advantages and current limitations of liquid biopsy in BC therapy. The role of blood-based circulating tumor cells, circulating tumor DNA, cell-free RNA, exosomes, metabolomics, and proteomics in diagnosis, prognosis, and treatment monitoring, and their applicability to the personalized management of BC, are highlighted.

Enhanced profiling and quantification of ginsenosides from mountain-cultivated ginseng and comparison with garden-cultivated ginseng

Panax ginseng can be generally divided into mountain-cultivated ginseng (MCG) and garden-cultivated ginseng (GCG). The market price of MCG is significantly higher than that of GCG. However, the chemical compositions of MCG and the differences from GCG remained unclear. In this study, an integrated strategy combing an offline two-dimensional liquid chromatography separation, LTQ-orbitrap dual mode acquisition, and Q-trap full quantification/quasi-quantification was proposed to explore and compare the chemical compositions of MCG. Consequently, 559 ginsenosides were characterized, among which 437 ginsenosides were in-depth characterized with α-chain and β-chain annotated. Subsequently, enhanced quantification of 213 ginsenosides was conducted in 57 batches of MCG and GCG. Ginsenosides were found more abundant in MCG than GCG. In addition, 25-year-old MCG could be distinctly differentiated from 15/20-year-old MCG. This strategy facilitated the enhanced profiling and comparison of ginsenosides, improved the quality control tactics of MCG and provided a reference approach for other ginseng related products.

Optimizing column-to-column retention time alignment in high-speed gas chromatography by combining retention time locking and correlation optimized warping

We examine and then optimize alignment of chromatograms collected on nominally identical columns using retention time locking (RTL), an instrumental alignment tool, and software-based alignment using correlation optimized warping (COW). For this purpose, three samples are constructed by spiking two sets of analytes into a base test mixture. The three samples are analyzed by high-speed gas chromatography with four nominally identical columns and identical separation conditions. The data is first analyzed without alignment, then using COW alone, then RTL alone, and finally with RTL followed by COW to correct the severe column-to-column misalignment. Principal component analysis (PCA) is used to investigate how well each alignment method clustered the chromatograms into the three sample classes via a scores plot without being compromised by the specific column(s) used. The degree-of-class separation (DCS) is used as a classification metric, measured as the Euclidian distance between the centroids of two clusters in PC space in the scores plot, normalized by their pooled variance. With no alignment, the average DCS between sample classes (DCS_sam) was 3.0, while the average DCS between the four nominally identical columns, i.e., column classes (DCS_col) was 76.1 (ideally the DCS_col should be 0), indicating the chromatograms were initially classified by the columns used. Using either COW or RTL alone also produced unsatisfactory results, with COW alone incorrectly aligning many peaks, leading to a DCS_sam of only 1.9 and DCS_col of 1.7, while RTL alone provided a DCS_sam of 4.7 and DCS_col of 4.2. Finally, using RTL followed by COW alignment, DCS_sam increased to 32.5, indicating successful classification by chemical differences between sample classes, while the DCS_col decreased to 0.4, indicating virtually no classification due to column-to-column differences, as desired. Thus, RTL provided a "first-order" correction of the initial retention mismatch observed for the nominally identical columns, while additional alignment via COW was required to optimize sample classification by PCA.

Kynurenic acid mediates bacteria-algae consortium in resisting environmental cadmium toxicity

Cadmium (Cd²⁺) is a toxic heavy metal in the environment, posing severe damage to animal health and drinking water safety. The bacteria-algae consortium remediates environmental Cd²⁺ pollution by secreting chelating reagents, but the molecular mechanisms remain elusive. Here, we showed that Cellulosimicrobium sp. SH8 isolated from a Cd²⁺-polluted lake could interact with Synechocystis sp. PCC6803, a model species of cyanobacteria, in strengthening Cd²⁺ toxicity resistance, while SH8 or PCC6803 alone barely immobilized Cd²⁺. In addition, the SH8-PCC6803 consortium, but not SH8 alone, could grow in a carbon-free medium, suggesting that autotrophic PCC6803 enabled the growth of heterotrophic SH8. Totally, 12 metabolites were significantly changed when SH8 was added to PCC6803 culture in the presence of Cd²⁺ (PCC6803/Cd²⁺). Among them, kynurenic acid was the only metabolite that precipitated Cd²⁺. Remarkably, adding kynurenic acid increased the growth of PCC6803/Cd²⁺ by 14.1 times. Consistently, the expressions of kynA, kynB, and kynT genes, known to be essential for kynurenic acid synthesis, were considerably increased when SH8 was added to PCC6803/Cd²⁺. Collectively, kynurenic acid secreted by SH8 mitigates Cd²⁺ toxicity for algae, and algae provide organic carbon for the growth of SH8, unveiling a critical link that mediates beneficial bacteria-algae interaction to resist Cd²⁺.

Environmental pseudotargeted metabolomics: A high throughput and wide coverage method for metabolic profiling of 1000-year paddy soil chronosequences

Pseudotargeted metabolomics is achieved by introducing an algorithm designed to choose ions for selected ion monitoring from identified metabolites. This method integrates the advantages of both untargeted and targeted metabolomics. In this study, environmental pseudotargeted metabolomics was established to analyze the soil metabolites, based on microwave assisted derivatization followed by gas chromatography-mass spectrometry analysis. The method development included the optimization of extraction factors and derivatization conditions, evaluation of silylation reagent types and matrix-dependent behaviors. Under the optimal conditions, the microwave oximation and silylation were completed in 5 min and 9 min. A total of 184 metabolites from 26 chemical classifications were identified in soil matrices. The method validation demonstrated excellent performance in terms of linearity (correlation coefficient > 0.99), repeatability (relative standard deviation (RSD) < 20 %), reproducibility (RSD < 25 %), stability (relative difference < 10 % within 18 h), and sensitivity (16-110 times higher signal-to-noise ratio). This developed method was applied to characterize the metabolite compositions and metabolic profiling in a 1000-year paddy soil chronosequence. The relative abundance of trehalose was highest in 6-(40.3 %), 60-(55.8 %), 300-(67.7 %)and 1000-(61.7 %)years paddy soil, respectively, but long-chain fatty acids were most abundant in marine sediment (57.4 %). Forty-two characteristic metabolites were considered as primarily responsible for discriminating and characterizing the paddy soil chronosequences development and seven major metabolic pathways were altered. In addition, GC-MS metabolite profile presented better discriminating power in paddy soil ecosystem changes than phospholipid fatty acids (PLFAs). Overall, environmental pseudotargeted metabolomics can provide a high throughout and wide coverage approach for performing metabolic profiling in the soil research.

Pseudotargeted Metabolomics Approach Enabling the Classification-Induced Ginsenoside Characterization and Differentiation of Ginseng and Its Compound Formulation Products

The use of diversified ginseng extracts in health-promoting foods is difficult to differentiate, as they share bioactive ginsenosides among different Panax species (e.g., P. ginseng, P. quinquefolius, P. notoginseng, and P. japonicus) and different parts (e.g., root, leaf, and flower). This work was designed to develop a pseudo-targeted metabolomics approach to discover ginsenoside markers facilitating the precise authentication of ginseng and its use in compound formulation products (CFPs). Versatile mass spectrometry experiments on the QTrap mass spectrometer achieved classified characterization of the neutral, malonyl, and oleanolic acid-type ginsenosides, with 567 components characterized. A pseudo-targeted metabolomics approach by multiple reaction monitoring (MRM) of 262 ion pairs could assist to establish key identification points for 12 ginseng species. The simultaneous detection of 14 markers enabled the identification of ginseng from 15 ginseng-containing CFPs. The pseudo-targeted metabolomics strategy enabled better performance in differentiating among multiple ginseng, compared with the full-scan high-resolution mass spectrometry approach.

Metabolomics in gestational diabetes mellitus: A review

Gestational diabetes mellitus (GDM), a common complication of pregnancy, is a type of diabetes that is first detected and diagnosed during pregnancy. The incidence of GDM is increasing annually and is associated with many adverse pregnancy outcomes. Early prediction of the risk of GDM and intervention are thus important to reduce adverse pregnancy outcomes. Studies have revealed a correlation between the levels of amino acids, fatty acids, triglycerides, and other metabolites in early pregnancy and the occurrence of GDM. The development of high-throughput technologies used in metabolomics has enabled the detection of changes in the levels of small-molecule metabolites during early pregnancy, which can help reflect the overall physiological and pathological status of the body and explore the underlying mechanisms of the development of GDM. This review sought to summarize current research in this field and provide data for the development of strategies to manage GDM.

Plasma metabolic signatures for intracranial aneurysm and its rupture identified by pseudotargeted metabolomics

BACKGROUND AND AIMS

The vital metabolic signatures for IA risk stratification and its potential biological underpinnings remain elusive. Our study aimed to develop an early diagnosis model and rupture classification model by analyzing plasma metabolic profiles of IA patients.

MATERIALS AND METHODS

Plasma samples from a cohort of 105 participants, including 75 IA patients in unruptured and ruptured status (UIA, RIA) and 30 control participants were collected for comprehensive metabolic evaluation using ultra-high-performance liquid chromatography-mass spectrometry-based pseudotargeted metabolomics method. Furthermore, an integrated machine learning strategy based on LASSO, random forest and logistic regression were used for feature selection and model construction.

RESULTS

The metabolic profiling disturbed significantly in UIA and RIA patients. Notably, adenosine content was significantly downregulated in UIA, and various glycine-conjugated secondary bile acids were decreased in RIA patients. Enriched KEGG pathways included glutathione metabolism and bile acid metabolism. Two sets of biomarker panels were defined to discriminate IA and its rupture with the area under receiver operating characteristic curve of 0.843 and 0.929 on the validation sets, respectively.

CONCLUSIONS

The present study could contribute to a better understanding of IA etiopathogenesis and facilitate discovery of new therapeutic targets. The metabolite panels may serve as potential non-invasive diagnostic and risk stratification tool for IA.

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.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Identification of Veratrum Species in Pimacao Based on ITS2 Sequences and Steroidal Alkaloids by a Pseudo-Targeted Metabolomics Method

Pimacao is a traditional Chinese folk medicine and is the main component of the famous Chinese herbal remedy "Yunnan Baiyao" for its significant analgesic activity in the treatment of wounds. Due to increases in consumption, its wild population is now difficult to find, and adulterant from the same genus has occurred. However, this is challenging to distinguish the species of Veratrum in Pimacao using dried roots and rhizomes or medicinal powder. ITS2 sequences and steroidal alkaloids by the non-targeted and pseudo-targeted metabolomics methods were taken advantage of establishing an effective identification method. Based on the ITS2 sequence, metabolite profiling of steroidal alkaloids and morphological characteristics, the classification of two distinct subspecies in V. mengzeanum has been reinforced. In addition, the new subspecies V. mengzeanum subsp. phuwae was collected in China for the first time. The ITS2 sequence could be used in the identification of V. taliense, V. mengtzeanum, V. stenophyllum, and V. nigrum, but is insufficient for intraspecific identification. Simultaneously, 147 variables were labeled by non-targeted analysis accomplished utilizing an ultra-high-performance liquid chromatography electrospray ionization orbitrap tandem mass spectrometry (UPLC-ESI-QE-Orbitrap-MS) system consisting of an Orbitrap QE HF-X. Followed by a pseudo-targeted analysis method developed for the Qtrap 6500-plus mass spectrometry system coupled with an ESI source, 29 labeled steroidal alkaloids detected by the MRM mode could distinguish between four species. Notably, 25 labeled steroidal alkaloids could distinguish between three closely related species. These have the potential to be used as markers for identification. Furthermore, there were several variables with statistical differences between two subspecies of V. mengtzeanum and populations of V. taliense, V. mengtzeanum, and V. stenophyllum.

A chemometric strategy to automatically screen selected ion monitoring ions for gas chromatography-mass spectrometry-based pseudotargeted metabolomics

The pseudotargeted metabolomics based on gas chromatography-mass spectrometry (GC-MS) has the advantage of filtering out artifacts originating from sample treatment and accurately quantifying underlying compounds in the analyzed samples. However, this technique faces the problem of selecting high-quality selective ions for performing selected ion monitoring (SIM) on instruments. In this work, we proposed AntDAS-SIMOpt, an automatic untargeted strategy for SIM ion optimization that was accomplished on the basis of an experimental design combined with advanced chemometric algorithms. First, a group of diluted quality control samples was used to screen underlying compounds in samples automatically. Ions in each of the resolved mass spectrum were then evaluated by using the developed algorithms to identify the SIM ion. A Matlab graphical user interface (GUI) was designed to facilitate routine analysis, which can be obtained from http://www.pmdb.org.cn/antdassimopt. The performance of the developed strategy was comprehensively investigated by using standard and complex plant datasets. Results indicated that AntDAS-SIMOpt may be useful for GC-MS-based metabolomics.

A high coverage pseudotargeted lipidomics method based on three-phase liquid extraction and segment data-dependent acquisition using UHPLC-MS/MS with application to a study of depression rats

Pseudotargeted analysis combines the advantages of untargeted and targeted lipidomics methods based on chromatography-mass spectrometry (MS). This study proposed a comprehensive pseudotargeted lipidomics method based on three-phase liquid extraction (3PLE) and segment data-dependent acquisition (SDDA). We used a 3PLE method to extract the lipids with extensive coverage from biological matrixes. 3PLE was composed of one aqueous and two organic phases. The upper and middle organic phases enriched neutral lipids and glycerophospholipids, respectively, combined and detected together. Besides, the SDDA strategy improved the detection of co-elution ions in the lipidomics analysis. A total of 554 potential lipids were detected by the developed approach in both positive and negative modes using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Compared with the conventional liquid-liquid extraction (LLE) approaches, including methyl tert-butyl ether (MTBE) and Bligh-Dyer (BD) methods, 3PLE combined with SDDA significantly increased the lipid coverage 87.2% and 89.7%, respectively. Also, the proposed pseudotargeted lipidomics approach exhibited higher sensitivity and better repeatability than the untargeted approach. Finally, we applied the established pseudotargeted method to the plasma lipid profiling from the depressed rats and screened 61 differential variables. The results demonstrated that the pseudotargeted method based on 3PLE and SDDA broadened lipid coverage and improved the detection of co-elution ions with excellent sensitivity and precision, indicating significant potential for the lipidomics analysis.

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.

Evaluation of metabolomics behavior of human colon cancer HT29 cell lines treated with ionic liquid graviola fruit pulp extract

ETHNOPHARMACOLOGICAL RELEVANCE

Medicinal plants have been used by indigenous people across the world for centuries to help individuals preserve their wellbeing and cure diseases. Annona muricata L. (Graviola) which is belonging to the Annonaceae family has been traditionally used due to its medicinal abilities including antimicrobial, anti-inflammatory, antioxidant and cancer cell growth inhibition. Graviola is claimed to be a potential antitumor due to its selective cytotoxicity against several cancer cell lines. However, the metabolic mechanism information underlying the anticancer activity remains limited.

AIM OF THE STUDY

This study aimed to investigate the effect of ionic liquid-Graviola fruit pulp extract (IL-GPE) on the metabolomics behavior of colon cancer (HT29) by using an untargeted GC-TOFMS-based metabolic profiling.

MATERIALS AND METHODS

Multivariate data analysis was used to determine the metabolic profiling, and the ingenuity pathway analysis (IPA) was used to predict the altered canonical pathways after treating the HT29 cells with crude IL-GPE and Taxol (positive control).

RESULTS

The principal components analysis (PCA) identified 44 metabolites with the most reliable factor loading, and the cluster analysis (CA) separated three groups of metabolites: metabolites specific to the non-treated HT29 cells, metabolites specific to the treated HT29 cells with the crude IL-GPE and metabolites specific to Taxol treatment. Pathway analysis of metabolomic profiles revealed an alteration of many metabolic pathways, including amino acid metabolism, aerobic glycolysis, urea cycle and ketone bodies metabolism that contribute to energy metabolism and cancer cell proliferation.

CONCLUSION

The crude IL-GPE can be one of the promising anticancer agents due to its selective inhibition of energy metabolism and cancer cell proliferation.

Combining metabolomics with bioanalysis methods to investigate the potential toxicity of dihexyl phthalate

Dihexyl phthalate (DHP) is one of the most commonly used phthalate esters in various plastic and consumer products. Human are inevitably exposed to DHPs. Although several animal and human experiments have revealed that DHP can cause multiple toxicities, few studies have previously assessed the effects of DHP exposure by liquid chromatography mass spectrometry (LC-MS) analysis combine with molecular biology methods on human cells. Therefore, the purpose of our study was to investigate the effect of DHP on human cell metabolism by systems biology methods. In this study, U₂ OS cancer cells were treated with 10 μM DHP for metabolomics analysis and apoptosis analysis at indicate time. Metabolomic study of the metabolic changes caused by DHP in U₂ OS cells was performed for the first time using integrative liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). To investigate the possible reason of fatty acids level altered by DHP, we measured some key fatty acid synthesis and oxidation-related enzyme expression levels by quantitative real-time PCR (Q-PCR). Apoptotic cells were analyzed by flow cytometry and apoptosis-related gene expressions were measured by Q-PCR. 2',7'-Dichlorofluorescein diacetate (DCFH-DA) staining was used to evaluate ROS content. Partial least squares-discriminate analysis (PLS-DA) clearly showed that significant differences in metabolic profiles were observed in U₂ OS cells exposed to DHP compared with controls. A total of 58 putative metabolites in electrospray ionization source (ESI) + mode and 32 putative metabolites in ESI-mode were detected, the majority of the differential metabolites being lipids and lipid-like molecules. Among them, the altered fatty acids level corresponded to expression levels of genes encoding enzymes related to fatty acids synthesis and oxidation. Moreover, DHP induced reactive oxygen species (ROS) accumulation, promoted cell apoptosis and inflammation, and resulted in a significant increase in apoptosis and inflammation-related gene expression levels compared with controls. In summary, our results suggested that metabolomics combined with molecular bioanalysis methods could be an efficient tool to assess toxic effects, which contribute to explore the possible cytotoxicity mechanisms of DHP, and provide a basis for further research.

Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources

The noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS.

Recent advances in analytical strategies for mass spectrometry-based lipidomics

Lipids are vital biological molecules and play multiple roles in cellular function of mammalian organisms such as cellular membrane anchoring, signal transduction, material trafficking and energy storage. Driven by the biological significance of lipids, lipidomics has become an emerging science in the field of omics. Lipidome in biological systems consists of hundreds of thousands of individual lipid molecules that possess complex structures, multiple categories, and diverse physicochemical properties assembled by different combinations of polar headgroups and hydrophobic fatty acyl chains. Such structural complexity poses a huge challenge for comprehensive lipidome analysis. Thanks to the great innovations in chromatographic separation techniques and the continuous advances in mass spectrometric detection tools, analytical strategies for lipidomics have been highly diversified so that the depth and breadth of lipidomics have been greatly enhanced. This review will present the current state of mass spectrometry-based analytical strategies including untargeted, targeted and pseudotargeted lipidomics. Recent typical applications of lipidomics in biomarker discovery, pathogenic mechanism and therapeutic strategy are summarized, and the challenges facing to the field of lipidomics are also discussed.

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.

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.

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.

UV-B Radiation Largely Promoted the Transformation of Primary Metabolites to Phenols in Astragalus mongholicus Seedlings

: Ultraviolet-B (UV-B) radiation (280-320 nm) may induce photobiological stress in plants, activate the plant defense system, and induce changes of metabolites. In our previous work, we found that between the two Astragalus varieties prescribed by the Chinese Pharmacopoeia, Astragalus mongholicus has better tolerance to UV-B. Thus, it is necessary to study the metabolic strategy of Astragalus under UV-B radiation further. In the present study, we used untargeted gas chromatography-mass spectrometry (GC-MS) and targeted liquid chromatography-mass spectrometry (LC-MS techniques) to investigate the profiles of primary and secondary metabolic. The profiles revealed the metabolic response of Astragalus to UV-B radiation. We then used real-time polymerase chain reaction (RT-PCR) to obtain the transcription level of relevant genes under UV-B radiation (UV-B supplemented in the field, λmax = 313 nm, 30 W, lamp-leaf distance = 60 cm, 40 min·day-1), which annotated the responsive mechanism of phenolic metabolism in roots. Our results indicated that supplemental UV-B radiation induced a stronger shift from carbon assimilation to carbon accumulation. The flux through the phenylpropanoids pathway increased due to the mobilization of carbon reserves. The response of metabolism was observed to be significantly tissue-specific upon the UV-B radiation treatment. Among phenolic compounds, C6C1 carbon compounds (phenolic acids in leaves) and C6C3C6 carbon compounds (flavones in leaves and isoflavones in roots) increased at the expense of C6C3 carbon compounds. Verification experiments show that the response of phenolics in roots to UV-B is activated by upregulation of relevant genes rather than phenylalanine. Overall, this study reveals the tissues-specific alteration and mechanism of primary and secondary metabolic strategy in response to UV-B radiation.

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.

Simultaneous Profiling and Holistic Comparison of the Metabolomes among the Flower Buds of Panax ginseng, Panax quinquefolius, and Panax notoginseng by UHPLC/IM-QTOF-HDMSE-Based Metabolomics Analysis

The flower buds of three Panax species (PGF: flower bud of P. ginseng; PQF: flower bud of P. quinquefolius; PNF: flower bud of P. notoginseng), widely consumed as healthcare products, are easily confused particularly in the extracts or traditional Chinese medicine (TCM) formulae. We are aimed to develop an untargeted metabolomics approach, by ultra-high performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UHPLC/IM-QTOF-MS) to unveil the chemical markers diagnostic for the differentiation of PGF, PQF, and PNF. Key parameters affecting chromatographic separation and MS detection were optimized in sequence. Forty-two batches of flower bud samples were analyzed in negative high-definition MS^E (HDMS^E; enabling three-dimensional separations). Efficient metabolomics data processing was performed by Progenesis QI (Waters, Milford, MA, USA), while pattern-recognition chemometrics was applied for species classification and potential markers discovery. Reference compounds comparison, analysis of both HDMS^E and targeted MS/MS data, and retrieval of an in-house ginsenoside library, were simultaneously utilized for the identification of discovered potential markers. Satisfactory conditions for metabolite profiling were achieved on a BEH Shield RP18 column and Vion™ IMS-QTOF instrument (Waters; by setting the capillary voltage of 1.0 kV and the cone of voltage 20 V) within 37 min. A total of 32 components were identified as the potential markers, of which Rb3, Ra1, isomer of m-Rc/m-Rb2/m-Rb3, isomer of Ra1/Ra2, Rb1, and isomer of Ra3, were the most important for differentiating among PGF, PQF, and PNF. Conclusively, UHPLC/IM-QTOF-MS-based metabolomics is a powerful tool for the authentication of TCM at the metabolome level.

Determination of trace water contents of organic solvents by gas chromatography-mass spectrometry-selected ion monitoring

This paper describes the development of a novel gas chromatography-mass spectrometry-selected ion monitoring (GC/MS-SIM) method for the determination of trace water contents of organic solvents, using the characteristic m/z 18, m/z 17, and m/z 16 ions of H₂O as the qualitative ion and the m/z 18 ion as the quantifier ion. The accuracy and precision of this method were validated. An excellent linear correlation was obtained for trace water contents between 0 and 0.5217 wt%, with a correlation coefficient (R²) of 0.9999, in addition to spike recoveries of 82.6-112.6%, and relative standard deviations (n = 6) of 0.4-7.2%. The limit of detection (S/N = 3) and limit of quantitation (S/N = 10) for the trace water contents of organic solvents were 0.0005% wt% and 0.0016% wt%, respectively.The analytical results confirmed that this method was useful for determining the trace water contents of organic solvents, because it has a low detection limit and wide linear range. It requires only small amounts of the samples and enables sample batch analysis. It is very environmentally friendly and saves reagents.

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.

Metabolites Re-programming and Physiological Changes Induced in Scenedesmus regularis under Nitrate Treatment

Nitrate is required to maintain the growth and metabolism of plant and animals. Nevertheless, in excess amount such as polluted water, its concentration can be harmful to living organisms such as microalgae. Recently, studies on microalgae response towards nutrient fluctuation are usually limited to lipid accumulation for the production of biofuels, disregarding the other potential of microalgae to be used in wastewater treatments and as source of important metabolites. Our study therefore captures the need to investigate overall metabolite changes via NMR spectroscopy approach coupled with multivariate data to understand the complex molecular process under high (4X) and low (1/4X) concentrations of nitrate (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{NO}}}_{{\bf{3}}}^{{\boldsymbol{-}}}$$\end{document}NO3−). NMR spectra with the aid of chemometric analysis revealed contrasting metabolites makeup under abundance and limited nitrate treatment. By using NMR technique, 43 types of metabolites and 8 types of fatty acid chains were detected. Nevertheless, only 20 key changes were observed and 16 were down regulated in limited nitrate condition. This paper has demonstrated the feasibility of NMR-based metabolomics approach to study the physiological impact of changing environment such as pollution to the implications for growth and productivity of microalgae population.

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.

Metabolomics and transcriptomics profiles reveal the dysregulation of the tricarboxylic acid cycle and related mechanisms in prostate cancer

Genetic alterations drive metabolic reprograming to meet increased biosynthetic precursor and energy demands for cancer cell proliferation and survival in unfavorable environments. A systematic study of gene-metabolite regulatory networks and metabolic dysregulation should reveal the molecular mechanisms underlying prostate cancer (PCa) pathogenesis. Herein, we performed gas chromatography-mass spectrometry (GC-MS)-based metabolomics and RNA-seq analyses in prostate tumors and matched adjacent normal tissues (ANTs) to elucidate the molecular alterations and potential underlying regulatory mechanisms in PCa. Significant accumulation of metabolic intermediates and enrichment of genes in the tricarboxylic acid (TCA) cycle were observed in tumor tissues, indicating TCA cycle hyperactivation in PCa tissues. In addition, the levels of fumarate and malate were highly correlated with the Gleason score, tumor stage and expression of genes encoding related enzymes and were significantly related to the expression of genes involved in branched chain amino acid degradation. Using an integrated omics approach, we further revealed the potential anaplerotic routes from pyruvate, glutamine catabolism and branched chain amino acid (BCAA) degradation contributing to replenishing metabolites for TCA cycle. Integrated omics techniques enable the performance of network-based analyses to gain a comprehensive and in-depth understanding of PCa pathophysiology and may facilitate the development of new and effective therapeutic strategies.

Plasma metabolic profiling analysis of Cortex Periplocae-induced cardiotoxicity based on UPLC/Q-TOF-MS

Cortex Periplocae is a well-known form of traditional medicine with its unique cardiotonic action, anti-tumor activity and immune regulation effect. However, improper use of Cortex Periplocae often leads to cardiac toxicity, which in the most severe cases can even be life-threatening. Biochemical tests and histopathological examinations are primary methods for clinical trials. However, such approaches are time-consuming, lack specificity and have low sensitivity, which can easily lead to negative results in studies. Therefore, a more scientific and systematic evaluation of Cortex Periplocae cardiotoxicity is particularly important. In this study, we established a method that combines metabonomics with trend analysis of a gavage concentration series to find cardiac toxicity biomarkers of Cortex Periplocae. We created rat cardiotoxicity models, in which the toxicity was caused by Cortex Periplocae. We collected data from rat plasma samples based on metabonomics using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS). Multiple statistical analyses, such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), were used to examine metabolite profile changes in plasma samples to screen potential cardiotoxicity biomarkers and metabolic pathways. Compared with the control group, after 7 days administration, the pathological sections showed cardiac toxicity. Moreover, some metabolites in the body changed significantly. Receiver operating characteristic curve (ROC) analysis showed that there are 11 metabolites related with cardiac toxicity, which play a role in "phenylalanine, tyrosine and tryptophan biosynthesis"; "phenylalanine metabolism"; "valine, leucine and isoleucine biosynthesis"; "glycerophospholipid metabolism" as well as "pantothenate and CoA biosynthesis". These metabolites can better explain the cardiotoxicity mechanism of Cortex Periplocae and provide a scientific and systematic method to evaluate the cardiotoxicity of Cortex Periplocae.

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.