Chemical Discovery in the Era of Metabolomics

Nontargeted screening strategy of chemical residues in animal-derived foods based on endogenous metabolite global annotation and interquartile range filtering by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry

Endogenous components and other experimental background information can interfere with the efficiency of nontargeted screening for unknown chemical residues in complex food matrices. In this study, a new nontargeted screening strategy of chemical residues in animal-derived foods was developed based on endogenous metabolite global annotation and interquartile range (IQR) filtering by ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS). First, endogenous metabolite global annotation was employed to eliminate the majority of endogenous components and their related ion peaks in food from the UHPLC-HRMS data. Second, IQR filtering was evaluated across multiple intercept values under different sample sizes and spiking concentrations based on 72 standards of chemical residues. The evaluation results indicated that chemical residues could be rapidly found after background interference was effectively removed when the sum of the third quartile and 20 times the interquartile range was used as intercept values. By integrating endogenous metabolite global annotation information with IQR filtering, this strategy achieved a total background interference filtering performance of >95 %. Finally, this strategy was applied to screen 13 meat samples. Suspected chemical residues in meat samples were successfully found out and identified by endogenous metabolite global annotation and IQR filtering, and the overall capability for filtering background interference reached 93.1 %. The strategy effectively improved the efficiency of nontargeted screening for unknown chemical residues in animal-derived foods, which can provide a powerful tool for routine risk assessment of food safety.

Recent advance in macrolactams: Structure, bioactivity, and biosynthesis

Macrolactams have garnered significant attention in recent years due to their diverse structures and remarkable biological activities. Despite the increasing number of new members being reported, a systematic discussion of recent advancements in this family is still lacking, particularly in areas such as structure-activity relationship and newly identified biosynthetic pathways that deviate from the traditional collinear rule. To address this gap, we compiled 105 macrolactams reported between 2004 and 2023, produced by microbial strains isolated from diverse environments, including marine sediments, soil, plants, and animals. This review not only highlights the sources, structures, and biological activities of these macrolactams but also delves into 17 known biosynthetic pathways. We provide an in-depth analysis of the associated biosynthetic gene clusters, the mechanisms of key enzymes, and their roles in the biosynthesis process. By offering these insights, this review serves as a valuable reference for the discovery of novel macrolactams and their sustainable production using synthetic biology approaches in the future.

A Spatial Metabolomics Annotation Workflow Leveraging Cyclic Ion Mobility and Machine Learning-Predicted Collision Cross Sections

In nontargeted spatial metabolomics, accurate annotation is crucial for understanding metabolites' biological roles and spatial patterns. MS2 mass spectrometry imaging (MSI) coverage is often incomplete or nonexistent, resulting in many unknown features that represent an untapped source of biological information. Ion mobility-derived collision cross sections (CCS) have been leveraged as valuable descriptors for confirming putative metabolite annotations, distinguishing isomers, and aiding in unknown structural elucidation. In this study, desorption electrospray ionization cyclic ion mobility mass spectrometry imaging (DESI-cIM-MSI) data from human renal cell carcinoma (RCC) tissues is used as a testbed to explore the extent to which CCS measurements enhance MSI lipid annotation confidence when combined with machine learning CCS predictions and SIRIUS analysis of MS2 data. Multipass IM experiments yielded excellent CCS accuracy (<0.4%) relative to database values for differential lipids found in RCC tissues, improving the filtering threshold used in previous CCS-based annotation workflows. High-accuracy multipass CCS measurements enabled the correct annotation of isobaric lipid database matches, even in the absence of MS2 data. Additionally, MS2 data from differential RCC features were uploaded to SIRIUS, and the predicted CCS values for SIRIUS candidates were compared to experimental CCS data to filter out unlikely candidates. Finally, CCS measurements contributed to the annotation of two spatially correlated unknown features, differential between tumor and control kidney tissues. Both features were assigned to rocuronium, a surgical muscle relaxant that had not been previously reported in MSI studies. Overall, these results underscore the potential of high-accuracy CCS values to enhance metabolite annotations in MSI-based spatial metabolomics.

Exploring the Mechanism of Directional Production of Characteristic Compounds by Interaction between Fish Myofibrillar Protein and Key Lipids with Different Saturations

Lipid saturation and protein oxidation influence the types and amounts of volatile and nonvolatile compounds by affecting lipid oxidation. This study examined three reaction modes: systems containing only fatty acids with three different saturations, only myofibrillar proteins (MPs), or their combinations. Sensomics (including volatilomics) and metabolomics were used to analyze these systems. Results showed that MPs enhanced saturated fatty acid oxidation, suppressed unsaturated fatty acid double-bond oxidation, and adsorbed volatiles from unsaturated fatty acid thermal oxidation. MPs promoted secondary oxidation to generate short-chain volatiles. The volatile products derived from stearic acid (SA) were mainly dominated by C6-C8 compounds, while oleic acid (OA) was mainly dominated by C7-C10 compounds, and linoleic acid (LA) was mainly dominated by C5-C8 compounds. The concentrations of volatile compounds followed the order: LA (14,061.66 ng/g) > OA (11,943.52 ng/g) > MPs-LA (2,224.45 ng/g) > MPs-OA (154.63 ng/g) > MPs-SA (28.00 ng/g) > SA (24.11 ng/g) > MPs (0.73 ng/g). Through multivariate statistical analysis, hexamethylenetetramine was identified as a differential metabolite specifically associated with the reaction between SA and MPs. This compound may act as an intermediate, potentially playing a regulatory role in flavor formation. Additionally, α-methyl-dl-phenylalanine and triethylene glycol were found to be differential metabolites in the reaction between LA and MPs. This study systematically explained how characteristic volatile and nonvolatile compounds were formed and transformed. The research findings provided important theoretical support for the flavor regulation of fish products, promoting innovative development in the food industry regarding flavor quality enhancement.

Establishment of a novel large-scale targeted metabolomics method based on NFSWI-DDA mode utilizing HRMS and TQ-MS

Metabolites identification is the major bottleneck in untargeted LC-MS metabolomics, primarily due to the limited availability of MS2 information for most detected metabolites in data dependent acquisition (DDA) mode. To solve this problem, we have integrated the iterative, interval, and segmented window acquisition concepts to develop an innovative non-fixed segmented window interval data dependency acquisition (NFSWI-DDA) mode, which achieves comparable MS2 coverage to data independent acquisition (DIA) mode. This acquisition strategy harnesses the strengths of both DDA and DIA, which could provide extensive coverage and excellent reproducibility of MS2 spectra. Furthermore, utilizing the NFSWI-DDA data, we successfully acquired and identified a large-scale of multiple reaction monitoring (MRM) ion pairs, and transitioned them from high-resolution mass spectrometry (HRMS) to triple quadrupole mass spectrometry (TQ-MS). At last, a large-scale targeted metabolomics method was established practically. This method enables targeted analysis of 475 endogenous metabolites encompassing amino acids, nucleotides, bile acids, fatty acids, and carnitines, which could cover 9 major metabolic pathways as well as 65 secondary metabolic pathways. The established targeted method allows for semi-quantitative assessment of 475 metabolites while enabling quantitative analysis of 327 specific metabolites in biological samples. The method demonstrates immense potential in the detection of various biological samples, offering robust technical support and generating extensive data to advance applications in precision medicine and life sciences.

HeuSMA: A Multigradient LC-MS Strategy for Improving Peak Identification in Untargeted Metabolomics

Metabolomics, which involves the comprehensive analysis of small molecules within biological systems, plays a crucial role in elucidating the biochemical underpinnings of physiological processes and disease conditions. However, current coverage of the metabolome remains limited. In this study, we present a heuristic strategy for untargeted metabolomics analysis (HeuSMA) based on multiple chromatographic gradients to enhance the metabolome coverage in untargeted metabolomics. This strategy involves performing LC-MS analysis under multiple gradient conditions on a given sample (e.g., a pooled sample or a quality control sample) to obtain a comprehensive metabolomics data set, followed by constructing a heuristic peak list using a retention index system. Guided by this list, heuristic peak picking in quantitative metabolomics data is achieved. The benchmarking and validation results demonstrate that HeuSMA outperforms existing tools (such as MS-DIAL and MZmine) in terms of metabolite coverage and peak identification accuracy. Additionally, HeuSMA improves the accessibility of MS/MS data, thereby facilitating the metabolite annotation. The effectiveness of the HeuSMA strategy was further demonstrated through its application in serum metabolomics analysis of human hepatocellular carcinoma (HCC). To facilitate the adoption of the HeuSMA strategy, we also developed two user-friendly graphical interface software solutions (HPLG and HP), which automate the analysis process, enabling researchers to efficiently manage data and derive meaningful conclusions (https://github.com/Lacterd/HeuSMA).

Changes in aqueous humor cytokines and metabolomics in contralateral eye after unilateral cataract surgery

BACKGROUND

For patients with bilateral age-related cataracts, sequential phacoemulsification and intraocular lens implantation is a common treatment. However, it remains unclear whether surgery on the first eye has an impact on the second eye, as current research results are inconsistent. This study will explore whether surgery on one eye affects the non-operated eye through aqueous humor cytokines and metabolomic analyses in the second eye.

METHODS

A rabbit model of unilateral phacoemulsification and intraocular lens implantation was established. The experimental group consisted of 15 rabbits undergoing this procedure. Postoperatively, rabbits were divided into five subgroups (three rabbits per subgroup), and aqueous humor was collected from both the operated and non-operated eyes at 1 day, 3 days, 1 week, 2 weeks, and 3 weeks after surgery. Additionally, 5 rabbits were selected as a control group, from which aqueous humor was extracted. Levels of IL-1a, IL-1β, IL-2, IL-4, IL-6, IL-8, IFN-γ, TNF-α, MCP-1, and VEGF in the aqueous humor were compared. In the clinical study, preoperative aqueous humor samples were collected from 22 patients undergoing bilateral phacoemulsification and intraocular lens implantation. Among them, 11 patients were tested for the aforementioned 10 cytokines, while the other 11 patients underwent untargeted metabolomics research.

RESULTS

In the animal experiment, levels of all 10 cytokines in the operated eyes were significantly higher compared to both the control and non-operated eyes groups (P < 0.05). In the non-operated eyes, IL-1β and IL-2 levels were also elevated compared to the control (P < 0.05); however, no statistically significant differences were observed between the non-operated eyes and the control group at postoperative time points of 1 day, 3 days, 1 week, 2 weeks, and 3 weeks. In the clinical study, no significant differences were found in cytokine levels between the two eyes. In the untargeted metabolomics analysis, 354 metabolites showed differential expression, 280 were upregulated and 74 were downregulated. Notably, Adenine and 2-Aminopurine were significantly downregulated, highlighting Purine metabolism as the most impacted pathway.

CONCLUSIONS

Animal experiments showed a significant increase in IL-1β and IL-2 levels in the non-operated eyes postoperatively, reflecting systemic and local inflammatory responses. In clinical experiments, although no significant changes in cytokines were observed in the aqueous humor of both eyes, differential expression of metabolites indicated metabolic adjustments in the non-operated eye following surgery on the first eye. These findings reveal that unilateral cataract surgery may affect the stability of the intraocular environment in the contralateral eye, suggesting that in staged bilateral surgeries, potential metabolic changes in the non-operated eye and their clinical significance should be considered. This result provides important reference value for optimizing postoperative management strategies, reducing complications, and determining the timing for bilateral surgeries, warranting further investigation.

Non-targeted metabolomic study in plasma in rats with post-traumatic osteoarthritis model

PURPOSE

This study aimed to examine the differential expression profiles of plasma metabolites in rat models of post-traumatic osteoarthritis (PTOA) and elucidate the roles of metabolites and their pathways in the progression of PTOA using bioinformatics analysis.

METHOD

Plasma samples were collected from 24 SD female rats to model PTOA, and metabolomic assays were conducted. The samples were divided into three groups: the surgically induced mild PTOA group (Group A: 3 weeks postoperative using the modified Hulth model; age 2 months), the surgically induced severe PTOA group (Group B: 5 weeks postoperative using the modified Hulth model; age 2 months), and the normal control group (Group C: healthy rats aged 2 months). Metabolites were structurally identified by comparing the retention times, molecular masses, secondary fragmentation spectra, collision energies, and other metabolite data with a database (provided by Shanghai Applied Protein Technology Co., Ltd.). Target prediction and pathway analysis were subsequently performed using bioinformatics analysis.

RESULTS

The experiment revealed that in the mild PTOA group, levels of Alpha-ketoglutarate, Isocitric acid, Dichloroacetate, and other metabolites increased significantly compared with the normal group, whereas Linolenic acid, Lactose, and others decreased significantly. These findings suggest that these metabolites can serve as biomarkers for the diagnosis of early PTOA. In the severe PTOA group, Diosgenin, Indoleacrylic acid, Alpha-ketoglutarate, Isocitric acid, and others were elevated and may also be used as biomarkers for PTOA diagnosis. Adrenosterone, (+)-chlorpheniramine, and Phenanthridine levels were higher in the severe PTOA group compared to the mild PTOA group, while Menadione, Adenosine 5'-monophosphate, and Arg-Gly-Asp levels were lower.

CONCLUSIONS

Taurocholate, indoleacrylic acid, alpha-ketoglutarate, and isocitric acid may serve as biomarkers for PTOA joint injury in rats. Menadione, adenosine 5'-monophosphate, and Arg-Gly-Asp exhibited differential expression between severe and mild PTOA groups in rats, potentially reflecting the injury's severity. Further investigation into these molecules in human tissues is warranted to ascertain their utility as biomarkers for PTOA in humans.

Metabolomic characteristics and related pathways in patients with different severity of COVID-19: a systematic review and meta-analysis

Background

Despite advances in metabolomic research on COVID-19, existing studies have small sample sizes and few have comprehensively described the metabolic characteristics of patients with COVID-19 at each stage. In this systematic review, we aimed to summarise the similarities and differences of biomarkers in patients with COVID-19 of different severity and describe their metabolic characteristics at different stages.

Methods

We retrieved studies from PubMed, Embase, Web of Science, and the Cochrane Library published by October 2022. We performed a meta-analysis on untargeted and targeted metabolomics research data, using the ratio of means as the effect size. We compared changes in metabolite levels between patients with varying severity and controls and investigated sources of heterogeneity through subgroup analyses and meta-regression analysis.

Results

We included 22 cohorts from 21 studies, comprising 2421 participants, including COVID-19 patients of varying severity and healthy controls. We conducted meta-analysis and heterogeneity analysis on the 1058 metabolites included in the study. The results indicated that, compared to the healthy control group, 23 biomarkers were associated with mild cases (P < 0.05), 3 biomarkers with moderate cases (P < 0.05), and 37 biomarkers with severe cases (P < 0.05). Pathway enrichment analysis revealed significant disturbances in amino acid metabolism, aminoacyl-tRNA biosynthesis, primary bile acid biosynthesis, pantothenate and CoA biosynthesis, the tricarboxylic acid cycle, taurine and hypotaurine metabolism, and nitrogen metabolism in patients with mild, moderate, and severe disease. Additionally, we found that each severity stage exhibited unique metabolic patterns (all P < 0.05) and that the degree of metabolic dysregulation progressively worsened with increasing disease severity (P < 0.05).

Conclusions

The results of our meta-analysis indicate the similarities and differences of biomarkers and metabolic characteristics of patients with different severity in COVID-19, thereby providing new pathways for the study of pathogenesis, the development precise treatment, and the formulation of comprehensive strategies.

Registration

PROSPERO: CRD42022369937.

Challenges in Metabolite Biomarkers as Avenues of Diagnosis and Prognosis of Cancer

Given the evolutionary nature of tumor complexities and heterogeneity, the early diagnosis of cancer encounters various challenges. Complexities at the level of metabolite reprogramming are compelling in the background of invasiveness, metastasis, drug- and radiation-induced metabolic alterations, immunotherapy-influenced changes, and pro-tumor niche including microbiome. Therefore, it is crucial to examine both current and future obstacles associated with early cancer detection specifically in the context of tumor metabolite biomarkers at preclinical and clinical levels. In conclusion, the significance of tumor metabolite biomarkers must be aligned with a comprehensive approach to achbieve diagnosis and prognosis of cancer patients by securing solutions to formidable challenges.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors

INTRODUCTION

Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.

OBJECTIVES

This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.

METHODS

An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR).

RESULTS

Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques.

CONCLUSION

This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix

INTRODUCTION

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis.

OBJECTIVE

This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics.

METHODOLOGY

The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation.

RESULTS

The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis.

CONCLUSION

The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Predicting Collision Cross-Section Values for Small Molecules through Chemical Class-Based Multimodal Graph Attention Network

Libraries of collision cross-section (CCS) values have the potential to facilitate compound identification in metabolomics. Although computational methods provide an opportunity to increase library size rapidly, accurate prediction of CCS values remains challenging due to the structural diversity of small molecules. Here, we developed a machine learning (ML) model that integrates graph attention networks and multimodal molecular representations to predict CCS values on the basis of chemical class. Our approach, referred to as MGAT-CCS, had superior performance in comparison to other ML models in CCS prediction. MGAT-CCS achieved a median relative error of 0.47%/1.14% (positive/negative mode) and 1.40%/1.63% (positive/negative mode) for lipids and metabolites, respectively. When MGAT-CCS was applied to real-world metabolomics data, it reduced the number of false metabolite candidates by roughly 25% across multiple sample types ranging from plasma and urine to cells. To facilitate its application, we developed a user-friendly stand-alone web server for MGAT-CCS that is freely available at https://mgat-ccs-web.onrender.com. This work represents a step forward in predicting CCS values and can potentially facilitate the identification of small molecules when using ion mobility spectrometry coupled with mass spectrometry.

Metabolomics applications for plant-based foods origin tracing, cultivars identification and processing: Feasibility and future aspects

Metabolomics, the systematic study of metabolites, is dedicated to a comprehensive analysis of all aspects of plant-based food research and plays a pivotal role in the nutritional composition and quality control of plant-based foods. The diverse chemical compositions of plant-based foods lead to variations in sensory characteristics and nutritional value. This review explores the application of the metabolomics method to plant-based food origin tracing, cultivar identification, and processing methods. It also addresses the challenges encountered and outlines future directions. Typically, when combined with other omics or techniques, synergistic and complementary information is uncovered, enhancing the classification and prediction capabilities of models. Future research should aim to evaluate all factors affecting food quality comprehensively, and this necessitates advanced research into influence mechanisms, metabolic pathways, and gene expression.

A metabolomics pipeline highlights microbial metabolism in bloodstream infections

The growth of antimicrobial resistance (AMR) highlights an urgent need to identify bacterial pathogenic functions that may be targets for clinical intervention. Although severe infections profoundly alter host metabolism, prior studies have largely ignored microbial metabolism in this context. Here, we describe an iterative, comparative metabolomics pipeline to uncover microbial metabolic features in the complex setting of a host and apply it to investigate gram-negative bloodstream infection (BSI) in patients. We find elevated levels of bacterially derived acetylated polyamines during BSI and discover the enzyme responsible for their production (SpeG). Blocking SpeG activity reduces bacterial proliferation and slows pathogenesis. Reduction of SpeG activity also enhances bacterial membrane permeability and increases intracellular antibiotic accumulation, allowing us to overcome AMR in culture and in vivo. This study highlights how tools to study pathogen metabolism in the natural context of infection can reveal and prioritize therapeutic strategies for addressing challenging infections.

Discovery and Identification of Three Homocysteine Metabolites by Chemical Derivatization and Mass Spectrometry Fragmentation

Discovery and identification of a new endogenous metabolite are typically hindered by requirements of large sample volumes and multistage purifications to guide synthesis of the standard. Presented here is a metabolomics platform that uses chemical tagging and tandem mass spectrometry to determine structure, direct synthesis, and confirm identity. Three new homocysteine metabolites are reported: N-succinyl homocysteine, 2-methyl-1,3-thiazinane-4-carboxylic acid (MTCA), and homolanthinone.

Protocol for identifying metabolite biomarkers in patient uterine fluid for early ovarian cancer detection

High mortality of ovarian cancer (OC) is primarily attributed to the lack of effective early detection methods. Uterine fluid, pooling molecules from neighboring ovaries, presents an organ-specific advantage over conventional blood samples. Here, we present a protocol for identifying metabolite biomarkers in uterine fluid for early OC detection. We describe steps for uterine fluid collection from patients, metabolite extraction, metabolomics experiments, and candidate metabolite biomarker screening. This standardized workflow holds the potential to achieve early OC diagnosis in clinical practice. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Proposal and confirmation of N-(2-carboxyethyl)proline as a human endogenous metabolite

RATIONALE

Malondialdehyde, one of the peroxidation products of polyunsaturated fatty acids, has been widely reported as an oxidative stress biomarker in many diseases. However, malondialdehyde is inherently unstable in biological matrices, which renders its measurement unreliable with all the reported analytical methods. To find an alternative oxidative stress biomarker, we envisioned that N-(2-carboxyethyl)proline, a modified conjugate of malondialdehyde and proline, could be a stable candidate for this purpose.

METHODS

The proposed compound was chemically synthesized, and liquid chromatography-mass spectrometry methods were developed and used to search for the compound in human biological samples.

RESULTS

An endogenous metabolite in human feces and urine samples was found to match the synthetic N-(2-carboxyethyl)proline by chromatographic retention and the fragmentation pattern of its molecular ion.

CONCLUSION

The results confirmed that N-(2-carboxyethyl)proline was a new metabolite in human feces and urine samples. In addition, our results demonstrated a case of successful identification of true unknown metabolite by knowledge-based hypothesis of possible metabolites followed by experimental confirmation with a synthetic standard.

Application of the Hydrophilic Interaction Liquid Chromatography (HILIC-MS) Novel Protocol to Study the Metabolic Heterogeneity of Glioblastoma Cells

Glioblastoma is a highly malignant brain tumor consisting of a heterogeneous cellular population. The transformed metabolism of glioblastoma cells supports their growth and division on the background of their milieu. One might hypothesize that the transformed metabolism of a primary glioblastoma could be well adapted to limitations in the variety and number of substrates imported into the brain parenchyma and present it their microenvironment. Additionally, the phenotypic heterogeneity of cancer cells could promote the variations among their metabolic capabilities regarding the utilization of available substrates and release of metabolic intermediates. With the aim to identify the putative metabolic footprint of different types of glioblastoma cells, we exploited the possibility for separation of polar and ionic molecules present in culture media or cell lysates by hydrophilic interaction liquid chromatography (HILIC). The mass spectrometry (MS) was then used to identify and quantify the eluted compounds. The introduced method allows the detection and quantification of more than 150 polar and ionic metabolites in a single run, which may be present either in culture media or cell lysates and provide data for polaromic studies within metabolomics. The method was applied to analyze the culture media and cell lysates derived from two types of glioblastoma cells, T98G and U118. The analysis revealed that even both types of glioblastoma cells share several common metabolic aspects, and they also exhibit differences in their metabolic capability. This finding agrees with the hypothesis about metabolic heterogeneity of glioblastoma cells. Furthermore, the combination of both analytical methods, HILIC-MS, provides a valuable tool for metabolomic studies based on the simultaneous identification and quantification of a wide range of polar and ionic metabolites-polaromics.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls

AIMS

Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end-stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD.

MATERIALS AND METHODS

We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta-analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta-regression analysis.

RESULTS

The 34 clinical-based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta-analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered.

CONCLUSIONS

We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.

HILIC-IM-MS for Simultaneous Lipid and Metabolite Profiling of Bacteria

Although MALDI-ToF platforms for microbial identifications have found great success in clinical microbiology, the sole use of protein fingerprints for the discrimination of closely related species, strain-level identifications, and detection of antimicrobial resistance remains a challenge for the technology. Several alternative mass spectrometry-based methods have been proposed to address the shortcomings of the protein-centric approach, including MALDI-ToF methods for fatty acid/lipid profiling and LC-MS profiling of metabolites. However, the molecular diversity of microbial pathogens suggests that no single "ome" will be sufficient for the accurate and sensitive identification of strain- and susceptibility-level profiling of bacteria. Here, we describe the development of an alternative approach to microorganism profiling that relies upon both metabolites and lipids rather than a single class of biomolecule. Single-phase extractions based on butanol, acetonitrile, and water (the BAW method) were evaluated for the recovery of lipids and metabolites from Gram-positive and -negative microorganisms. We found that BAW extraction solutions containing 45% butanol provided optimal recovery of both molecular classes in a single extraction. The single-phase extraction method was coupled to hydrophilic interaction liquid chromatography (HILIC) and ion mobility-mass spectrometry (IM-MS) to resolve similar-mass metabolites and lipids in three dimensions and provide multiple points of evidence for feature annotation in the absence of tandem mass spectrometry. We demonstrate that the combined use of metabolites and lipids can be used to differentiate microorganisms to the species- and strain-level for four of the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Acinetobacter baumannii, and Pseudomonas aeruginosa) using data from a single ionization mode. These results present promising, early stage evidence for the use of multiomic signatures for the identification of microorganisms by liquid chromatography, ion mobility, and mass spectrometry that, upon further development, may improve upon the level of identification provided by current methods.

High-resolution mass spectrometry-based non-targeted metabolomics reveals toxicity of naphthalene on tall fescue and intrinsic molecular mechanisms

Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous at relatively high concentrations by atmospheric deposition, and they are threatening to the environment. In this study, the toxicity of naphthalene on tall fescue and its potential responding mechanism was first studied by integrating approaches. Tall fescue seedlings were exposed to 0, 20, and 100 mg L-1 naphthalene in a hydroponic environment for 9 days, and toxic effects were observed by the studies of general physiological studies, chlorophyll fluorescence, and root morphology. Additionally, Ultra Performance Liquid Chromatography - Electrospray Ionization - High-Resolution Mass Spectrometry (UPLC-ESI-HRMS) was used to depict metabolic profiles of tall fescue under different exposure durations of naphthalene, and the intrinsic molecular mechanism of tall fescue resistance to abiotic stresses. Tall fescue shoots were more sensitive to the toxicity of naphthalene than roots. Low-level exposure to naphthalene inhibited the electron transport from the oxygen-evolving complex (OEC) to D1 protein in tall fescue shoots but induced the growth of roots. Naphthalene induced metabolic change of tall fescue roots in 12 h, and tall fescue roots maintained the level of sphingolipids after long-term exposure to naphthalene, which may play important roles in plant resistance to abiotic stresses.

HDPairFinder: A data processing platform for hydrogen/deuterium isotopic labeling-based nontargeted analysis of trace-level amino-containing chemicals in environmental water

The combination of hydrogen/deuterium (H/D) formaldehyde-based isotopic methyl labeling with solid-phase extraction and high-performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS) is a powerful analytical solution for nontargeted analysis of trace-level amino-containing chemicals in water samples. Given the huge amount of chemical information generated in HPLC-HRMS analysis, identifying all possible H/D-labeled amino chemicals presents a significant challenge in data processing. To address this, we designed a streamlined data processing pipeline that can automatically extract H/D-labeled amino chemicals from the raw HPLC-HRMS data with high accuracy and efficiency. First, we developed a cross-correlation algorithm to correct the retention time shift resulting from deuterium isotopic effects, which enables reliable pairing of H- and D-labeled peaks. Second, we implemented several bioinformatic solutions to remove false chemical features generated by in-source fragmentation, salt adduction, and natural 13C isotopes. Third, we used a data mining strategy to construct the AMINES library that consists of over 38,000 structure-disjointed primary and secondary amines to facilitate putative compound annotation. Finally, we integrated these modules into a freely available R program, HDPairFinder.R. The rationale of each module was justified and its performance tested using experimental H/D-labeled chemical standards and authentic water samples. We further demonstrated the application of HDPairFinder to effectively extract N-containing contaminants, thus enabling the monitoring of changes of primary and secondary N-compounds in authentic water samples. HDPairFinder is a reliable bioinformatic tool for rapid processing of H/D isotopic methyl labeling-based nontargeted analysis of water samples, and will facilitate a better understanding of N-containing chemical compounds in water.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Fish Skin Grafts Affect Adenosine and Methionine Metabolism during Burn Wound Healing

Burn wound healing is a complex process orchestrated through successive biochemical events that span from weeks to months depending on the depth of the wound. Here, we report an untargeted metabolomics discovery approach to capture metabolic changes during the healing of deep partial-thickness (DPT) and full-thickness (FT) burn wounds in a porcine burn wound model. The metabolic changes during healing could be described with six and seven distinct metabolic trajectories for DPT and FT wounds, respectively. Arginine and histidine metabolism were the most affected metabolic pathways during healing, irrespective of burn depth. Metabolic proxies for oxidative stress were different in the wound types, reaching maximum levels at day 14 in DPT burns but at day 7 in FT burns. We examined how acellular fish skin graft (AFSG) influences the wound metabolome compared to other standard-or-care burn wound treatments. We identified changes in metabolites within the methionine salvage pathway, specifically in DPT burn wounds that is novel to the understanding of the wound healing process. Furthermore, we found that AFSGs boost glutamate and adenosine in wounds that is of relevance given the importance of purinergic signaling in regulating oxidative stress and wound healing. Collectively, these results serve to define biomarkers of burn wound healing. These results conclusively contribute to the understanding of the multifactorial mechanism of the action of AFSG that has traditionally been attributed to its structural properties and omega-3 fatty acid content.

Comprehensive Profiling of Amine-Containing Metabolite Isomers with Chiral Phosphorus Reagents

Metabolite isomers play diverse and crucial roles in various metabolic processes. However, in untargeted metabolomics analysis, it remains a great challenge to distinguish between the constitutional isomers and enantiomers of amine-containing metabolites due to their similar chemical structures and physicochemical properties. In this work, the triplex stable isotope N-phosphoryl amino acids labeling (SIPAL) is developed to identify and relatively quantify the amine-containing metabolites and their isomers by using chiral phosphorus reagents coupled with high-resolution tandem mass spectroscopy. The constitutional isomers could be effectively distinguished with stereo isomers by using the diagnosis ions in MS/MS spectra. The in-house software MS-Isomerism has been parallelly developed for high-throughput screening and quantification. The proposed strategy enables the unbiased detection and relative quantification of isomers of amine-containing metabolites. Based on the characteristic triplet peaks with SIPAL tags, a total of 854 feature peaks with 154 isomer groups are successfully recognized as amine-containing metabolites in liver cells, in which 37 amine-containing metabolites, including amino acids, polyamines, and small peptides, are found to be significantly different between liver cancer cells and normal cells. Notably, it is the first time to identify S-acetyl-glutathione as an endogenous metabolite in liver cells. The SIPAL strategy could provide spectacular insight into the chemical structures and biological functions of the fascinating amine-containing metabolite isomers. The feasibility of SIPAL in isomeric metabolomics analysis may reach a deeper understanding of the mirror-chemistry in life and further advance the discovery of novel biomarkers for disease diagnosis.

Mass Spectrometry Analysis for Clinical Applications: A Review

Mass spectrometry (MS) has become an attractive analytical method in clinical analysis due to its comprehensive advantages of high sensitivity, high specificity and high throughput. Separation techniques coupled MS detection (e.g., LC-MS/MS) have shown unique advantages over immunoassay and have developed as golden criterion for many clinical applications. This review summarizes the characteristics and applications of MS, and emphasizes the high efficiency of MS in clinical research. In addition, this review also put forward further prospects for the future of mass spectrometry technology, including the introduction of miniature MS instruments, point-of-care detection and high-throughput analysis, to achieve better development of MS technology in various fields of clinical application. Moreover, as ambient ionization mass spectrometry (AIMS) requires little or no sample pretreatment and improves the flux of MS, this review also summarizes its potential applications in clinic.

A large-scale, targeted metabolomics method for the analysis and quantification of metabolites in human plasma via liquid chromatography-mass spectrometry

Metabolomics is the study of small molecules, primarily metabolites, that are produced during metabolic processes. Analysis of the composition of an organism's metabolome can yield useful information about an individual's health status at any given time. In recent years, the development of large-scale, targeted metabolomic methods has allowed for the analysis of biological samples using analytical techniques such as LC-MS/MS. This paper presents a large-scale metabolomics method for analysis of biological samples, with a focus on quantification of metabolites found in blood plasma. The method comprises a 10-min chromatographic separation using HILIC and RP stationary phases combined with positive and negative electrospray ionization in order to maximize metabolome coverage. Complete analysis of a single sample can be achieved in as little as 40 min using the two columns and dual modes of ionization. With 540 metabolites and the inclusion of over 200 analytical standards, this method is comprehensive and quantitatively robust when compared to current targeted metabolomics methods. This study uses a large-scale evaluation of metabolite recovery from plasma that enables absolute quantification of metabolites by correcting for analyte loss throughout processes such as extraction, handling, or storage. In addition, the method was applied to plasma collected from adjuvant breast cancer patients to confirm the suitability of the method to clinical samples.

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.

Quantitative Analytical and Computational Workflow for Large-Scale Targeted Plasma Metabolomics

Quantifying metabolites from various biological samples is necessary for the clinical and biomedical translation of metabolomics research. One of the ongoing challenges in biomedical metabolomics studies is the large-scale quantification of targeted metabolites, mainly due to the complexity of biological sample matrices. Furthermore, in LC-MS analysis, the response of compounds is influenced by their physicochemical properties, chromatographic conditions, eluent composition, sample preparation, type of MS ionization source, and analyzer used. To facilitate large-scale metabolite quantification, we evaluated the relative response factor (RRF) approach combined with an integrated analytical and computational workflow. This approach considers a compound's individual response in LC-MS analysis relative to that of a non-endogenous reference compound to correct matrix effects. We created a quantitative LC-MS library using the Skyline/Panorama web platform for data processing and public sharing of data. In this study, we developed and validated a metabolomics method for over 280 standard metabolites and quantified over 90 metabolites. The RRF quantification was validated and compared with conventional external calibration approaches as well as literature reports. The Skyline software environment was adapted for processing such metabolomics data, and the results are shared as a "quantitative chromatogram library" with the Panorama web application. This new workflow was found to be suitable for large-scale quantification of metabolites in human plasma samples. In conclusion, we report a novel quantitative chromatogram library with a targeted data analysis workflow for biomedical metabolomic applications.

Repurposing degradation pathways for modular metabolite biosynthesis in nematodes

Recent studies have revealed that Caenorhabditis elegans and other nematodes repurpose products from biochemical degradation pathways for the combinatorial assembly of complex modular structures that serve diverse signaling functions. Building blocks from neurotransmitter, amino acid, nucleoside and fatty acid metabolism are attached to scaffolds based on the dideoxyhexose ascarylose or glucose, resulting in hundreds of modular ascarosides and glucosides. Genome-wide association studies have identified carboxylesterases as the key enzymes mediating modular assembly, enabling rapid compound discovery via untargeted metabolomics and suggesting that modular metabolite biosynthesis originates from the 'hijacking' of conserved detoxification mechanisms. Modular metabolites thus represent a distinct biosynthetic strategy for generating structural and functional diversity in nematodes, complementing the primarily polyketide synthase- and nonribosomal peptide synthetase-derived universe of microbial natural products. Although many aspects of modular metabolite biosynthesis and function remain to be elucidated, their identification demonstrates how phenotype-driven compound discovery, untargeted metabolomics and genomic approaches can synergize to facilitate the annotation of metabolic dark matter.

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

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

Biogenic Solution Map Based on the Definition of the Metabolic Correlation Distance between 4-Dimensional Fingerprints

Accurate discrimination and classification of unknown species are the basis to predict its characteristics or applications to make correct decisions. However, for biogenic solutions that are ubiquitous in nature and our daily lives, direct determination of their similarities and disparities by their molecular compositions remains a scientific challenge. Here, we explore a standard and visualizable ontology, termed "biogenic solution map", that organizes multifarious classes of biogenic solutions into a map of hierarchical structures. To build the map, a novel 4-dimensional (4D) fingerprinting method based on data-independent acquisition data sets of untargeted metabolomics is developed, enabling accurate characterization of complex biogenic solutions. A generic parameter of metabolic correlation distance, calculated based on averaged similarities between 4D fingerprints of sample groups, is able to define "species", "genus", and "family" of each solution in the map. With the help of the "biogenic solution map", species of unknown biogenic solutions can be explicitly defined. Simultaneously, intrinsic correlations and subtle variations among biogenic solutions in the map are accurately illustrated. Moreover, it is worth mentioning that samples of the same analyte but prepared by alternative protocols may have significantly different metabolic compositions and could be classified into different "genera".

Systems Immunology Approaches to Metabolism

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

Supercritical Fluid Nanospray Mass Spectrometry: II. Effects on Ionization

Nanospraying supercritical fluids coupled to a mass spectrometer (nSF-MS) using a 90% supercritical fluid CO₂ carrier (sCO₂) has shown an enhanced desolvation compared to traditional liquid eluents. Capillaries of 25, 50, and 75 μm internal diameter (i.d.) with pulled emitter tips provided high MS detection sensitivity. Presented here is an evaluation of the effect of proton affinity, hydrophobicity, and nanoemitter tip size on the nSF-MS signal. This was done using a set of primary, secondary, tertiary, and quaternary amines with butyl, hexyl, octyl, and decyl chains as analytes. Each amine class was analyzed individually to evaluate hydrophobicity and proton affinity effects on signal intensity. The system has shown a mass sensitive detection on a linear dynamic range of 0.1-100 μM. Results indicate that hydrophobicity has a larger effect on the signal response than proton affinity. Nanospraying a mixture of all amine classes using the 75 μm emitter has shown a quaternary amine signal not suppressed by competing analytes. Competing ionization was observed for primary, secondary, and tertiary amines. The 75 and 50 μm emitters demonstrated increased signal with increasing hydrophobicity. Surprisingly, the 25 μm i.d. emitter yielded a signal decrease as the alkyl chain length increased, contrary to conventional understanding. Nanospraying the evaporative fluid in a sub-500 nm emitter likely resulted in differences in the ionization mechanism. Results suggest that 90% sCO₂ with 9.99% methanol and 0.01% formic acid yielded fast desolvation, high ionization efficiency, and low matrix effect, which could benefit complex biological matrix analysis.

Untargeted metabolomics description of propolis's in vitro antibacterial mechanisms against Clostridium perfringens

Propolis is a natural resinous substance that is collected by honeybees (Apis mellifera) with promising antibacterial effects. Here, we examined the antibacterial activity of Chinese propolis against Clostridium perfringens, a bacterial pathogen that threatens food safety and causes intestinal erosion. The inhibitory effects of the ethanolic extract of Chinese propolis (CPE) on human-associated C. perfringens strains were determined by using the circle of inhibition, the minimum inhibitory concentrations, and bactericidal concentrations. CPE also induced morphological elongation, bacterial cell wall damage, and intracellular material leakage in C. perfringens. Untargeted HPLC-qTOF-MS-based metabolomics analysis of the bacterial metabolic compounds revealed that propolis triggered glycerophospholipid metabolism, one carbon pool by folate, and d-glutamine and d-glutamate metabolism alterations in C. perfringens. Finally, caffeic acid phenethyl ester was identified as the key active ingredient in CPE. This study suggested the usage of propolis as an alternative to antibiotics in controlling C. perfringens.

Closing the gap between in vivo and in vitro omics: using QA/QC to strengthen ex vivo NMR metabolomics

Metabolomics aims to achieve a global quantitation of the pool of metabolites within a biological system. Importantly, metabolite concentrations serve as a sensitive marker of both genomic and phenotypic changes in response to both internal and external stimuli. NMR spectroscopy greatly aids in the understanding of both in vitro and in vivo physiological systems and in the identification of diagnostic and therapeutic biomarkers. Accordingly, NMR is widely utilized in metabolomics and fluxomics studies due to its limited requirements for sample preparation and chromatography, its non-destructive and quantitative nature, its utility in the structural elucidation of unknown compounds, and, importantly, its versatility in the analysis of in vitro, in vivo, and ex vivo samples. This review provides an overview of the strengths and limitations of in vitro and in vivo experiments for translational research and discusses how ex vivo studies may overcome these weaknesses to facilitate the extrapolation of in vitro insights to an in vivo system. The application of NMR-based metabolomics to ex vivo samples, tissues, and biofluids can provide essential information that is close to a living system (in vivo) with sensitivity and resolution comparable to those of in vitro studies. The success of this extrapolation process is critically dependent on high-quality and reproducible data. Thus, the incorporation of robust quality assurance and quality control checks into the experimental design and execution of NMR-based metabolomics experiments will ensure the successful extrapolation of ex vivo studies to benefit translational medicine.

Integrated transcriptomic and metabolomic analyses of DNCB-induced atopic dermatitis in mice

AIMS

Atopic dermatitis (AD) is a common chronic inflammatory skin disorder that affects up to 20 % of children and 10 % of adults worldwide; however, the exact molecular mechanisms remain largely unknown.

MATERIALS AND METHODS

In this study, we used integrated transcriptomic and metabolomic analyses to study the potential mechanisms of 1-chloro-2,4-dinitrobenzene (DNCB)-induced AD-like skin lesions.

KEY FINDINGS

We found that DNCB induced AD-like skin lesions, including phenotypical and histomorphological alterations and transcriptional and metabolic alterations in mice. A total of 3413 differentially expressed metabolites were detected between DNCB-induced AD-like mice and healthy controls, which includes metabolites in taurine and hypotaurine metabolism, phenylalanine metabolism, biosynthesis of unsaturated fatty acids, tryptophan metabolism, arachidonic acid metabolism, pantothenate and CoA biosynthesis, pyrimidine metabolism, and glycerophospholipid metabolism pathways. Furthermore, the differentially expressed genes associated (DEGs) with these metabolic pathways were analyzed using RNA sequencing (RNA-seq), and we found that the expression of pyrimidine metabolism-associated genes was significantly increased. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the glycolysis/gluconeogenesis, glucagon signaling pathway and pentose phosphate pathway-associated metabolic genes were dramatically altered.

SIGNIFICANCE

Our results explain the possible mechanism of AD at the gene and metabolite levels and provide potential targets for the development of clinical drugs for AD.

Model-driven data curation pipeline for LC-MS-based untargeted metabolomics

INTRODUCTION

There is still no community consensus regarding strategies for data quality review in liquid chromatography mass spectrometry (LC-MS)-based untargeted metabolomics. Assessing the analytical robustness of data, which is relevant for inter-laboratory comparisons and reproducibility, remains a challenge despite the wide variety of tools available for data processing.

OBJECTIVES

The aim of this study was to provide a model to describe the sources of variation in LC-MS-based untargeted metabolomics measurements, to use it to build a comprehensive curation pipeline, and to provide quality assessment tools for data quality review.

METHODS

Human serum samples (n=392) were analyzed by ultraperformance liquid chromatography coupled to high-resolution mass spectrometry (UPLC-HRMS) using an untargeted metabolomics approach. The pipeline and tools used to process this dataset were implemented as part of the open source, publicly available TidyMS Python-based package.

RESULTS

The model was applied to understand data curation practices used by the metabolomics community. Sources of variation, which are often overlooked in untargeted metabolomic studies, were identified in the analysis. New tools were used to characterize certain types of variations.

CONCLUSION

The developed pipeline allowed confirming data robustness by comparing the experimental results with expected values predicted by the model. New quality control practices were introduced to assess the analytical quality of data.

Systematic analysis of in-source modifications of primary metabolites during flow-injection time-of-flight mass spectrometry

Flow-injection mass spectrometry (FI-MS) enables metabolomics studies with a very high sample-throughput. However, FI-MS is prone to in-source modifications of analytes because samples are directly injected into the electrospray ionization source of a mass spectrometer without prior chromatographic separation. Here, we spiked authentic standards of 160 primary metabolites individually into an Escherichia coli metabolite extract and measured the thus derived 160 spike-in samples by FI-MS. Our results demonstrate that FI-MS can capture a wide range of chemically diverse analytes within 30 s measurement time. However, the data also revealed extensive in-source modifications. Across all 160 spike-in samples, we identified significant increases of 11,013 ion peaks in positive and negative mode combined. To explain these unknown m/z features, we connected them to the m/z feature of the (de-)protonated metabolite using information about mass differences and MS2 spectra. This resulted in networks that explained on average 49 % of all significant features. The networks showed that a single metabolite undergoes compound specific and often sequential in-source modifications like adductions, chemical reactions, and fragmentations. Our results show that FI-MS generates complex MS1 spectra, which leads to an overestimation of significant features, but neutral losses and MS2 spectra explain many of these features.

Development of an Untargeted Metabolomics Strategy to Study the Metabolic Rewiring of Dendritic Cells upon Lipopolysaccharide Activation

Dendritic cells (DCs) are essential immune cells for defense against external pathogens. Upon activation, DCs undergo profound metabolic alterations whose precise nature remains poorly studied at a large scale and is thus far from being fully understood. The goal of the present work was to develop a reliable and accurate untargeted metabolomics workflow to get a deeper insight into the metabolism of DCs when exposed to an infectious agent (lipopolysaccharide, LPS, was used to mimic bacterial infection). As DCs transition rapidly from a non-adherent to an adherent state upon LPS exposure, one of the leading analytical challenges was to implement a single protocol suitable for getting comparable metabolomic snapshots of those two cellular states. Thus, a thoroughly optimized and robust sample preparation method consisting of a one-pot solvent-assisted method for the simultaneous cell lysis/metabolism quenching and metabolite extraction was first implemented to measure intracellular DC metabolites in an unbiased manner. We also placed special emphasis on metabolome coverage and annotation by using a combination of hydrophilic interaction liquid chromatography and reverse phase columns coupled to high-resolution mass spectrometry in conjunction with an in-house developed spectral database to identify metabolites at a high confidence level. Overall, we were able to characterize up to 171 unique meaningful metabolites in DCs. We then preliminarily compared the metabolic profiles of DCs derived from monocytes of 12 healthy donors upon in vitro LPS activation in a time-course experiment. Interestingly, the resulting data revealed differential and time-dependent activation of some particular metabolic pathways, the most impacted being nucleotides, nucleotide sugars, polyamines pathways, the TCA cycle, and to a lesser extent, the arginine pathway.

Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets

Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.

Data Fusion-based Discovery (DAFdiscovery) pipeline to aid compound annotation and bioactive compound discovery across diverse spectral data

INTRODUCTION

Data Fusion-based Discovery (DAFdiscovery) is a pipeline designed to help users combine mass spectrometry (MS), nuclear magnetic resonance (NMR), and bioactivity data in a notebook-based application to accelerate annotation and discovery of bioactive compounds. It applies Statistical Total Correlation Spectroscopy (STOCSY) and Statistical HeteroSpectroscopy (SHY) calculation in their data using an easy-to-follow Jupyter Notebook.

METHOD

Different case studies are presented for benchmarking, and the resultant outputs are shown to aid natural products identification and discovery. The goal is to encourage users to acquire MS and NMR data from their samples (in replicated samples and fractions when available) and to explore their variance to highlight MS features, NMR peaks, and bioactivity that might be correlated to accelerated bioactive compound discovery or for annotation-identification studies.

RESULTS

Different applications were demonstrated using data from different research groups, and it was shown that DAFdiscovery reproduced their findings using a more straightforward method.

CONCLUSION

DAFdiscovery has proven to be a simple-to-use method for different situations where data from different sources are required to be analyzed together.

Two-dimensional nanostructures based '-onics' and '-omics' in personalized medicine

With the maturing techniques for advanced synthesis and engineering of two-dimensional (2D) materials, its nanocomposites, hybrid nanostructures, alloys, and heterostructures, researchers have been able to create materials with improved as well as novel functionalities. One of the major applications that have been taking advantage of these materials with unique properties is biomedical devices, which currently prefer to be decentralized and highly personalized with good precision. The unique properties of these materials, such as high surface to volume ratio, a large number of active sites, tunable bandgap, nonlinear optical properties, and high carrier mobility is a boon to 'onics' (photonics/electronics) and 'omics' (genomics/exposomics) technologies for developing personalized, low-cost, feasible, decentralized, and highly accurate medical devices. This review aims to unfold the developments in point-of-care technology, the application of 'onics' and 'omics' in point-of-care medicine, and the part of two-dimensional materials. We have discussed the prospects of photonic devices based on 2D materials in personalized medicine and briefly discussed electronic devices for the same.

Integrated dose–response metabolomics with therapeutic effects and adverse reactions may demystify the dosage of traditional Chinese medicine

Background

Traditional Chinese medicine (TCM) has been used to treat various diseases for thousands of years. However, the uncertainty of dosage as well as the lack of systemic evaluation of pharmacology and toxicology is one major reason why TCM remains mysterious and is not accepted worldwide. Hence, we aimed to propose an integrated dose–response metabolomics strategy based on both therapeutic effects and adverse reactions to guide the TCM dosage in treatment.

Methods

The proposed methodology of integrated dose–response metabolomics includes four steps: dose design, multiple comparison of metabolic features, response calculation and dose–response curve fitting. By comparing the changes of all metabolites under different doses and calculating these changes through superposition, it is possible to characterize the global disturbance and thus describe the overall effect and toxicity of TCM induced by different doses. Rhubarb, commonly used for constipation treatment, was selected as a representative TCM.

Results

This developed strategy was successfully applied to rhubarb. The dose–response curves clearly showed the efficacy and adverse reactions of rhubarb at different doses. The rhubarb dose of 0.69 g/kg (corresponding to 7.66 g in clinic) was selected as the optimal dose because it was 90% of the effective dose and three adverse reactions were acceptable in this case.

Conclusion

An integrated dose–response metabolomics strategy reflecting both therapeutic effects and adverse reactions was established for the first time, which we believe is helpful to uncover the mysterious veil of TCM dosage. In addition, this strategy benefits the modernization and internationalization of TCM, and broadens the application of metabolomics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13020-022-00687-4.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.