An exposome atlas of serum reveals the risk of chronic diseases in the Chinese population.

Although adverse environmental exposures are considered a major cause of chronic diseases, current studies provide limited information on real-world chemical exposures and related risks. For this study, we collected serum samples from 5696 healthy people and patients, including those with 12 chronic diseases, in China and completed serum biomonitoring including 267 chemicals via gas and liquid chromatography-tandem mass spectrometry. Seventy-four highly frequently detected exposures were used for exposure characterization and risk analysis. The results show that region is the most critical factor influencing human exposure levels, followed by age. Organochlorine pesticides and perfluoroalkyl substances are associated with multiple chronic diseases, and some of them exceed safe ranges. Multi-exposure models reveal significant risk effects of exposure on hyperlipidemia, metabolic syndrome and hyperuricemia. Overall, this study provides a comprehensive human serum exposome atlas and disease risk information, which can guide subsequent in-depth cause-and-effect studies between environmental exposures and human health.

MSDCNN: A multiscale dilated convolution neural network for fine-grained 3D shape classification.

Multi-view deep neural networks have shown excellent performance on 3D shape classification tasks. However, global features aggregated from multiple views data often lack content information and spatial relationship, which leads to difficult identification the small variance among subcategories in the same category. To solve this problem, in this paper, a novel multiscale dilated convolution neural network termed as MSDCNN is proposed for multi-view fine-grained 3D shape classification. Firstly, a sequence of views are rendered from 12-viewpoints around the input 3D shape by the sequential view capturing module. Then, the first 22 convolution layers of ResNeXt50 is employed to extract the semantic features of each view, and a global mixed feature map is obtained through the element-wise maximum operation of the 12 output feature maps. Furthermore, attention dilated module (ADM), which combines four concatenated attention dilated block (ADB), is designed to extract larger receptive field features from global mixed feature map to enhance context information among the views. Specifically, each ADB is consisted by an attention mechanism module and a dilated convolution with different dilation rates. In addition, prediction module with label smoothing is proposed to classify features, which contains 3 × 3 convolution and adaptive average pooling. The performance of our method is validated experimentally on the ModelNet10, ModelNet40 and FG3D datasets. Experimental results demonstrate the effectiveness and superiority of the proposed MSDCNN framework for 3D shape fine-grained classification.

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.

Analysis of dipeptides in Chinese liquors based on dansylation combined with liquid chromatography-mass spectrometry.

Dipeptides have been shown to be an important taste substance in alcoholic beverages. However, the characterization of dipeptides in Chinese liquors was poor. Here, dansylation combined with liquid chromatography - mass spectrometry was employed to analyze dipeptides in eight liquors of two flavors. Consequently, 35 dipeptides were identified from liquors and 32 of them were quantified. Dipeptide quantification showed LODs smaller than 2.5 ng/mL. The calibration curves showed concentration spans from two to three orders of magnitude with satisfactory linearity. The matrix effects in low and high concentrations were from -25.71 % to 24.19 % and -14.82 % to 20.73 %, respectively. Intra- and inter-day precision is lower than 15 % for both low and high concentrations. The dipeptide contents in sauce flavor liquors were higher than those in strong flavor liquors. Ala- and -Phe dipeptides showed their unique trends between sauce and strong flavor liquors. This study provides new clues to evaluate taste of liquors.

Maturation of the gut metabolome during the first year of life in humans.

The gut microbiota is involved in the production of numerous metabolites that maintain host wellbeing. The assembly of the gut microbiome is highly dynamic, and influenced by many postnatal factors, moreover, little is known about the development of the gut metabolome. We showed that geography has an important influence on the microbiome dynamics in the first year of life based on two independent cohorts from China and Sweden. Major compositional differences since birth were the high relative abundance of Bacteroides in the Swedish cohort and Streptococcus in the Chinese cohort. We analyzed the development of the fecal metabolome in the first year of life in the Chinese cohort. Lipid metabolism, especially acylcarnitines and bile acids, was the most abundant metabolic pathway in the newborn gut. Delivery mode and feeding induced particular differences in the gut metabolome since birth. In contrast to C-section newborns, medium- and long-chain acylcarnitines were abundant at newborn age only in vaginally delivered infants, associated by the presence of bacteria such as Bacteroides vulgatus and Parabacteroides merdae. Our data provide a basis for understanding the maturation of the fecal metabolome and the metabolic role of gut microbiota in infancy.

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.

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.

Retention time prediction for small samples based on integrating molecular representations and adaptive network.

Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data size is usually small in most chromatography systems. To enhance the performance of RT prediction, this study proposes a RT prediction method based on multi-data combinations and adaptive neural network (MDC-ANN). MDC-ANN establishes the RT prediction model for the target chromatographic system through transfer learning and a base deep learning model trained on a big dataset. It selects the optimal molecular representation combination from the multiple input candidates and automatically determines the neural network structure according to the determined input combination. MDC-ANN was compared with two new efficient deep learning methods, three transferring methods and four popular machine learning methods on 14 small datasets and showed advantages in MAE, MedAE, MRE and R2 in most cases. The experiment results illustrated that integrating multiple molecular representations can provide more information, improve the performance of RT prediction and contribute to compound annotation, different chromatographic systems may use different molecular representation combinations to obtain good RT prediction performance. Hence, MDC-ANN which automatically determines the best combination of molecular representations for a specific system is promising for predicting RTs accurately in real applications.

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.

In-depth profiling of carboxyl compounds in Chinese Baijiu based on chemical derivatization and ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry.

Carboxyl compounds have a significant influence on the flavor of Chinese Baijiu. However, because of the structural diversity and low concentration, the deep profiling of carboxyl compounds in Chinese Baijiu is still challenging. In this work, a systematic method for comprehensive analysis of carboxyl compounds in Chinese Baijiu was established. After derivatized under optimized conditions, 197 p-dimethylaminophenacyl bromide-derived carboxylic compounds were annotated by multidimensional information including accurate mass, predicted tR, in-silico MS/MS, and diagnostic ions for the first time. In addition, 48 of the 197 carboxyl compounds were positively identified, and three of them were newly identified in Chinese Baijiu. Moreover, we found the number and the concentration of carboxyl compounds in sauce-flavor Baijiu were more abundant than in strong-flavor Baijiu. This work provides a novel method for the analysis of carboxyl compounds in Baijiu and other complex samples.

[A novel method for efficient screening and annotation of important pathway-associated metabolites based on the modified metabolome and probe molecules].

Plants produce a wide variety of secondary metabolites in the process of evolution. Secondary metabolites have highly diverse structures due to the modification of the basic skeletons of metabolites. They are required for interaction with the environment and are produced in response to abiotic/biotic stress. Characterization of secondary metabolic pathways is significant to plant molecular breeding and natural product biosynthesis. The liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS) is one of the major techniques for untargeted metabolomics study. The LC-HRMS/MS method could detect tens of thousands of metabolic features and provide abundant structural information. It has been widely used in the discovery and characterization of the secondary metabolome. However, due to the largely diverse structure and limited records in the mass spectral library, the annotation of the secondary metabolome is very difficult. To address the analytical challenges associated with the vast structural diversity and the large numbers of secondary metabolites, particularly those previously unknown structural metabolites, a novel method for the efficient characterization of pathway-associated metabolites was developed. Modification reactions and MS/MS spectral information were collected using the metabolic pathways database and mass spectral library. Screening and annotation of metabolites involved in phenylpropanoid metabolism in maize leaves were used as an example. First, a database of modified groups was established via pathway-associated modifications from open access metabolic pathway database and literature. Here, pathway databases included the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Plant Metabolic Pathways (PlantCyc). A total of 61 modification types were enrolled, including 10 generic and 51 pathway-specific modifications. Modified metabolomes were filtered from untargeted LC-HRMS/MS metabolomics data. Next, MS/MS spectra of the pathway-associated compounds (probe molecules) were collected in the Global Natural Products Social Molecular Networking (GNPS) MS/MS spectral library. The MS/MS of compounds assigned to chemical classes of phenylpropanoids were kept. An MS/MS spectral database of the probe molecules was constructed. It included 2677 spectra of 1542 phenylpropanoid compounds in the positive mode and 814 spectra of 661 phenylpropanoid compounds in the negative mode. Then, an MS/MS molecular network was generated by modified metabolome and probe molecules. The clusters comprising both probe molecules and modified metabolites were kept. To explore more previously unknown structural metabolites, the clusters with one more pathway-specific modified metabolite were retained even though they didn't contain any probe molecule. A total of 392 and 417 phenylpropanoid pathway-related metabolic metabolites were obtained in positive and negative ion modes, respectively. The pathway-associated metabolites were annotated based on the propagation of the molecular network. For the metabolites within the co-cluster, annotations were performed using the probe molecules as the initial seed. The modification group's substructure information was used for network propagation annotation. For the clusters containing only pathway-specific modified metabolites, the annotation is similar to the above process if identified nodes were present within the cluster. Otherwise, de novo annotation was manually executed based on substructure information. Finally, 129 unique metabolites were annotated after integration and removal of redundancy. Ten annotated metabolites were validated using commercially available or synthesized reference compounds. The other annotation results were validated using predicted chemical classes, in silico MS/MS, and predicted retention time. They are mainly involved in the downstream branch of phenylpropanoid pathways, including the flavonoid pathway (8 flavonoids, 19 flavonoid O-glycosides, 32 flavonoid C-glycosides), the hydroxycinnamic acid pathway (31 hydroxycinnamic acids and derivatives), and the lignan pathway (22 neo-lignans/lignan/lignan glycosides). All the annotated structures were searched against the PubChem and SciFinder databases. Among them, 26 metabolites were previously unreported in both the databases. In this study, the pathway-associated metabolites could be quickly discovered and annotated by the integration of probe molecules and modified metabolome. It provides a method for the in-depth study of the phenylpropanoid pathway.

High-throughput single cell metabolomics and cellular heterogeneity exploration by inertial microfluidics coupled with pulsed electric field-induced electrospray ionization-high resolution mass spectrometry.

Single cell metabolomics can obtain the metabolic profiles of individual cells and reveal cellular heterogeneity. However, high-throughput single-cell mass spectrometry (MS) analysis under physiological conditions remains a great challenge due to the presence of complex matrix and extremely small cell volumes. Herein, a serpentine channel microfluidic device which was designed to achieve continuous cell separation and inertial focusing, was coupled with a pulsed electric field-induced electrospray ionization-high resolution MS (PEF-ESI-HRMS) to achieve high-throughput single cell analysis. The pulsed square wave electric field was applied to realize on-line cell disruption and induce electrospray ionization. Single cells were analyzed under near-physiological conditions at a throughput of up to 80 cells min-1. More than 900 features were detected and approximately 120 metabolites were tentatively identified from a single cell. Further, by continually analyzing more than 3000 MCF7 and HepG2 cells, discrimination of different cancer cells based on their individual metabolic profiles was achieved by using the principal component analysis. The PEF-ESI-HRMS method was also applied for the analysis of single yeast cells, and more than 40 metabolites were annotated. This method is versatile and has good robustness, which is promising for high-throughput single cell metabolomics analysis.

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.

Nontargeted screening method for veterinary drugs and their metabolites based on fragmentation characteristics from ultrahigh-performance liquid chromatography-high-resolution mass spectrometry.

Nontargeted screening of both veterinary drugs and their metabolites is important for comprehensive safety evaluation of animal-derived foods. In this study, a novel nontargeted screening strategy was developed for veterinary drugs and their metabolites based on fragmentation characteristics from ultrahigh-performance liquid chromatography-high-resolution mass spectrometry. First, an in-house database of mass spectra including 3,710 veterinary drugs and their metabolites was constructed. Second, fragmentation characteristics of parent drugs and their metabolites in mass spectrometry were investigated and summarized. Then, a nontargeted screening procedure was established based on fragmentation characteristics to screen unknown parent drugs and their metabolites. Finally, the strategy was applied to 33 egg samples, and four veterinary drugs and three drug metabolites were determined and identified. These results showed that the developed strategy can realize suspect and nontargeted screening of veterinary drugs and their metabolites, and can also be applied to other animal-derived foods.

Metabolome-wide association study of serum exogenous chemical residues in a cohort with 5 major chronic diseases.

BACKGROUND: Chronic diseases have become main killers affecting the health of human, and environmental pollution is a major health risk factor that cannot be ignored. It has been reported that exogenous chemical residues including pesticides, herbicides, fungicides, veterinary drugs and persistent organic pollutants are associated with chronic diseases. However, the evidence for their relationship is equivocal and the underlying mechanisms are unclear. OBJECTIVES: We aim to investigate the linkages between serum exogenous chemical residues and 5 main chronic diseases including obesity, hyperuricemia, hypertension, diabetes and dyslipidemia, and further reveal the metabolic perturbations of chronic diseases related to exogenous chemical residue exposure, then gain potential mechanism insight at the metabolic level. METHODS: LC-MS-based targeted and nontargeted methods were respectively performed to quantify exogenous chemical residues and acquire metabolic profiling of 496 serum samples from chronic disease patients. Non-parametric test, correlation and regression analyses were carried out to investigate the association between exogenous chemical residues and chronic diseases. Metabolome-wide association study combined with the meeting-in-the-middle strategy and mediation analysis was performed to reveal and explain exposure-related metabolic disturbances and their risk to chronic diseases. RESULTS: In the association analysis of 106 serum exogenous chemical residues and 5 chronic diseases, positive associations of serum perfluoroalkyl substances (PFASs) with hyperuricemia were discovered while other associations were not significant. 240 exposure markers of PFASs and 84 disease markers of hyperuricemia were found, and 47 of them were overlapped and considered as putative effective markers. Serum uric acid, amino acids, cholesterol, carnitines, fatty acids, glycerides, glycerophospholipids, ceramides, and a part of sphingolipids were positively correlated with PFASs and associated with increased risk for hyperuricemia. Creatine, creatinine, glyceryl monooleate, phosphatidylcholine 36:6, phosphatidylethanolamine 40:6, cholesterol and sphingolipid 36:1;2O were significant markers which mediated the associations of the residues with hyperuricemia. CONCLUSIONS: Our study demonstrated a significantly positive association between PFASs exposure and hyperuricemia. The most significant metabolic abnormality was lipid metabolism which not only was positively associated with PFASs, but also increased the risk of hyperuricemia.

Deep Neural Network Pretrained by Weighted Autoencoders and Transfer Learning for Retention Time Prediction of Small Molecules.

Retention time (RT) prediction contributes to identification of small molecules measured by high-performance liquid chromatography coupled with high-resolution mass spectrometry. Deep learning algorithms based on big data can enhance the accuracy of RT prediction. But at different chromatographic conditions, RTs of compounds are different, and the number of compounds with known RTs is small in most cases. Therefore, the transfer of big data is necessary. In this work, a strategy using a deep neural network (DNN) pretrained by weighed autoencoders and transfer learning (DNNpwa-TL) was proposed to efficiently predict RTs of compounds. The loss function in the autoencoders was calculated with features weighted by mutual information. Then, a DNN pretrained by weighted autoencoders (DNNpwa) was produced. For other specific chromatographic methods, the transfer learning model DNNpwa-TLs were built through fine-tuning the DNNpwa with the help of some compounds with known RTs to conduct the RT prediction. With the above strategy, a DNNpwa was first built with the METLIN small molecule retention time data set containing 80 038 small molecule compounds. A median relative error of 3.1% and a mean relative error of 4.9% were achieved. Then, 17 data sets from different chromatographic methods were studied, and the results showed that the performance of DNNpwa-TL was better than those of other deep learning models. Besides, DNNpwa-TL outperformed random forest, gradient boost, least absolute shrinkage and selection operator regression, and DNN for most of the 17 data sets. Therefore, DNNpwa-TL can provide an efficient method to perform RT prediction of small molecule compounds for different chromatographic methods and conditions.

Novel Stable Isotope-Resolved Metabolomics Method for a Small Number of Cells Using Chip-Based Nanoelectrospray Mass Spectrometry.

Stable isotope-resolved metabolomics (SIRM) can provide metabolic conversion information of specific targets; it is a powerful tool for cell metabolism studies. The common analytical platform for SIRM is chromatography-mass spectrometry, which requires a large number of cells and is not suitable for precious rare cell analysis. To study a small number of cells, we established a novel SIRM method using chip-based nanoelectrospray mass spectrometry (MS). 13C-glutamine was taken as an example; the unlabeled and 13C-labeled cells were cultured and extracted in a 96-well plate and then directly injected into MS and analyzed in full scan mode and parallel reaction monitoring (PRM) mode targeting 44 glutamine-derived metabolites and their isotopologues. To define focused metabolite-related MS2 fragments produced in the PRM, a new strategy was proposed including MS2 exact m/z matching, MS2 false positive filtering, and MS2 fragment grouping to remove the interfering MS2 ions. In total, 292 and 349 pairs of paired MS2 ions were obtained in positive and negative ionization modes, respectively. By searching spectra databases, 31 targeted metabolites with their isotopologues were identified and their characteristic product ions were confirmed for MS2 quantification. The relative quantification was achieved by MS2 quantification, which showed better sensitivity and accuracy than common MS1-based quantification. Finally, this method was applied to isocitrate dehydrogenase I-mutated glioma cells for revealing the effects of triptolide on glioma cell metabolism using U-13C-glutamine as a labeling substrate.

Systematic, Modifying Group-Assisted Strategy Expanding Coverage of Metabolite Annotation in Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Studies.

From microbes to human beings, nontargeted metabolic profiling by liquid chromatography (LC)-mass spectrometry (MS) has been commonly used to investigate metabolic alterations. Still, a major challenge is the annotation of metabolites from thousands of detected features. The aim of our research was to go beyond coverage of metabolite annotation in common nontargeted metabolomics studies by an integrated multistep strategy applying data-dependent acquisition (DDA)-based ultrahigh-performance liquid chromatography (UHPLC)-high-resolution mass spectrometry (HRMS) analysis followed by comprehensive neutral loss matches for characteristic metabolite modifications and database searches in a successive manner. Using pooled human urine as a model sample for method establishment, we found 22% of the detected compounds having modifying structures. Major types of metabolite modifications in urine were glucuronidation (33%), sulfation (20%), and acetylation (6%). Among the 383 annotated metabolites, 100 were confirmed by standard compounds and 50 modified metabolites not present in common databases such as human metabolite database (HMDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were structurally elucidated. Practicability was tested by the investigation of urines from pregnant women diagnosed with gestational diabetes mellitus vs healthy controls. Overall, 83 differential metabolites were annotated and 67% of them were modified metabolites including five previously unreported compounds. To conclude, the systematic modifying group-assisted strategy can be taken as a useful tool to extend the number of annotated metabolites in biological and biomedical nontargeted studies.

Strategy for Nontargeted Metabolomic Annotation and Quantitation Using a High-Resolution Spectral-Stitching Nanoelectrospray Direct-Infusion Mass Spectrometry with Data-Independent Acquisition.

Direct-infusion nanoelectrospray ionization high-resolution mass spectrometry (DI-nESI-HRMS) is an alternative approach to chromatography-MS-based techniques for nontargeted metabolomics, offering a high sample throughout. However, its annotation accuracy of analytes is still full of challenges. In this study, we proposed a strategy for the annotation and quantitation of nontargeted metabolomic data using a spectral-stitching DI-nESI-HRMS with data-independent acquisition. The metabolite annotation strategy included the isotopic distribution, MS/MS spectrum similarity, and precursor and product ion correlation as well as matching of the extracted metabolite features along with the targeted metabolite precursors. Two groups of mixed standard solutions containing 40 and 79 metabolites were, respectively, used to establish the metabolite annotation strategy and validate its reliability. The results showed that the detected standards could be well annotated at top three explanations and total qualitative percentages were 100% (40 of 40) for the standard solution and 94.9% (74 of 78) for the standards spiked into the serum matrix. The intensity of the precursor ions was used for quantitation except for isomers, which were quantified by the intensities of the characteristic product ions if available. Finally, the strategy was applied to study serum metabolomics in diabetes, and the results demonstrated that it is promising for a large-scale cohort metabolomic study.

Metabolite Triplet in Serum Improves the Diagnostic Accuracy of Prediabetes and Diabetes Screening.

Large-scale population screenings are not feasible by applying laborious oral glucose tolerance tests, but using fasting blood glucose (FPG) and glycated hemoglobin (HbA1c), a considerable number of diagnoses are missed. A novel marker is urgently needed to improve the diagnostic accuracy of broad-scale diabetes screening in easy-to-collect blood samples. In this study, by applying a novel knowledge-based, multistage discovery and validation strategy, we scaled down from 108 diabetes-associated metabolites to a diagnostic metabolite triplet (Met-T), namely hexose, 2-hydroxybutyric/2-hydroxyisobutyric acid, and phenylalanine. Met-T showed in two independent cohorts, each comprising healthy controls, prediabetic, and diabetic individuals, distinctly higher diagnostic sensitivities for diabetes screening than FPG alone (>79.6 vs <68%). Missed diagnoses decreased from >32% using fasting plasma glucose down to <20.4%. Combining Met-T and fasting plasma glucose further improved the diagnostic accuracy. Additionally, a positive association of Met-T with future diabetes risk was found (odds ratio: 1.41; p = 1.03 × 10-6). The results reveal that missed prediabetes and diabetes diagnoses can be markedly reduced by applying Met-T alone or in combination with FPG and it opens perspectives for higher diagnostic accuracy in broad-scale diabetes-screening approaches using easy to collect sample materials.