Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects

Molecular networking: An efficient tool for discovering and identifying natural products

Natural products (NPs), play a crucial role in drug development. However, the discovery of NPs is accidental, and conventional identification methods lack accuracy. To overcome these challenges, an increasing number of researchers are directing their attention to Molecular networking (MN). MN based on secondary mass spectrometry has become an important tool for the separation, purification and structural identification of NPs. However, most new tools are not well known. This review started with the most basic MN tool and explains it from the principle, workflow, and application. Then introduce the principles and workflows of the remaining eight new types of MN tools. The reliability of various MNs is mainly verified based on the application of phytochemistry and metabolomics. Subsequently, the principles and applications of 12 structural annotation tools are introduced. For the first time, the scope of 9 kinds of MN tools is compared horizontally, and 12 kinds of structured annotation tools are classified from the type of compound structure suitable for identification. The advantages and disadvantages of various tools are summarized, and make suggestions for future application directions and the development of computing tools in this review. MN tools are expected to enhance the efficiency of the discovery and identification in NPs.

Unveiling the dark matter of the metabolome: A narrative review of bioinformatics tools for LC-HRMS-based compound annotation

Compound annotation, including the unveiling of dark matter in the metabolomics study represents a pivotal undertaking within the metabolomics field, serving as the linchpin for unraveling the identities and attributes of chemical entities. This narrative review examines the evolution of widely adopted compound annotation tools tailored for liquid chromatography-mass spectrometry (LC-MS) data analysis over the past two decades, which has been characterized by a transition from library-based search methodologies to advanced high-throughput approaches. Furthermore, emerging tools originating from both LC and MS domains were summarized. The synergistic partnership between quantitative structure-retention relationship (QSRR) models and machine learning (ML) techniques is explored, encompassing both conventional methodologies and advanced convolutional neural networks (CNNs). This collaborative framework has played a pivotal role in the precise prediction of retention times. Additionally, the enhanced applicability and extensibility of retention order prediction are emphasized, particularly under the constraints of experimental configurations. Within the domain of mass spectra-based annotation, the foundational task of mapping compound structures to mass spectra is examined-traditionally accomplished by aligning experimental data with established standards and libraries. Recent advancements highlight emerging tools that adopt multi-tiered mapping strategies, such as molecular networks and fragmentation trees, or incorporate machine learning to capture complex mapping patterns. This comprehensive examination underscores the pivotal role of compound annotation tools in advancing our understanding of complex LC-MS data matrix to further assist the annotation of dark matter in metabolome.

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).

Mass spectral database-based methodologies for the annotation and discovery of natural products

Natural products (NPs) have long held a significant position in various fields such as medicine, food, agriculture, and materials. The chemical space covered by NPs is extensive but often underexplored. Therefore, high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems. Mass spectrometry (MS) has emerged as a powerful platform for the annotation and discovery of NPs. MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information. Additionally, the released annotation methodologies, based on a variety of informatics tools, continuously improve the ability to annotate the structure and properties of compounds. This review examines the current mainstream databases and annotation methodologies, focusing on their advantages and limitations. Prospects for future technological advancements are then discussed in terms of novel applications and research objectives. Through a systematic overview, this review aims to provide valuable insights and a reference for MS-based NPs annotation, thereby promoting the discovery of novel natural entities.

Pushing Beyond the Resolution Limits of the "Brick" Miniature Mass Spectrometer Using a Deep Neural Network

In mass spectrometry, achieving high resolution often involves a trade-off with the signal-to-noise ratio (SNR), particularly in miniature mass spectrometers, where both properties typically fall short compared to commercial instruments. This study introduces a novel super-resolution reconstruction algorithm based on bidirectional long short-term memory (Bi-LSTM) networks. This algorithm effectively overcomes hardware performance limitations and reconciles the trade-off between SNR and resolution. The trained Bi-LSTM can perform super-resolution reconstruction of fixed-length mass spectral segments. When comparing the reconstruction results with theoretical ground truth, the normalized Euclidean distance achieves 98.9%, and the cosine similarity for isotope peak ratios reaches 98.1%. The algorithm demonstrates an impressive reconstruction capability for overlapping peaks. Following the integration of a reprocessing method, the algorithm can be directly applied to reconstruct experimental mass spectra. The robustness of the proposed algorithm is further validated through variations in ion trap scanning speed and peak dynamic range. Comparative analyses with a commercial time-of-flight (TOF) mass spectrometer confirm the validity of this method. Specifically, the cosine similarity at the highest scanning speed (6000 Da/s) reaches 0.88, while the cosine logarithmic values for 1 and 10 ng/mL concentrations attain 0.91 and 0.88, respectively. These results underscore the algorithm's effectiveness for analytical testing tasks in miniature mass spectrometers.

Rapid identification of pathogenic bacteria from clinical positive blood cultures via virus-like magnetic bead enrichment and MALDI-TOF MS profiling

Reducing the time required for the detection of bacteria in blood samples is a critical area of investigation in the field of clinical diagnosis. Positive blood culture samples often require a plate culture stage due to the interference of blood cells and proteins, which can result in significant delays before the isolation of single colonies suitable for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. In this study, we developed a non-specific enrichment strategy based on SiO2-encapsulated Fe3O4 nanoparticles combined with MALDI-TOF MS for direct identification of bacteria from aqueous environments or positive blood culture samples. Three distinct types of Fe3O4@SiO2 magnetic nanoparticles (MNPs) with unique surface morphologies were developed: spherical MNPs with smooth surfaces (Fe3O4@SN), mesoporous silica coated MNPs (Fe3O4@MSN), and MNPs exhibiting a viral spiked structure (Fe3O4@VSN). These MNPs exhibited excellent binding affinity towards both Staphylococcus aureus and Klebsiella pneumoniae in PBS and artificial saliva solutions. Furthermore, the strategy of using Fe3O4@VSN, which involves non-specific interactions between bacterial cells and the virus-like surface, resulted in a dramatic reduction in the minimum detectable concentrations of target pathogens by up to 1000-fold compared to conventional methods. Our results demonstrate that the use of Fe3O4@VSN has the potential to significantly reduce the processing time required after blood culture and may be useful for enrichment and identification of microorganisms in complex clinical samples.

Metabolomics Profiling and Advanced Methodologies for Wheat Stress Research

Metabolomics is an omics technology that studies the types, quantities, and changes of endogenous metabolic substances in organisms affected by abiotic and biotic factors.

BACKGROUND/OBJECTIVES

Based on metabolomics, small molecule metabolites in biological organisms can be qualitatively and quantitatively analysed. This method analysis directly correlates with biological phenotypes, facilitating the interpretation of life conditions. Wheat (Triticum aestivum L.) is one of the major food crops in the world, and its quality and yield play important roles in safeguarding food security.

METHODS

This review elaborated on the significance of metabolomics research techniques and methods in enhancing wheat resilience against biotic and abiotic stresses.

RESULTS

Metabolomics plays an important role in identifying the metabolites in wheat that respond to diverse stresses. The integrated examination of metabolomics with other omics disciplines provides new insights and approaches for exploring resistance genes, understanding the genetic basis of wheat metabolism, and revealing the mechanisms involved in stress responses.

CONCLUSIONS

Emerging metabolomics research techniques to propose innovative avenues of research is important to enhance wheat resistance.

Precision Dietary Intervention: Gut Microbiome and Meta-metabolome as Functional Readouts

Gut microbiome, the group of commensals residing within the intestinal tract, is closely associated with dietary patterns by interacting with food components. The gut microbiome is modifiable by the diet, and in turn, it utilizes the undigested food components as substrates and generates a group of small molecule-metabolites that addressed as "meta-metabolome" in this review. Profiling and mapping of meta-metabolome could yield insightful information at higher resolution and serve as functional readouts for precision nutrition and formation of personalized dietary strategies. For assessing the meta-metabolome, sample preparation is important, and it should aim for retrieval of gut microbial metabolites as intact as possible. The meta-metabolome can be investigated via untargeted and targeted meta-metabolomics with analytical platforms such as nuclear magnetic resonance spectroscopy and mass spectrometry. Employing flux analysis with meta-metabolomics using available database could further elucidate metabolic pathways that lead to biomarker discovery. In conclusion, integration of gut microbiome and meta-metabolomics is a promising supplementary approach to tailor precision dietary intervention. In this review, relationships among diet, gut microbiome, and meta-metabolome are elucidated, with an emphasis on recent advances in alternative analysis techniques proposed for nutritional research. We hope that this review will provide information for establishing pipelines complementary to traditional approaches for achieving precision dietary intervention.

PAH-Finder: A Pattern Recognition Workflow for Identification of PAHs and Their Derivatives

Polycyclic aromatic hydrocarbons (PAHs) are pervasive environmental pollutants with significant health risks due to their carcinogenic, mutagenic, and teratogenic properties. Traditional methods for PAH identification, primarily relying on gas chromatography-mass spectrometry (GC-MS), utilize spectral library searches together with other techniques, such as mass defect analysis. However, these methods are limited by incomplete spectral libraries and a high false positive rate. Here, we present PAH-Finder, a data-driven workflow that integrates machine learning with high-resolution mass spectrometry (HRMS). PAH-Finder introduces a novel approach to evaluate the fragment distribution of PAH backbones in MS spectra by normalizing fragment m/z values to a 0-100% range relative to the molecular ion peak. Seven machine learning features capture PAH fragmentation characteristics, and a random forest model trained on 98 PAH spectra and 1003 background spectra achieved an F1 score of ∼0.9 in 5-fold cross validation. Additionally, PAH-Finder leverages the presence of doubly charged fragments and molecular formula prediction to enhance the identification accuracy. In a case study, PAH-Finder identified 135 PAHs, including 7 types of previously unreported PAH formulas in particulate matter samples, demonstrating a 246% increase in annotation efficiency compared to the NIST20 library search. It also identified 32 heteroatom-doped PAHs not included in the training data set, showcasing its robustness of generalization. PAH-Finder's high accuracy in detecting a broad spectrum of PAHs facilitates efficient data processing and interpretation for nontargeted analysis, enhancing our understanding of air pollution and public health protection. PAH-Finder is freely available at Github (https://github.com/FangLabNTU/PAH-Finder).

Progress in Tandem Mass Spectrometry Data Analysis for Nucleic Acids

Mass spectrometry (MS) has become a critical tool in the characterization of covalently modified nucleic acids. Well-developed bottom-up approaches, where nucleic acids are digested with an endonuclease and the resulting oligonucleotides are separated before MS and MS/MS analysis, provide substantial insight into modified nucleotides in biological and synthetic nucleic. Top-down MS presents an alternative approach where the entire nucleic acid molecule is introduced to the mass spectrometer intact and then fragmented by MS/MS. Current top-down MS workflows have incorporated automated, on-line HPLC workflows to enable rapid desalting of nucleic acid samples for facile mass analysis without complication from adduction. Furthermore, optimization of MS/MS parameters utilizing collision, electron, or photon-based activation methods have enabled effective bond cleavage throughout the phosphodiester backbone while limiting secondary fragmentation, allowing characterization of progressively larger (~100 nt) nucleic acids and localization of covalent modifications. Development of software applications to perform automated identification of fragment ions has accelerated the broader adoption of mass spectrometry for analysis of nucleic acids. This review focuses on progress in tandem mass spectrometry for characterization of nucleic acids with particular emphasis on the software tools that have proven critical for advancing the field.

Extracting Knowledge from MS Clinical Metabolomic Data: Processing and Analysis Strategies

Assessing potential alterations of metabolic pathways using large-scale approaches plays today a central role in clinical research. Because several thousands of mass features can be measured for each sample with separation techniques hyphenated to mass spectrometry (MS) detection, adapted strategies have to be implemented to detect altered pathways and help to elucidate the mechanisms of pathologies. These procedures include peak detection, sample alignment, normalization, statistical analysis, and metabolite annotation. Interestingly, considerable advances have been made over the last years in terms of analytics, bioinformatics, and chemometrics to help massive and complex metabolomic data to be more adequately handled with automated processing and data analysis workflows. Recent developments and remaining challenges related to MS signal processing, metabolite annotation, and biomarker discovery based on statistical models are illustrated in this chapter in light of their application to clinical research.

Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences

Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.

Phytohormones enhance resistance to Tenebrio molitor by regulating reactive oxygen species and phenolic metabolism in pigeon pea

Pigeon pea is an important economic crop with medicinal and nutritional value. Unfortunately, pest infestation of leaves during postharvest storage seriously affects the quality of pigeon pea. Phytohormones play a crucial role in disease and pest defence by regulating the accumulation of specialized metabolites. Still, their impact on the postharvest storage of pigeon pea has not been reported. In this study, the physiological parameters and main phenotypes of pigeon pea leaves treated with MeJA, ABA, and GA were investigated for the first time. The activity of the antioxidant enzyme system, which eliminates reactive oxygen species, was enhanced by applying MeJA, GA, and ABA. MeJA, GA, and ABA significantly affected crown width, plant height, and relative water content in pigeon pea, respectively. Metabolomic profiling analysis identified phenolic compounds as the main differentially accumulated metabolites (DAMs). UPLC-QqQ-MS/MS identified stilbenes, flavanones, flavones, isoflavones and anthocyanins as major phenolic compounds responsive to MeJA, GA, and ABA induction. By feeding insects, it was found that the insects fed on MeJA-, ABA-, and GA-treated leaves less than on control leaves. Correlation analysis confirmed that isoflavones play an important role in this process. Moreover, the expression of key genes involved in flavonoid biosynthetic pathways and anti-insect-related genes was regulated by MeJA, GA, and ABA. Overall, this work provides a new strategy for the cultivation and storage of pigeon pea or other commercial crops and preliminarily clarifies that flavonoid metabolites under plant hormone treatment can promote plant growth and defence against insects by regulating reactive oxygen species.

Uncertainty Quantification and Flagging of Unreliable Predictions in Predicting Mass Spectrometry-Related Properties of Small Molecules Using Machine Learning

Mass spectral identification (in particular, in metabolomics) can be refined by comparing the observed and predicted properties of molecules, such as chromatographic retention. Significant advancements have been made in predicting these values using machine learning and deep learning. Usually, model predictions do not contain any indication of the possible error (uncertainty) or only one criterion is used for this purpose. The spread of predictions of several models included in the ensemble, and the molecular similarity of the considered molecule and the most "similar" molecule from the training set, are values that allow us to estimate the uncertainty. The Euclidean distance between vectors, calculated based on real-valued molecular descriptors, can be used for the assessment of molecular similarity. Another factor indicating uncertainty is the molecule's belonging to one of the clusters (data set clustering). Together, all three factors can be used as features for the uncertainty assessment model. Classification models that predict whether a prediction belongs to the worst 15% were obtained. The area under the receiver operating curve value is in the range of 0.73-0.82 for the considered tasks: the prediction of retention indices in gas chromatography, retention times in liquid chromatography, and collision cross-sections in ion mobility spectroscopy.

Integrating Metabolomics, Histopathology, and Cardiac Marker Analysis to Assess Valsartan's Efficacy in Mitigating Dasatinib-Induced Cardiac Toxicity in Sprague-Dawley Rats

Background

Dasatinib (DASA) is associated with cardiotoxic effects, posing risks to patients. Valsartan (VAL) may offer protective benefits against these effects. This study evaluates the impact of DASA, VAL, and their combination on cardiac health.

Methods

Wistar rats were treated with DASA, VAL, and a combination of VAL and DASA intraperitoneally every other day for 14 days. Body weight and survival rates were monitored. Serum levels of cardiac biomarkers (CPK, LDH, AST) were analyzed. Histopathological and immunohistochemical analyses assessed myocardial architecture and apoptosis-related protein expression. Metabolomic profiling was conducted using GC-MS to identify metabolic changes across treatment groups.

Results

The DASA group experienced significant weight loss and a 50% mortality rate, while the combination group had no mortality. Cardiac biomarkers like CPK, LDH, and AST were elevated in the DASA group but significantly reduced in the VAL + DASA group. Histopathological examination showed significant myocardial injury in the DASA group, with improved cardiac tissue morphology in the combination group. Immunohistochemical analysis revealed altered expression of apoptosis-related proteins, including caspase-3 and BCL-2, with improved levels in the combination group compared to DASA alone. Metabolomic profiling identified significant metabolic shifts, with 15 metabolites differentiating the treatment groups, and the VAL + DASA group mitigated the metabolic disturbances caused by DASA.

Conclusion

The study suggesting VAL's potential therapeutic role in managing DASA-induced cardiac toxicity. The combination of VAL with DASA not only improved survival rates and reduced cardiac biomarker levels but also preserved myocardial architecture and normalized metabolic profiles. These findings highlight the importance of integrated approaches in evaluating drug efficacy and suggest VAL as a promising candidate for protecting cardiac function in preclinical models of DASA therapy.

Arsenic-induced enhancement of diazotrophic recruitment and nitrogen fixation in Pteris vittata rhizosphere

Heavy metal contamination poses an escalating global challenge to soil ecosystems, with hyperaccumulators playing a crucial role in environmental remediation and resource recovery. The enrichment of diazotrophs and resulting nitrogen accumulation promoted hyperaccumulator growth and facilitated phytoremediation. Nonetheless, the regulatory mechanism of hyperaccumulator biological nitrogen fixation has remained elusive. Here, we report the mechanism by which arsenic regulates biological nitrogen fixation in the arsenic-hyperaccumulator Pteris vittata. Field investigations and greenhouse experiments, based on multi-omics approaches, reveal that elevated arsenic stress induces an enrichment of key diazotrophs, enhances plant nitrogen acquisition, and thus improves plant growth. Metabolomic analysis and microfluidic experiments further demonstrate that the upregulation of specific root metabolites plays a crucial role in recruiting key diazotrophic bacteria. These findings highlight the pivotal role of nitrogen-acquisition mechanisms in the arsenic hyperaccumulation of Pteris vittata, and provide valuable insights into the plant stress resistance.

Predicting Collision-Induced-Dissociation Tandem Mass Spectra (CID-MS/MS) Using Ab Initio Molecular Dynamics

Compound identification is at the center of metabolomics, usually by comparing experimental mass spectra against library spectra. However, most compounds are not commercially available to generate library spectra. Hence, for such compounds, MS/MS spectra need to be predicted. Machine learning and heuristic models have largely failed except for lipids. Here, quantum chemistry software can be used to predict mass spectra. However, quantum chemistry predictions for collision induced dissociation (CID) mass spectra in LC-MS/MS are rare. We present the CIDMD (Collision-Induced Dissociation via Molecular Dynamics) framework to model CID-based MS/MS spectra. It uses first-principles molecular dynamics (MD) to simulate the physical process of molecular collisions in CID tandem mass spectrometry. First, molecular ions are constructed at specific protonation sites. Using density functional theory, these protonated ions are targeted by argon collider gas atoms at user-specified velocities. Subsequent bond breakages are simulated over time for at least 1,000 fs. Each simulation is repeated multiple times from various collisional directions. Fragmentations are accumulated over those repeated collisions to generate CIDMD in silico mass spectra. Twelve small metabolites (<205 Da) were selected to test the accuracy of this framework in comparison to experimental MS/MS spectra. When testing different protomers, collider velocities, number of simulations, simulation time and impact factor b cutoffs, we yielded 261 predicted mass spectra. These in silico spectra resulted in entropy similarity scores of an average 624 ± 189 for all 261 spectra compared to their corresponding experimental spectra, which improved to 828 ± 77 when using optimal parameters of the most probable protomers for 12 molecules. With increasing molecular mass, higher velocities achieved better results. Similarly, different protomers showed large differences in fragmentation; hence, with increasing numbers of protomers and tautomers, the average CIDMD prediction accuracy decreased. Mechanistic details showed that specific fragment ions can be produced from different protomers via multiple fragmentation pathways. We propose that CIDMD is a suitable tool to predict mass spectra of small metabolites like produced by the gut microbiome.

Insights into predicting small molecule retention times in liquid chromatography using deep learning

In untargeted metabolomics, structures of small molecules are annotated using liquid chromatography-mass spectrometry by leveraging information from the molecular retention time (RT) in the chromatogram and m/z (formerly called ''mass-to-charge ratio'') in the mass spectrum. However, correct identification of metabolites is challenging due to the vast array of small molecules. Therefore, various in silico tools for mass spectrometry peak alignment and compound prediction have been developed; however, the list of candidate compounds remains extensive. Accurate RT prediction is important to exclude false candidates and facilitate metabolite annotation. Recent advancements in artificial intelligence (AI) have led to significant breakthroughs in the use of deep learning models in various fields. Release of a large RT dataset has mitigated the bottlenecks limiting the application of deep learning models, thereby improving their application in RT prediction tasks. This review lists the databases that can be used to expand training datasets and concerns the issue about molecular representation inconsistencies in datasets. It also discusses the application of AI technology for RT prediction, particularly in the 5 years following the release of the METLIN small molecule RT dataset. This review provides a comprehensive overview of the AI applications used for RT prediction, highlighting the progress and remaining challenges. SCIENTIFIC CONTRIBUTION: This article focuses on the advancements in small molecule retention time prediction in computational metabolomics over the past five years, with a particular emphasis on the application of AI technologies in this field. It reviews the publicly available datasets for small molecule retention time, the molecular representation methods, the AI algorithms applied in recent studies. Furthermore, it discusses the effectiveness of these models in assisting with the annotation of small molecule structures and the challenges that must be addressed to achieve practical applications.

Metabolomics reveals the lipid metabolism disorder in Pelophylax nigromaculatus exposed to environmentally relevant levels of microcystin-LR

Cyanobacterial blooms have emerged as a significant environmental issue worldwide in recent decades. However, the toxic effects of microcystin-LR (MC-LR) on aquatic organisms, such as frogs, have remained poorly understood. In this study, frogs (Pelophylax nigromaculatus) were exposed to environmentally relevant concentrations of MC-LR (0, 1, and 10 μg/L) for 21 days. Subsequently, we assessed the impact of MC-LR on the histomorphology of the frogs' livers and conducted a global MS-based nontarget metabolomics analysis, followed by the determination of substances involved in lipid metabolism. Results showed that MC-LR significantly induced histological alterations in the frogs' hepatopancreas. Over 200 differentially expressed metabolites were identified, primarily enriched in lipid metabolism. Biochemical analysis further confirmed that MC-LR exposure led to a disorder in lipid metabolism in the frogs. This study laid the groundwork for a mechanistic understanding of MC-LR toxicity in frogs and potentially other aquatic organisms.

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

Label-Free Quantitative Proteomics Analysis of COVID-19 Vaccines by Nano LC-HRMS

A nanoliter liquid chromatography-high resolution mass spectrometry-based method was developed for quantitative proteomics analysis of COVID-19 vaccines. It can be used for simultaneous qualitative and quantitative analysis of target proteins and host cell proteins (HCPs) in vaccine samples. This approach can directly provide protein information at the molecular level. Based on this, the proteomes of 15 batches of COVID-19 inactivated vaccine samples from two companies and 12 batches of COVID-19 recombinant protein vaccine samples from one company were successfully analyzed, which provided a significant amount of valuable information. Samples produced in different batches or by different companies can be systematically contrasted in this way, offering powerful supplements for existing quality standards. This strategy paves the way for profiling proteomics in complex samples and provides a novel perspective on the quality evaluation of bio-macromolecular drugs.

High-coverage identification of hydroxyl compounds based on pyridine derivatization-assisted liquid chromatography mass spectrometry

Hydroxyl compounds are widely present in plants and play essential roles in plant growth and development. High-coverage detection of hydroxyl compounds is crucial for understanding the physiological processes of plants. Despite the prevalence of chemical derivatization-assisted liquid chromatography-high resolution mass spectrometry (CD-LC-HRMS) in high-coverage detection of compounds with diverse functional groups, the confident identification of these compounds after derivatization remains a significant challenge. Herein, a novel method was developed for the identification of pyridine (PY)-derivatized hydroxyl compounds by comparing the MS/MS similarity of derivatized and corresponding underivatized compounds. Fragmentation rules of standards were summarized, and theoretical calculations have demonstrated the MS/MS similarity of PY-derivatized hydroxyl compounds with their underivatized counterparts. The effectiveness of the developed method was demonstrated by identifying PY-derivatized authentic standards. A total of 90 hydroxyl compounds were putatively identified in maize using the proposed method. This method can significantly enhance ionization efficiency with minimal impact on the quality of the MS/MS spectra, enabling the effective utilization of mass spectra databases for the identification of hydroxyl compounds.

Nontarget and suspect screening reveals the presence of multiple plastic-related compounds in polar bear, killer whale, narwhal and long-finned pilot whale blubber from East Greenland

The monitoring of legacy contaminants in sentinel northern marine mammals has revealed some of the highest concentrations globally. However, investigations into the presence of chemicals of emerging Arctic concern (CEACs) and other lesser-known chemicals are rarely conducted, if at all. Here, we used a nontarget/suspect approach to screen for thousands of different chemicals, including many CEACs and plastic-related compounds (PRCs) in blubber/adipose from killer whales (Orcinus orca), narwhals (Monodon monoceros), long-finned pilot whales (Globicephala melas), and polar bears (Ursus maritimus) in East Greenland. 138 compounds were tentatively identified mostly as PRCs, and four were confirmed using authentic standards: di(2-ethylhexyl) phthalate (DEHP), diethyl phthalate (DEP), di(2-propylheptyl) phthalate (DPHP), and one antioxidant (Irganox 1010). Three other PRCs, a nonylphenol isomer, 2,6-di-tert-butylphenol, and dioctyl sebacate, exhibited fragmentation patterns matching those in library databases. While phthalates were only above detection limits in some polar bear and narwhal, Irganox 1010, nonylphenol, and 2,6-di-tert-butylphenol were detected in >50% of all samples. This study represents the first application of a nontarget/suspect screening approach in Arctic cetaceans, leading to the identification of multiple PRCs in their blubber. Further nontarget analyses are warranted to comprehensively characterize the extent of CEAC and PRC contamination within Arctic marine food webs.

Quantum Chemistry-Based Prediction of Electron Ionization Mass Spectra for Environmental Chemicals

There is a lack of experimental electron ionization high-resolution mass spectra available to assist compound identification. The in silico generation of mass spectra by quantum chemistry can aid annotation workflows, in particular to support the identification of compounds that lack experimental reference spectra, such as environmental chemicals. We present an open-source, semiautomated workflow for the in silico prediction of electron ionization high-resolution mass spectra at 70 eV based on the QCxMS software. The workflow was applied to predict the spectra of 367 environmental chemicals, and the accuracy was evaluated by comparison to experimental reference spectra acquired. The molecular flexibility, number of rotatable bonds, and number of electronegative atoms of a compound were negatively correlated with prediction accuracy. Few analytes are predicted to sufficient accuracy for the direct application of predicted spectra in spectral matching workflows (overall average score 428). The m/z values of the top 5 most abundant ions of predicted spectra rarely match ions in experimental spectra, evidencing the disconnect between simulated fragmentation pathways and empirical reaction mechanisms.

Phytochemical screening, antioxidant, anti-diabetic, and anti-obesity activities, formulation, and characterization of a self-nanoemulsion system loaded with pomegranate (Punica granatum) seed oil

Pomegranate (Punica granatum) is a tree of the Punicaceae family that is widespread all over the world and has several types and therapeutic uses. The current study aimed to investigate the phytochemical compounds by GC analysis and carried out physical characterization of the pomegranate seed oil and its self-nanoemulsifying system. Then antioxidant, anti-diabetic, and anti-lipase activities were investigated for both.The pomegranate seed oil was extracted, and its self-nanoemulsifying system was then prepared. Phytochemical compounds were analyzed by GC, and physical characterization was established of the pomegranate seed oil and its self-nanoemulsifying system. Then antioxidant, anti-diabetic, and anti-lipase activities were investigated for both.The GC-MS analysis revealed that punicic acid, β-eleosteric acid, catalpic acid, α-eleosteric acid, and oleic acid were the most predominant compounds in pomegranate seed oil. Other active compounds like linoleic acid, palmitic acid, stearic acid, and α-linolenic acid were detected in trace percentages. The self-nanoemulsifying system was prepared using various concentrations of surfactant (Tween 80), co-surfactant (Span 80), and pomegranate seed oil. The selected formulation had a PDI of 0.229 ± 0.09 and a droplet size of 189.44 ± 2.1 nm. The free radical scavenging activity of pomegranate seed oil, the self-emulsifying system, and Trolox was conducted using DPPH. The oil-self-nanoemulsifying system showed potent antioxidant activity compared to Trolox. Also, pomegranate oil inhibited α-amylase with a weak IC50 value of 354.81 ± 2.3 µg/ml. The oil self-nanoemulsifying system showed potent activity compared to acarbose and had a weaker IC50 value (616.59 ± 2.1 µg/ml) and a potent IC50 value (43.65 ± 1.9 µg/ml) compared to orlistat.Pomegranate seed oil self-nanoemulsifying system could be applied in the future for the preparation of possible oral medications for the prevention and treatment of oxidative stress, diabetes, and obesity due to its high activity against free radical, amylase, and lipase enzymes compared to pomegranate seed oil itself and the references used. This study reveals that self-nanoemulsion systems can enhance oil drug formulations by improving pharmacokinetics and pharmacodynamics, acting as drug reservoirs, and facilitating efficient oil release.

In-depth characterization of cycloartane triterpenoids and discovery of species-specific markers from three Cimicifuga species guided by a strategy that integrates in-source fragment elimination, diagnostic ion recognition, and feature-based molecular networking

Characterization studies of the plant metabolome are crucial for revealing plant physiology, developing functional foods, and controlling quality. Mass spectrometry-based metabolite profiling allows unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte, which makes it challenging to filter out redundant signals and organize the signals corresponding to abundant constituents. This study proposed a strategy integrating in-source fragments elimination, diagnostic ions recognition, and feature-based molecular networking (ISFE-DIR-FBMN) to simultaneously characterize cycloartane triterpenoids (CTs) from three medicinal Cimicifuga species. The results showed that 63.1 % of the measured ions were redundant. A total of 184 CTs were annotated, with 27.1 % being reported for the first time. It presents a promising approach to assess the composition of natural extracts, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns. Besides, chemometrics analysis of the three Cimicifuga species identified 32 species-specific markers, highlighting significant differences among them. The valuable information can enhance the sustainable utilization and further development of Cimicifuga resources. The codes involved in ISFE-DIR-FBMN are freely available on GitHub (https://github.com/LHJ-Group/ISFE-DIR-FBMN.git).

Bioprospecting of soil-borne microorganisms and chemical dereplication of their anti-microbial constituents with the aid of UPLC-QTOF-MS and molecular networking approach

Due to the emergence of drug-resistant microorganisms, the search for broad-spectrum antimicrobial compounds has become extremely crucial. Natural sources like plants and soils have been explored for diverse metabolites with antimicrobial properties. This study aimed to identify microorganisms from agricultural soils exhibiting antimicrobial effects against known human pathogens, and to highlight the chemical space of the responsible compounds through the computational metabolomics-based bioprospecting approach. Herein, bacteria were extracted from soil samples and their antimicrobial potential was measured via the agar well diffusion method. Methanolic extracts from the active bacteria were analyzed using the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) technique, and the subsequent data was further analyzed through molecular networking approach which aided in identification of potential anti-microbial compounds. Furthermore, 16S rRNA gene sequencing enabled identification of the active bacterial isolates, where isolate 1 and 2 were identified as strains of Bacillus pumilus, whilst isolate 3 was found to be Bacillus subtilis. Interestingly, isolate 3 (Bacillus subtilis) displayed wide-ranging antimicrobial activity against the tested human pathogens. Molecular networking revealed the presence of Diketopiperazine compounds such as cyclo (D-Pro-D-Leu), cyclo (L-Tyr-L-Pro), cyclo (L-Pro-D-Phe), and cyclo (L-Pro-L-Val), alongside Surfactin C, Surfactin B, Pumilacidin E, and Isarrin D in the Bacillus strains as the main anti-microbial compounds. The application of the molecular networking approach represents an innovation in the field of bio-guided bioprospection of microorganisms and has proved to be an effective and feasible towards unearthing potent antimicrobial compounds. Additionally, the (computational metabolomics-based) approach accelerates the discovery of bioactive compounds and isolation of strains which offer a promising avenue for discovering new clinical antimicrobials. Finally, soil microbial flora could serve an alternative source of anti-microbial compounds which can assist in the fight against emergence of multi-drug resistance bacterial pathogens.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

Recent Developments in Machine Learning for Mass Spectrometry

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

Biological, phytochemical and molecular docking characteristics of Laurus nobilis L. fresh leaves essential oil from Palestine

BACKGROUND

The historical use of Laurus nobilis L., the plant is native to the Mediterranean region and has been cultivated for its aromatic leaves, which are used as a flavoring agent in cooking and for their potential therapeutic properties.

METHODS

The purpose of the current investigation was to characterize the essential oil composition of the fresh L. nobilis leaves from Palestine by using the gas chromatography-mass spectrometry (GC-MS) technique. DPPH (2,2-diphenyl-1-picrylhydrazyl), p-nitrophenyl butyrate, and 3,5-dinitro salicylic acid (DNSA) methods were employed to estimate the antioxidant, antiobesity, and antidiabetic effects of the essential oil. While MTS assay were used to evaluate their antiproliferative activities on panels of cell lines. Moreover, the docking studies were aided by the Prime MM GBSA method for estimating binding affinities.

RESULTS

The GC-MS investigation demonstrated that the fresh L. nobilis leaves essential oil has a variety of chemicals, about 31 different biochemicals were identified, and the major compounds were 1,8-cineole (48.54 ± 0.91%), terpinyl acetate (13.46 ± 0.34%), and α-terpinyl (3.84 ± 0.35%). Furthermore, the investigated oil demonstrated broad-spectrum antimicrobial activity against all tested bacterial and candidal strains and significantly inhibited the growth of MCF-7 cancerous cells more than the chemotherapeutic drug Doxorubicin. Furthermore, it contains robust DPPH free radicals, as well as porcine pancreatic α-amylase and lipase enzymes. Using the 1,8-cineole compound as the predominant biomolecule found in the L. nobilis essential oil, molecular docking studies were performed to confirm these observed fabulous results. The molecular docking simulations proposed that these recorded biological activities almost emanated from its high ability to form strong and effective hydrophobic interactions, this led to the getting of optimal fitting and interaction patterns within the binding sites of the applied crystallographic protein targets.

CONCLUSION

The results of these experiments showed that the fresh L. nobilis leaves essential oil has outstanding pharmacological capabilities, making this oil a potential source of natural medications.

Comprehensive toxicological, metabolomic, and transcriptomic analysis of the biodegradation and adaptation mechanism by Achromobacter xylosoxidans SL-6 to diuron

Biodegradation was considered a promising and environmentally friendly method for treating environmental pollution caused by diuron. However, the mechanisms of biodegradation of diuron required further research. In this study, the degradation process of diuron by Achromobacter xylosoxidans SL-6 was systematically investigated. The results suggested that the antioxidant system of strain SL-6 was activated by adding diuron, thereby alleviating their oxidative stress response. In addition, degradation product analysis showed that diuron in strain SL-6 was mainly degraded by urea bridge cleavage, dehalogenation, deamination, and ring opening, and finally cis, cis-muconic acid was generated. The combined analysis of metabolomics and transcriptomics revealed the biodegradation and adaptation mechanism of strain SL-6 to diuron. Metabolomics analysis showed that after the strain SL-6 was exposed to diuron, metabolic pathways such as tricarboxylic acid cycle (cis, cis-muconic acid), glutathione metabolism (oxidized glutathione), and urea cycle (arginine) were reprogrammed in the cells. Furthermore, diuron could induce the production of membrane transport proteins in strain SL-6 cells and overexpress antioxidant enzyme genes, finally ultimately promoting the up-regulation of genes encoding amide hydrolases and dioxygenases, which was revealed by transcriptomics studies. This work enriched the biodegradation mechanism of phenylurea herbicides and provided guidance for the removal of diuron residues in the environment and promoting agriculture sustainable development.

In-Electrospray source Hydrogen/Deuterium exchange coupled to multistage fragmentation for the investigation of the protonation and fragmentation pathways of gas phase ions

Identification of molecules in complex natural matrices relies on matching the fragmentation spectra of ions under investigation and the spectra acquired for the corresponding analytical standards. Currently, there are many databases of experimentally measured tandem mass spectrometry spectra (such as NIST, MzCloud, and Metlin), and considerable progress has been made in the development of software for predicting tandem mass spectrometry fragments in silico using combinatorial, machine learning, and quantum chemistry approaches (such as MetFrag, CFM-ID, and QCxMS). However, the electrospray ionization molecules can be ionized at different sites (protonated or deprotonated), and the fragmentation spectra of such ions are different. Here, we are using the combination of the in-ESI source hydrogen/deuterium exchange reaction and MSn fragmentation for the investigation of the fragmentation pathways for different protomers of organic molecules. It is shown that the distribution of the deuterium in the fragment ions reflects the presence of different protomers. For several molecules, the distribution of deuterium was traced up to the MS5 level of fragmentation revealing many unusual and unexpected effects. For example, we investigated the loss of HF from the ciprofloxacin and norfloxacin ions and observed that for ions protonated at -COOH group, the eliminating hydrogen always comes from -NH group. When ions are protonated at another site, the elimination of hydrogen with a probability of 30% occurs from the -NH group, and with a probability of 70%, it originates from other sites on the molecule. Such effects were not described previously. Quantum chemical simulation was used for the verification of the protonated structures and simulation of the corresponding fragmentation spectra.

Natural organic small molecules promote the aging of plastic wastes and refractory carbon decomposition in water

Microplastics and nanoplastics are ubiquitous in rivers and undergo environmental aging. However, the molecular mechanisms of plastic aging and the in-depth effects of aging on ecological functions remain unclear in waters. The synergies of microplastics and nanoplastics (polystyrene as an example) with natural organic small molecules (e.g., natural hyaluronic acid and vitamin C related to biological tissue decomposition) are the key to producing radicals (•OH and •C). The radicals promote the formation of bubbles on plastic surfaces and generate derivatives of plastics such as monomer and dimer styrene. Nanoplastics are easier to age than microplastics. Pristine plastics inhibit the microbial Shannon diversity index and evenness, but the opposite results are observed for aging plastics. Pristine plastics curb pectin decomposition (an indicator of plant-originated refractory carbon), but aging plastics promote pectin decomposition. Microplastics and nanoplastics undergoing aging processes enhance the carbon biogeochemical cycle. For example, the increased carbohydrate active enzyme diversity, especially the related glycoside hydrolase and functional species Pseudomonas and Clostridium, contributes to refractory carbon decomposition. Different from the well-studied toxicity and aging of plastic pollutants, this study connects plastic pollutants with biological tissue decomposition, biodiversity and climate change together in rivers.

Behavior, antioxidant, and metabolomics effects of Allium tuncelianum

Allium species are consumed extensively as folkloric medicine and dietary elements, but limited studies have been conducted on them. In this study, the effects of an ethanol-water extract obtained from the underground bulb of Allium tuncelianum (Kollmann) Özhatay, B. Mathew & Şiraneci (AT) on the behavioral, antioxidant, and metabolite parameters in rats were evaluated. AT was administered orally once a day at doses of 100 and 400 mg/kg to male Wistar albino rats for 10 consecutive days. The elevated plus maze, rotarod, and hotplate tests were used to examine anxiety-like behaviors, locomotor activities, and pain perception in the rats, respectively. Additionally, untargeted metabolomic analyses were performed on plasma samples and AT extracts using two orthogonal analytical platforms. The phenolic components, mainly fumaric acid, malic acid, vanillic acid, quercetin-3-arabinoside, hydrocinnamic acid, and gallocatechin, were determined in the extract. In addition, arbutin, salicylic acid, trehalose, and nicotinic acid were analyzed in the extract for the first time. The AT extract did not decrease the catalase, glutathione peroxidase, or superoxide dismutase levels; however, diazepam decreased some of those parameters significantly in the brain, liver, and kidney. Although both the AT and diazepam treatments resulted in an increase in anxiolytic-like effects compared to the control group, no significant differences were observed (p > .05). In the metabolomic analysis, significant changes were observed in the rats treated with AT and diazepam, and they caused significant changes in some metabolic pathways, including amino acid and fatty acid metabolism, compared to the control.

Qualitative and quantitative rapid detection of VOCs differentially released by VAP-associated bacteria using PTR-MS and FGC-PTR-MS

Ventilator-associated pneumonia (VAP) is a prevalent disease caused by microbial infection, resulting in significant morbidity and mortality within the intensive care unit (ICU). The rapid and accurate identification of pathogenic bacteria causing VAP can assist clinicians in formulating timely treatment plans. In this study, we attempted to differentiate bacterial species in VAP by utilizing the volatile organic compounds (VOCs) released by pathogens. We cultured 6 common bacteria in VAP in vitro, including Acinetobacter baumannii, Enterobacter cloacae, Escherichia coli, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, and Staphylococcus aureus, which covered most cases of VAP infection in clinic. After the VOCs released by bacteria were collected in sampling bags, they were quantitatively detected by a proton transfer reaction-mass spectrometry (PTR-MS), and the characteristic ions were qualitatively analyzed through a fast gas chromatography-proton transfer reaction-mass spectrometry (FGC-PTR-MS). After conducting principal component analysis (PCA) and analysis of similarities (ANOSIM), we discovered that the VOCs released by 6 bacteria exhibited differentiation following 3 h of quantitative cultivation in vitro. Additionally, we further investigated the variations in the types and concentrations of bacterial VOCs. The results showed that by utilizing the differences in types of VOCs, 6 bacteria could be classified into 5 sets, except for A. baumannii and E. cloacae which were indistinguishable. Furthermore, we observed significant variations in the concentration ratio of acetaldehyde and methyl mercaptan released by A. baumannii and E. cloacae. In conclusion, the VOCs released by bacteria could effectively differentiate the 6 pathogens commonly associated with VAP, which was expected to assist doctors in formulating treatment plans in time and improve the survival rate of patients.

A spectroscopic test suggests that fragment ion structure annotations in MS/MS libraries are frequently incorrect

Modern untargeted mass spectrometry (MS) analyses quickly detect and resolve thousands of molecular compounds. Although features are readily annotated with a molecular formula in high-resolution small-molecule MS applications, the large majority of them remains unidentified in terms of their full molecular structure. Collision-induced dissociation tandem mass spectrometry (CID-MS2) provides a diagnostic molecular fingerprint to resolve the molecular structure through a library search. However, for de novo identifications, one must often rely on in silico generated MS2 spectra as reference. The ability of different in silico algorithms to correctly predict MS2 spectra and thus to retrieve correct molecular structures is a topic of lively debate, for instance in the CASMI contest. Underlying the predicted MS2 spectra are the in silico generated product ion structures, which are normally not used in de novo identification, but which can serve to critically assess the fragmentation algorithms. Here we evaluate in silico generated MSn product ion structures by comparison with structures established experimentally by infrared ion spectroscopy (IRIS). For a set of three dozen product ion structures from five precursor molecules, we find that virtually all fragment ion structure annotations in three major in silico MS2 libraries (HMDB, METLIN, mzCloud) are incorrect and caution the reader against their use for structure annotation of MS/MS ions.

Comparative study of Fe2+/H2O2 and Fe2+/persulfate systems on the pre-treatment process of real pharmaceutical wastewater

Advanced oxidation technologies based on hydroxyl radical (•OH) and sulfate radical (SO4-•) are two common types of advanced oxidation technologies, but there are not many reports on the application of advanced oxidation methods in actual wastewater pretreatment. This article compares the pre-treatment performance of Fe2+/H2O2 and Fe2+/Persulfate systems in actual pharmaceutical wastewater, and combines EEM, GC-MS, and toxicity testing results to explore the differences in TOC, COD, and NH3-N removal rates, optimal catalyst dosage, applicable pH range, toxicity of effluent after reaction, and pollutant structure between the two systems. The results indicate that the Fe2+/H2O2 system has a higher pollutant removal rate (TOC: 71.9%, COD: 66.9%, NH3-N: 34.1%), but also requires a higher catalyst (Fe2+) concentration (6.0 g/L), and its effluent exhibits characteristic peaks of aromatic proteins. The Fe2+/Persulfate system has a wider pH range (pH ≈ 3-7) and is more advantageous in treating wastewater containing more cyclic organic compounds, but the effluent contains some sulfur-containing compounds. In addition, toxicity tests have shown that the toxicity reduction effect of the Fe2+/Persulfate system is stronger than that of the Fe2+/H2O2 system.

Data-Driven Compound Identification in Atmospheric Mass Spectrometry

Aerosol particles found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol particle formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Large amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, the authors review the current state of data-driven compound identification with mass spectrometry in atmospheric science and discuss current challenges and possible future steps toward a digital era for atmospheric mass spectrometry.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

Reactivity-based identification of oxygen containing functional groups of chemicals applied as potential classifier in non-target analysis

In this work, we developed a reactivity-based strategy to identify functional groups of unknown analytes, which can be applied as classifier in non-target analysis with gas chromatography. The aim of this strategy is to reduce the number of potential candidate structures generated for a molecular formula determined by high resolution mass spectrometry. We selected an example of 18 isomers with the molecular formula C12H10O2 to test the performance of different derivatization reagents, whereas our aim was to select mild and fast reaction conditions. Based on the results for the isomers, we developed a four-step workflow for the identification of functional groups containing oxygen.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Integration of non-targeted multicomponent profiling, targeted characteristic chromatograms and quantitative to accomplish systematic quality evaluation strategy of Huo-Xiang-Zheng-Qi oral liquid

Huo-Xiang-Zheng-Qi oral liquid (HXZQOL) is a well-known traditional Chinese medicine formula for the treatment of gastrointestinal diseases, with the pharmacologic effects of antiinflammatory, immune protection and gastrointestinal motility regulation. More significantly, HXZQOL is recommended for the treatment of COVID-19 patients with gastrointestinal symptoms, and it has been clinically proven to reduce the inflammatory response in patients with COVID-19. However, the effective and overall quality control of HXZQOL is currently limited due to its complex composition, especially the large amount of volatile and non-volatile active components involved. In this study, aimed to fully develop a comprehensive strategy based on non-targeted multicomponent identification, targeted authentication and quantitative analysis for quality evaluation of HXZQOL from different batches. Firstly, the non-targeted high-definition MSE (HDMSE) approach is established based on UHPLC/IM-QTOF-MS, utilized for multicomponent comprehensive characterization of HXZQOL. Combined with in house library-driven automated peak annotation and comparison of 47 reference compounds, 195 components were initially identified. In addition, HS-SPME-GC-MS was employed to analyze the volatile organic compounds (VOCs) in HXZQOL, and a total of 61 components were identified by comparison to the NIST database, reference compounds as well as retention indices. Secondly, based on the selective ion monitoring (SIM) of 24 "identity markers" (involving each herbal medicine), characteristic chromatograms (CCs) were established on LC-MS and GC-MS respectively, to authenticate 15 batches of HXZQOL samples. The targeted-SIM CCs showed that all marker compounds in 15 batches of samples could be accurately monitored, which could indicate preparations authenticity. Finally, a parallel reaction monitoring (PRM) method was established and validated to quantify the nine compounds in 15 batches of HXZQOL. Conclusively, this study first reports chemical-material basis, SIM CCs and quality evaluation of HXZQOL, which is of great implication to quality control and ensuring the authenticity of the preparation.

LibGen: Generating High Quality Spectral Libraries of Natural Products for EAD-, UVPD-, and HCD-High Resolution Mass Spectrometers

Compound annotation using spectral-matching algorithms is vital for (MS/MS)-based metabolomics research, but is hindered by the lack of high-quality reference MS/MS library spectra. Finding and removing errors from libraries, including noise ions, is mostly done manually. This process is both error-prone and time-consuming. To address these challenges, we have developed an automated library curation pipeline, LibGen, to universally build novel spectral libraries. This pipeline corrects mass errors, denoises spectra by subformula assignments, and performs quality control of the reference spectra by calculating explained intensity and spectral entropy. We employed LibGen to generate three high-quality libraries with chemical standards of 2241 natural products. To this end, we used an IQ-X orbital ion trap mass spectrometer to generate 1947 classic high-energy collision dissociation spectra (HCD) as well as 1093 ultraviolet-photodissociation (UVPD) mass spectra. The third library was generated by an electron-activated collision dissociation (EAD) 7600 ZenoTOF mass spectrometer yielding 3244 MS/MS spectra. The natural compounds covered 140 chemical classes from prenol lipids to benzypyrans with >97% of the compounds showing <0.2 Tanimoto-similarity, demonstrating a very high structural variance. Mass spectra showed much higher information content for both UVPD- and EAD-mass spectra compared to classic HCD spectra when using spectral entropy calculations. We validated the denoising algorithm by acquiring MS/MS spectra at high concentration and at 13-fold diluted chemical standards. At low concentrations, a higher proportion of spectra showed apparent fragment ions that could not be explained by subformula losses of the parent molecule. When more than 10% of the total intensity of MS/MS fragments was regarded as noise ions, spectra were considered as low quality and were not included in the libraries. As the overall process is fully automated, LibGen can be utilized by all researchers who create or curate mass spectral libraries. The libraries we created here are publicly available at MassBank.us.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Metabolomic analysis indicated changes in triacylglycerols' levels as a result of training in Whippet dogs

Regular physical effort produces metabolic changes manifested as adaptation to exercise and increasing performance. In humans these changes have been characterized at metabolome level as depending on the discipline. However, all sports involve some level of changes in protein, carbohydrate and lipid metabolism. Recently, also performance horses have been subjected to metabolic analyses, but similar studies were lacking in sports dogs. In this study we performed the metabolomic analysis in plasma of Whippet dogs regularly trained and competing in coursing events, and untrained dogs of the same breed, fed with the same diet. We have also compared the hematological and blood biochemical results in these two groups of dogs. Basic blood tests indicated that enzymes related to lipid metabolism (lipase and gamma-glutamyltransferase) differed considerably between the groups. Metabolomic analysis of plasma confirmed the metabolic shift expressed as the differences in triacylglycerols levels between training and non-training dogs, aimed at improving the use of fatty acids as a source of energy during exertion. Surprisingly, other classes of metabolites were only hardly changed when comparing training and non-training Whippets.

Mass spectrometry-based analysis of gut microbial metabolites of aromatic amino acids

Small molecules derived from gut microbiota have been increasingly investigated to better understand the functional roles of the human gut microbiome. Microbial metabolites of aromatic amino acids (AAA) have been linked to many diseases, such as metabolic disorders, chronic kidney diseases, inflammatory bowel disease, diabetes, and cancer. Important microbial AAA metabolites are often discovered via global metabolite profiling of biological specimens collected from humans or animal models. Subsequent metabolite identity confirmation and absolute quantification using targeted analysis enable comparisons across different studies, which can lead to the establishment of threshold concentrations of potential metabolite biomarkers. Owing to their excellent selectivity and sensitivity, hyphenated mass spectrometry (MS) techniques are often employed to identify and quantify AAA metabolites in various biological matrices. Here, we summarize the developments over the past five years in MS-based methodology for analyzing gut microbiota-derived AAA. Sample preparation, method validation, analytical performance, and statistical methods for correlation analysis are discussed, along with future perspectives.