Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases.

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Metabolic characterization of sphere-derived prostate cancer stem cells reveals aberrant urea cycle in stemness maintenance.

Alteration of cell metabolism is one of the essential characteristics of tumor growth. Cancer stem cells (CSCs) are the initiating cells of tumorigenesis, proliferation, recurrence, and other processes, and play an important role in therapeutic resistance and metastasis. Thus, identification of the metabolic profiles in prostate cancer stem cells (PCSCs) is critical to understanding prostate cancer progression. Using untargeted metabolomics and lipidomics methods, we show distinct metabolic differences between prostate cancer cells and PCSCs. Urea cycle is the most significantly altered metabolic pathway in PCSCs, the key metabolites arginine and proline are evidently elevated. Proline promotes cancer stem-like characteristics via the JAK2/STAT3 signaling pathway. Meanwhile, the enzyme pyrroline-5-carboxylate reductase 1 (PYCR1), which catalyzes the conversion of pyrroline-5-carboxylic acid to proline, is highly expressed in PCSCs, and the inhibition of PYCR1 suppresses the stem-like characteristics of prostate cancer cells and tumor growth. In addition, carnitine and free fatty acid levels are significantly increased, indicating reprogramming of fatty acid metabolism in PCSCs. Reduced sphingolipid levels and increased triglyceride levels are also observed. Collectively, our data illustrate the comprehensive landscape of the metabolic reprogramming of PCSCs and provide potential therapeutic strategies for prostate cancer.

Leveraging Unidentified Metabolic Features for Key Pathway Discovery: Chemical Classification-driven Network Analysis in Untargeted Metabolomics.

Untargeted metabolomics using liquid chromatography-electrospray ionization-high-resolution tandem mass spectrometry (UPLC-ESI-MS/MS) provides comprehensive insights into the dynamic changes of metabolites in biological systems. However, numerous unidentified metabolic features limit its utilization. In this study, a novel approach, the Chemical Classification-driven Molecular Network (CCMN), was proposed to unveil key metabolic pathways by leveraging hidden information within unidentified metabolic features. The method was demonstrated by using the herbivore-induced metabolic response in corn silk as a case study. Untargeted metabolomics analysis using UPLC-MS/MS was performed on wild corn silk and two genetically modified lines (pre- and postinsect treatment). Global annotation initially identified 256 (ESI-) and 327 (ESI+) metabolites. MS/MS-based classifications predicted 1939 (ESI-) and 1985 (ESI+) metabolic features into the chemical classes. CCMNs were then constructed using metabolic features shared classes, which facilitated the structure- or class annotation for completely unknown metabolic features. Next, 844/713 significantly decreased and 1593/1378 increased metabolites in ESI-/ESI+ modes were defined in response to insect herbivory, respectively. Method validation on a spiked maize sample demonstrated an overall class prediction accuracy rate of 95.7%. Potential key pathways were prescreened by a hypergeometric test using both structure- and class-annotated differential metabolites. Subsequently, CCMN was used to deeply amend and uncover the pathway metabolites deeply. Finally, 8 key pathways were defined, including phenylpropanoid (C6-C3), flavonoid, octadecanoid, diterpenoid, lignan, steroid, amino acid/small peptide, and monoterpenoid. This study highlights the effectiveness of leveraging unidentified metabolic features. CCMN-based key pathway analysis reduced the bias in conventional pathway enrichment analysis. It provides valuable insights into complex biological processes.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

Strategy for Comprehensive Detection and Annotation of Gut Microbiota-Related Metabolites Based on Liquid Chromatography-High-Resolution Mass Spectrometry.

Gut microbiota, widely populating the mammalian gastrointestinal tract, plays an important role in regulating diverse pathophysiological processes by producing bioactive molecules. Extensive detection of these molecules contributes to probing microbiota function but is limited by insufficient identification of existing analytical methods. In this study, a systematic strategy was proposed to detect and annotate gut microbiota-related metabolites on a large scale. A pentafluorophenyl (PFP) column-based liquid chromatography-high-resolution mass spectrometry (LC-HRMS) method was first developed for high-coverage analysis of gut microbiota-related metabolites, which was verified to be stable and robust with a wide linearity range, high sensitivity, satisfactory recovery, and repeatability. Then, an informative database integrating 968 knowledge-based microbiota-related metabolites and 282 sample-sourced ones defined by germ-free (GF)/antibiotic-treated (ABX) models was constructed and subsequently used for targeted extraction and annotation in biological samples. Using pooled feces, plasma, and urine of mice for demonstration application, 672 microbiota-related metabolites were annotated, including 21% neglected by routine nontargeted peak detection. This strategy serves as a useful tool for the comprehensive capture of the intestinal flora metabolome, contributing to our deeper understanding of microbe-host interactions.

Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices.

Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.

Prediction of plant secondary metabolic pathways using deep transfer learning.

BACKGROUND: Plant secondary metabolites are highly valued for their applications in pharmaceuticals, nutrition, flavors, and aesthetics. It is of great importance to elucidate plant secondary metabolic pathways due to their crucial roles in biological processes during plant growth and development. However, understanding plant biosynthesis and degradation pathways remains a challenge due to the lack of sufficient information in current databases. To address this issue, we proposed a transfer learning approach using a pre-trained hybrid deep learning architecture that combines Graph Transformer and convolutional neural network (GTC) to predict plant metabolic pathways. RESULTS: GTC provides comprehensive molecular representation by extracting both structural features from the molecular graph and textual information from the SMILES string. GTC is pre-trained on the KEGG datasets to acquire general features, followed by fine-tuning on plant-derived datasets. Four metrics were chosen for model performance evaluation. The results show that GTC outperforms six other models, including three previously reported machine learning models, on the KEGG dataset. GTC yields an accuracy of 96.75%, precision of 85.14%, recall of 83.03%, and F1_score of 84.06%. Furthermore, an ablation study confirms the indispensability of all the components of the hybrid GTC model. Transfer learning is then employed to leverage the shared knowledge acquired from the KEGG metabolic pathways. As a result, the transferred GTC exhibits outstanding accuracy in predicting plant secondary metabolic pathways with an average accuracy of 98.30% in fivefold cross-validation and 97.82% on the final test. In addition, GTC is employed to classify natural products. It achieves a perfect accuracy score of 100.00% for alkaloids, while the lowest accuracy score of 98.42% for shikimates and phenylpropanoids. CONCLUSIONS: The proposed GTC effectively captures molecular features, and achieves high performance in classifying KEGG metabolic pathways and predicting plant secondary metabolic pathways via transfer learning. Furthermore, GTC demonstrates its generalization ability by accurately classifying natural products. A user-friendly executable program has been developed, which only requires the input of the SMILES string of the query compound in a graphical interface.

Redox state and altered pyruvate metabolism contribute to a dose-dependent metformin-induced lactate production of human myotubes.

Metformin-induced glycolysis and lactate production can lead to acidosis as a life-threatening side effect, but slight increases in blood lactate levels in a physiological range were also reported in metformin-treated patients. However, how metformin increases systemic lactate concentrations is only partly understood. Because human skeletal muscle has a high capacity to produce lactate, the aim was to elucidate the dose-dependent regulation of metformin-induced lactate production and the potential contribution of skeletal muscle to blood lactate levels under metformin treatment. This was examined by using metformin treatment (16-776 µM) of primary human myotubes and by 17 days of metformin treatment in humans. As from 78 µM, metformin induced lactate production and secretion and glucose consumption. Investigating the cellular redox state by mitochondrial respirometry, we found metformin to inhibit the respiratory chain complex I (776 µM, P < 0.01) along with decreasing the [NAD+]:[NADH] ratio (776 µM, P < 0.001). RNA sequencing and phospho-immunoblot data indicate inhibition of pyruvate oxidation mediated through phosphorylation of the pyruvate dehydrogenase (PDH) complex (39 µM, P < 0.01). On the other hand, in human skeletal muscle, phosphorylation of PDH was not altered by metformin. Nonetheless, blood lactate levels were increased under metformin treatment (P < 0.05). In conclusion, the findings suggest that metformin-induced inhibition of pyruvate oxidation combined with altered cellular redox state shifts the equilibrium of the lactate dehydrogenase (LDH) reaction leading to a dose-dependent lactate production in primary human myotubes.NEW & NOTEWORTHY Metformin shifts the equilibrium of lactate dehydrogenase (LDH) reaction by low dose-induced phosphorylation of pyruvate dehydrogenase (PDH) resulting in inhibition of pyruvate oxidation and high dose-induced increase in NADH, which explains the dose-dependent lactate production of differentiated human skeletal muscle cells.

Ex vivo instability of lipids in whole blood: preanalytical recommendations for clinical lipidomics studies.

Reliability, robustness, and interlaboratory comparability of quantitative measurements is critical for clinical lipidomics studies. Lipids' different ex vivo stability in blood bears the risk of misinterpretation of data. Clear recommendations for the process of blood sample collection are required. We studied by UHPLC-high resolution mass spectrometry, as part of the "Preanalytics interest group" of the International Lipidomics Society, the stability of 417 lipid species in EDTA whole blood after exposure to either 4°C, 21°C, or 30°C at six different time points (0.5 h-24 h) to cover common daily routine conditions in clinical settings. In total, >800 samples were analyzed. 325 and 288 robust lipid species resisted 24 h exposure of EDTA whole blood to 21°C or 30°C, respectively. Most significant instabilities were detected for FA, LPE, and LPC. Based on our data, we recommend cooling whole blood at once and permanent. Plasma should be separated within 4 h, unless the focus is solely on robust lipids. Lists are provided to check the ex vivo (in)stability of distinct lipids and potential biomarkers of interest in whole blood. To conclude, our results contribute to the international efforts towards reliable and comparable clinical lipidomics data paving the way to the proper diagnostic application of distinct lipid patterns or lipid profiles in the future.

Annotation of Dipeptides and Tripeptides Derivatized via Dansylation Based on Liquid Chromatography-Mass Spectrometry and Iterative Quantitative Structure Retention Relationship.

Small peptides such as dipeptides and tripeptides show various biological activities in organisms. However, methods for identifying dipeptides/tripeptides from complex biological samples are lacking. Here, an annotation strategy involving the derivatization of dipeptides and tripeptides via dansylation was suggested based on liquid chromatography-mass spectrometry (LC-MS) and iterative quantitative structure retention relationship (QSRR) to choose dipeptides/tripeptides by using a small number of standards. First, the LC-autoMS/MS method and initial QSRR model were built based on 25 selected grid-dipeptides and 18 test-dipeptides. To achieve high-coverage detection, dipeptide/tripeptide pools containing abundant dipeptides/tripeptides were then obtained from four dansylated biological samples including serum, tissue, feces, and soybean paste by using the parameter-optimized LC-autoMS/MS method. The QSRR model was further optimized through an iterative train-by-pick strategy. Based on the specific fragments and tR tolerances, 198 dipeptides and 149 tripeptides were annotated. The dipeptides at lower annotation levels were verified by using authentic standards and grid-correlation analysis. Finally, variation in serum dipeptides/tripeptides of three different liver diseases including hepatitis B infection, liver cirrhosis, and hepatocellular carcinoma was characterized. Dipeptides with N-prolinyl, C-proline, N-glutamyl, and N-valinyl generally increased with disease severity. In conclusion, this study provides an efficient strategy for annotating dipeptides/tripeptides from complex samples.

Integrative analysis of metabolomics and proteomics unravels purine metabolism dysregulation in the SOD1G93A mouse model of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with progressive paralysis of limbs and bulb in patients, the cause of which remains unclear. Accumulating studies suggest that motor neuron degeneration is associated with systemic metabolic impairment in ALS. However, the metabolic reprogramming and underlying mechanism in the longitudinal progression of the disease remain poorly understood. In this study, we aimed to investigate the molecular changes at both metabolic and proteomic levels during disease progression to identify the most critical metabolic pathways and underlying mechanisms involved in ALS pathophysiological changes. Utilizing liquid chromatography-mass spectrometry-based metabolomics, we analyzed the metabolites' levels of plasma, lumbar spinal cord, and motor cortex from SOD1G93A mice and wildtype (WT) littermates at different stages. To elucidate the regulatory network underlying metabolic changes, we further analyzed the proteomics profile in the spinal cords of SOD1G93A and WT mice. A group of metabolites implicated in purine metabolism, methionine cycle, and glycolysis were found differentially expressed in ALS mice, and abnormal expressions of enzymes involved in these metabolic pathways were also confirmed. Notably, we first demonstrated that dysregulation of purine metabolism might contribute to the pathogenesis and disease progression of ALS. Furthermore, we discovered that fatty acid metabolism, TCA cycle, arginine and proline metabolism, and folate-mediated one­carbon metabolism were also significantly altered in this disease. The identified differential metabolites and proteins in our study could complement existing data on metabolic reprogramming in ALS, which might provide new insight into the pathological mechanisms and novel therapeutic targets of ALS.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation.

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach.

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

Medium-chain triglyceride production in Nannochloropsis via a fatty acid chain length discriminating mechanism.

Depending on their fatty acid (FA) chain length, triacylglycerols (TAGs) have distinct applications; thus, a feedstock with a genetically designed chain length is desirable to maximize process efficiency and product versatility. Here, ex vivo, in vitro, and in vivo profiling of the large set of type-2 diacylglycerol acyltransferases (NoDGAT2s) in the industrial oleaginous microalga Nannochloropsis oceanica revealed two endoplasmic reticulum-localized enzymes that can assemble medium-chain FAs (MCFAs) with 8-12 carbons into TAGs. Specifically, NoDGAT2D serves as a generalist that assembles C8-C18 FAs into TAG, whereas NoDGAT2H is a specialist that incorporates only MCFAs into TAG. Based on such specialization, stacking of NoDGAT2D with MCFA- or diacylglycerol-supplying enzymes or regulators, including rationally engineering Cuphea palustris acyl carrier protein thioesterase, Cocos nucifera lysophosphatidic acid acyltransferase, and Arabidopsis thaliana WRINKLED1, elevated the medium-chain triacylglycerol (MCT) share in total TAG 66-fold and MCT productivity 64.8-fold at the peak phase of oil production. Such functional specialization of NoDGAT2s in the chain length of substrates and products reveals a dimension of control in the cellular TAG profile, which can be exploited for producing designer oils in microalgae.

Triptolide suppresses IDH1-mutated malignancy via Nrf2-driven glutathione metabolism.

Isocitrate dehydrogenase (IDH) mutation is a common genetic abnormality in human malignancies characterized by remarkable metabolic reprogramming. Our present study demonstrated that IDH1-mutated cells showed elevated levels of reactive oxygen species and higher demands on Nrf2-guided glutathione de novo synthesis. Our findings showed that triptolide, a diterpenoid epoxide from Tripterygium wilfordii, served as a potent Nrf2 inhibitor, which exhibited selective cytotoxicity to patient-derived IDH1-mutated glioma cells in vitro and in vivo. Mechanistically, triptolide compromised the expression of GCLC, GCLM, and SLC7A11, which disrupted glutathione metabolism and established synthetic lethality with reactive oxygen species derived from IDH1 mutant neomorphic activity. Our findings highlight triptolide as a valuable therapeutic approach for IDH1-mutated malignancies by targeting the Nrf2-driven glutathione synthesis pathway.

Z-Ligustilide Combined with Cisplatin Reduces PLPP1-Mediated Phospholipid Synthesis to Impair Cisplatin Resistance in Lung Cancer.

Lung cancer is a malignant tumor with one of the highest morbidity and mortality rates in the world. Approximately 80-85% of lung cancer is diagnosed as non-small lung cancer (NSCLC), and its 5-year survival rate is only 21%. Cisplatin is a commonly used chemotherapy drug for the treatment of NSCLC. Its efficacy is often limited by the development of drug resistance after long-term treatment. Therefore, determining how to overcome cisplatin resistance, enhancing the sensitivity of cancer cells to cisplatin, and developing new therapeutic strategies are urgent clinical problems. Z-ligustilide is the main active ingredient of the Chinese medicine Angelica sinensis, and has anti-tumor activity. In the present study, we investigated the effect of the combination of Z-ligustilide and cisplatin (Z-ligustilide+cisplatin) on the resistance of cisplatin-resistant lung cancer cells and its mechanism of action. We found that Z-ligustilide+cisplatin decreased the cell viability, induced cell cycle arrest, and promoted the cell apoptosis of cisplatin-resistant lung cancer cells. Metabolomics combined with transcriptomics revealed that Z-ligustilide+cisplatin inhibited phospholipid synthesis by upregulating the expression of phospholipid phosphatase 1 (PLPP1). A further study showed that PLPP1 expression was positively correlated with good prognosis, whereas the knockdown of PLPP1 abolished the effects of Z-ligustilide+cisplatin on cell cycle and apoptosis. Specifically, Z-ligustilide+cisplatin inhibited the activation of protein kinase B (AKT) by reducing the levels of phosphatidylinositol 3,4,5-trisphosphate (PIP3). Z-ligustilide+cisplatin induced cell cycle arrest and promoted the cell apoptosis of cisplatin-resistant lung cancer cells by inhibiting PLPP1-mediated phospholipid synthesis. Our findings demonstrate that the combination of Z-Ligustilide and cisplatin is a promising approach to the chemotherapy of malignant tumors that are resistant to cisplatin.

MetEx: A Targeted Extraction Strategy for Improving the Coverage and Accuracy of Metabolite Annotation in Liquid Chromatography-High-Resolution Mass Spectrometry Data.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is the most popular platform for untargeted metabolomics studies, but compound annotation is a challenge. In this work, we developed a new LC-HRMS data-targeted extraction method called MetEx for metabolite annotation. MetEx contains the retention time (tR), MS1, and MS2 information of 30 620 metabolites from freely available spectral databases, including MoNA and KEGG. The tR values of 95.4% of the compounds in our database were calculated by the GNN-RT model. The MS2 spectra of 39.4% compounds were also predicted using CFM-ID. MetEx was initially examined on a mixture of 634 standards, considering chemical coverage and accurate metabolite assignment, and later applied to human plasma (NIST SRM 1950), human urine, HepG2 cells, mouse liver tissue, and mouse feces. MetEx correctly assigned 252 out of 253 standards detected in our instruments. The platform also provided 8.0-44.2% more compounds in the biological samples compared to XCMS, MS-DIAL, and MZmine 2. MetEx is implemented and visualized in R and freely available at http://www.metaboex.cn/MetEx.

Novel Method for Comprehensive Annotation of Plant Glycosides Based on Untargeted LC-HRMS/MS Metabolomics.

Glycosides are a large family of secondary metabolites in plants, which play a critical role in plant growth and development. Due to the complexity and diversity in structures and the limited availability of authentic standards, comprehensive annotation of the glycosides remains a great challenge. In this study, using maize as an example, a deep annotation method of glycosides was proposed based on untargeted liquid chromatography-high-resolution tandem mass spectrometry metabolomics analysis. First, knowledge-based in silico aglycone and glycosyl/acyl-glycosyl libraries were built. A total of 1240 known and potential aglycones from databases and literature were recorded. Next, the MS parameters beneficial to aglycone ion-rich MS/MS were explored using 1782 high-resolution MS/MS spectra of glycosides from the MassBank of North America (MoNA) and confirmed by 52 authentic glycoside standards. Then, screening rules for aglycon ions in MS/MS were recommended. Glycoside candidates were further filtered by MS/MS-based chemical classification and MS/MS similarity of aglycon-glycoside pairs. Finally, the glycosylation sites of flavonoid mono-O-glycosides were recommended by characteristic fragmentation patterns. The developed method was validated using glycosides and nonglycosides from the MoNA library. The annotation accuracy rates were 96.8, 94.9, and 98.0% in negative ion mode (ESI-), positive ion mode (ESI+), and the combined ESI- & ESI+, respectively. The annotation specificity was 99.6% (ESI-), 99.6% (ESI+), and 99.2% (ESI- & ESI+). A total of 274 glycosides (including 34 acyl-glycosides) were tentatively annotated in maize by the developed method. The method enables effective and reliable annotation for plant glycosides.

Nontargeted screening of veterinary drugs and their metabolites in milk based on mass defect filtering using liquid chromatography-high-resolution mass spectrometry.

The development of nontargeted screening strategy for veterinary drugs and their metabolites is very important for food safety. In this study, a nontargeted screening strategy was developed to find the potentially hazardous substances based on mass defect filtering (MDF) using liquid chromatography-high-resolution mass spectrometry. First, the drug metabolites of 112 veterinary drugs from seven classes of antimicrobials were predicted. Second, three MDF models were established, including the traditional rectangular MDF, the enhanced parallelogram MDF, and the polygonal MDF. Finally, the strategy was applied to nontargeted screening of veterinary drugs in 36 milk samples. The polygonal MDF model based on the distribution area of parent drugs and their metabolites showed a better filtering effect. After removing food components and performing MDF, about 10% of the substances remained, and four veterinary drugs and six drug metabolites were discovered and identified, showing the effectiveness of this strategy. The nontargeted screening strategy can rapidly remove interfering substances and find the suspected compounds. It can also be used for nontargeted screening of veterinary drugs and their metabolites in other food matrices.

Metabolome-Wide Association Study of Multiple Plasma Metals with Serum Metabolomic Profile among Middle-to-Older-Aged Chinese Adults.

Metal exposure has been associated with risk of various cardio-metabolic disorders, and investigation on the association between exposure to multiple metals and metabolic responses may reveal novel clues to the underlying mechanisms. Based on a metabolome-wide association study of 17 plasma metals with untargeted metabolomic profiling of 189 serum metabolites among 1992 participants within the Dongfeng-Tongji cohort, we replicated two metal-associated pathways, linoleic acid metabolism and aminoacyl-tRNA biosynthesis, with novel metal associations (false discovery rate, FDR < 0.05), and we also identified two novel pathways, including biosynthesis of unsaturated fatty acids and alpha-linolenic acid metabolism, as associated with metal exposure (FDR < 0.05). Moreover, two-way orthogonal partial least-squares analysis showed that five metabolites, including aspartylphenylalanine, free fatty acid 14:1, uridine, carnitine C14:2, and LPC 18:2, contributed most to the joint covariation between the two data matrices (12.3%, 8.3%, 8.0%, 7.4%, and 7.3%, respectively). Further BKMR analysis showed significant positive joint associations of plasma Al, As, Ba, and Zn with aspartylphenylalanine and of plasma Ba, Co, Mn, and Pb with carnitine C14:2, when all the metals were at the 55th percentiles or above, compared with the median. We also found significant interactions between As and Ba in the association with aspartylphenylalanine (P for interaction = 0.048) and between Ba and Pb in the association with carnitine C14:2 (P for interaction < 0.001). Together, these findings may provide new insights into the mechanisms underlying the adverse health effects induced by metal exposure.