Hierarchical cluster analysis of technical replicates to identify interferents in untargeted mass spectrometry metabolomics

Integrative Stacking Machine Learning Model for Small Cell Lung Cancer Prediction Using Metabolomics Profiling

Background: Small cell lung cancer (SCLC) is an extremely aggressive form of lung cancer, characterized by rapid progression and poor survival rates. Despite the importance of early diagnosis, the current diagnostic techniques are invasive and restricted. Methods: This study presents a novel stacking-based ensemble machine learning approach for classifying small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) using metabolomics data. The analysis included 191 SCLC cases, 173 NSCLC cases, and 97 healthy controls. Feature selection techniques identified significant metabolites, with positive ions proving more relevant. Results: For multi-class classification (control, SCLC, NSCLC), the stacking ensemble achieved 85.03% accuracy and 92.47 AUC using Support Vector Machine (SVM). Binary classification (SCLC vs. NSCLC) further improved performance, with ExtraTreesClassifier reaching 88.19% accuracy and 92.65 AUC. SHapley Additive exPlanations (SHAP) analysis revealed key metabolites like benzoic acid, DL-lactate, and L-arginine as significant predictors. Conclusions: The stacking ensemble approach effectively leverages multiple classifiers to enhance overall predictive performance. The proposed model effectively captures the complementary strengths of different classifiers, enhancing the detection of SCLC and NSCLC. This work accentuates the potential of combining metabolomics with advanced machine learning for non-invasive early lung cancer subtype detection, offering an alternative to conventional biopsy methods.

Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

Differentiating Pond-Intensive, Paddy-Ecologically, and Free-Range Cultured Crayfish (Procambarus clarkii) Using Stable Isotope and Multi-Element Analysis Coupled with Chemometrics

The farming pattern of crayfish significantly impacts their quality, safety, and nutrition. Typically, green and ecologically friendly products command higher economic value and market competitiveness. Consequently, intensive farming methods are frequently employed in an attempt to replace these environmentally friendly products, leading to potential instances of commercial fraud. In this study, stable isotope and multi-element analysis were utilized in conjunction with multivariate modeling to differentiate between pond-intensive, paddy-ecologically, and free-range cultured crayfish. The four stable isotope ratios of carbon, nitrogen, hydrogen, and oxygen (δ13C, δ15N, δ2H, δ18O) and 20 elements from 88 crayfish samples and their feeds were determined for variance analysis and correlation analysis. To identify and differentiate three different farming pattern crayfish, unsupervised methods such as hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used, as well as supervised multivariate modeling, specifically partial least squares discriminant analysis (PLS-DA). The HCA and PCA exhibited limited effectiveness in classifying the farming pattern of crayfish, whereas the PLS-DA demonstrated a more robust performance with a predictive accuracy of 90.8%. Additionally, variables such as δ13C, δ15N, δ2H, Mn, and Co exhibited relatively higher contributions in the PLS-DA model, with a variable influence on projection (VIP) greater than 1. This study is the first attempt to use stable isotope and multi-element analysis to distinguish crayfish under three farming patterns. It holds promising potential as an effective strategy for crayfish authentication.

Transcriptome and metabolome analyses reveal chlorogenic acid accumulation in pigmented potatoes at different altitudes

Pigmented potato tubers are abundant in chlorogenic acids (CGAs), a metabolite with pharmacological activity. This article comprehensively analyzed the transcriptome and metabolome of pigmented potato Huaxingyangyu and Jianchuanhong at four altitudes of 1800 m, 2300 m, 2800 m, and 3300 m. A total of 20 CGAs and intermediate CGA compounds were identified, including 3-o-caffeoylquinic acid, 4-o-caffeoylquinic acid, and 5-o-caffeoylquinic acid. CGA contents in Huaxinyangyu and Jianchuanhong reached its maximum at an altitude of 2800 m and slightly decreased at 3300 m. 48 candidate genes related to the biosynthesis pathway of CGAs were screened through transcriptome analysis. Weighted gene co-expression network analysis (WGCNA) identified that the structural genes of phenylalanine deaminase (PAL), coumarate-3 hydroxylase (C3H), cinnamic acid 4-hydroxylase (C4H) and the transcription factors of MYB and bHLH co-regulate CGA biosynthesis. The results of this study provide valuable information to reveal the changes in CGA components in pigmented potato at different altitudes.

Metabolomics approach to understand molecular mechanisms involved in fungal pathogen-citrus pathosystems

Citrus is a crucial crop with a significant economic impact globally. However, postharvest decay caused by fungal pathogens poses a considerable threat, leading to substantial financial losses. Penicillium digitatum, Penicillium italicum, Geotrichum citri-aurantii and Phyllosticta citricarpa are the main fungal pathogens, causing green mold, blue mold, sour rot and citrus black spot diseases, respectively. The use of chemical fungicides as a control strategy in citrus raises concerns about food and environmental safety. Therefore, understanding the molecular basis of host-pathogen interactions is essential to find safer alternatives. This review highlights the potential of the metabolomics approach in the search for bioactive compounds involved in the pathogen-citrus interaction, and how the integration of metabolomics and genomics contributes to the understanding of secondary metabolites associated with fungal virulence and the fungal infection mechanisms. Our goal is to provide a pipeline combining metabolomics and genomics that can effectively guide researchers to perform studies aiming to contribute to the understanding of the fundamental chemical and biochemical aspects of pathogen-host interactions, in order to effectively develop new alternatives for fungal diseases in citrus cultivation. We intend to inspire the scientific community to question unexplored biological systems, and to employ diverse analytical approaches and metabolomics techniques to address outstanding questions about the non-studied pathosystems from a chemical biology perspective.

Identification of Potential Antiviral Hops Compounds against Chikungunya Virus

Chikungunya virus (CHIKV) is an arthropod-borne virus that belongs to the genus Alphavirus (family Togaviridae). CHIKV causes chikungunya fever, which is mostly characterized by fever, arthralgia and, sometimes, a maculopapular rash. The bioactive constituents of hops (Humulus lupulus, Cannabaceae), mainly acylphloroglucinols, known as well as α- and β-acids, exerted distinct activity against CHIKV, without showing cytotoxicity. For fast and efficient isolation and identification of such bioactive constituents, a silica-free countercurrent separation method was applied. The antiviral activity was determined by plaque reduction test and was visually confirmed by a cell-based immunofluorescence assay. All hops compounds demonstrated a promising post-treatment viral inhibition, except the fraction of acylphloroglucinols, in mixture. β-acids fraction of 125 µg/mL expressed the strongest virucidal activity (EC50 = 15.21 µg/mL), in a drug-addition experiment on Vero cells. Hypothesis for mechanism of action were proposed for acylphloroglucinols based on their lipophilicity and chemical structure. Therefore, inhibition of some steps of the protein kinase C (PKC) transduction cascades was also discussed.

New algorithms demonstrate untargeted detection of chemically meaningful changing units and formula assignment for HRMS data of polymeric mixtures in the open-source constellation web application

The field of high-resolution mass spectrometry (HRMS) and ancillary hyphenated techniques comprise a rapidly expanding and evolving area. As popularity of HRMS instruments grows, there is a concurrent need for tools and solutions to simplify and automate the processing of the large and complex datasets that result from these analyses. Constellation is one such of these tools, developed by our group over the last two years to perform unsupervised trend detection for repeating, polymeric units in HRMS data of complex mixtures such as natural organic matter, oil, or lignin. In this work, we develop two new unsupervised algorithms for finding chemically-meaningful changing units in HRMS data, and incorporate a molecular-formula-finding algorithm from the open-source CoreMS software package, both demonstrated here in the Constellation software environment. These algorithms are evaluated on a collection of open-source HRMS datasets containing polymeric analytes (PEG 400 and NIST standard reference material 1950, both metabolites in human plasma, as well as a swab extract containing polymers), and are able to successfully identify all known changing units in the data, including assigning the correct formulas. Through these new developments, we are excited to add to a growing body of open-source software specialized in extracting useful information from complex datasets without the high costs, technical knowledge, and processor-demand typically associated with such tools.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

Exploring Micromonospora as Phocoenamicins Producers

Over the past few years, new technological and scientific advances have reinforced the field of natural product discovery. The spirotetronate class of natural products has recently grown with the discovery of phocoenamicins, natural actinomycete derived compounds that possess different antibiotic activities. Exploring the MEDINA's strain collection, 27 actinomycete strains, including three marine-derived and 24 terrestrial strains, were identified as possible phocoenamicins producers and their taxonomic identification by 16S rDNA sequencing showed that they all belong to the Micromonospora genus. Using an OSMAC approach, all the strains were cultivated in 10 different media each, resulting in 270 fermentations, whose extracts were analyzed by LC-HRMS and subjected to High-throughput screening (HTS) against methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis H37Ra and Mycobacterium bovis. The combination of LC-UV-HRMS analyses, metabolomics analysis and molecular networking (GNPS) revealed that they produce several related spirotetronates not disclosed before. Variations in the culture media were identified as the most determining factor for phocoenamicin production and the best producer strains and media were established. Herein, we reported the chemically diverse production and metabolic profiling of Micromonospora sp. strains, including the known phocoenamicins and maklamicin, reported for the first time as being related to this family of compounds, as well as the bioactivity of their crude extracts. Although our findings do not confirm previous statements about phocoenamicins production only in unique marine environments, they have identified marine-derived Micromonospora species as the best producers of phocoenamicins in terms of both the abundance in their extracts of some major members of the structural class and the variety of molecular structures produced.

Non-Targeted Chemical Characterization of JUUL-Menthol-Flavored Aerosols Using Liquid and Gas Chromatography

The aerosol constituents generated from JUUL Menthol pods with 3.0% and 5.0% nicotine by weight (Me3 and Me5) are characterized by a non-targeted approach, which was developed to detect aerosol constituents that are not known to be present beforehand or that may be measured with targeted methods. Three replicates from three production batches (n = 9) were aerosolized using two puffing regimens (intense and non-intense). Each of the 18 samples were analyzed by gas chromatography electron ionization mass spectrometry and by liquid chromatography electrospray ionization high-resolving power mass spectrometry. All chemical constituents determined to differ from control were identified and semi-quantified. To have a complete understanding of the aerosol constituents and chemistry, each chemical constituent was categorized into one of five groups: (1) flavorants, (2) harmful and potentially harmful constituents, (3) leachables, (4) reaction products, and (5) chemical constituents that were unable to be identified or rationalized (e.g., chemical constituents that could not be categorized in groups (1–4). Under intense puffing, 74 chemical constituents were identified in Me3 aerosols and 68 under non-intense puffing, with 53 chemical constituents common between both regimens. Eighty-three chemical constituents were identified in Me5 aerosol using an intense puffing regimen and seventy-five with a non-intense puffing regimen, with sixty-two chemical constituents in common. Excluding primary constituents, reaction products accounted for the greatest number of chemical constituents (approximately 60% in all cases, ranging from about 0.05% to 0.1% by mass), and flavorants—excluding menthol—comprised the second largest number of chemical constituents (approximately 25%, ranging consistently around 0.01% by mass). The chemical constituents detected in JUUL aerosols were then compared to known constituents from cigarette smoke to determine the relative chemical complexities and commonalities/differences between the two. This revealed (1) a substantial decrease in the chemical complexity of JUUL aerosols vs. cigarette smoke and (2) that there are between 55 (Me3) and 61 (Me5) unique chemical constituents in JUUL aerosols not reported in cigarette smoke. Understanding the chemical complexity of JUUL aerosols is important because the health effects of combustible cigarette smoke are related to the combined effect of these chemical constituents through multiple mechanisms, not just the effects of any single smoke constituent.

Biosynthetic regulatory network of flavonoid metabolites in stems and leaves of Salvia miltiorrhiza

Flavonoid secondary metabolites can treat and prevent many diseases, but systematic studies on regulation of the biosynthesis of such metabolites in aboveground parts of Salvia miltiorrhiza are lacking. In this study, metabonomic and transcriptomic analyses of different S. miltiorrhiza phenotypes were conducted to explore pathways of synthesis, catalysis, accumulation, and transport of the main flavonoid secondary metabolites regulating pigment accumulation. Tissue localization and quantitative analysis of flavonoid secondary metabolites were conducted by laser scanning confocal microscopy (LSCM). A total 3090 differentially expressed genes were obtained from 114,431 full-length unigenes in purple and green phenotypes, and 108 functional genes were involved in flavonoid biosynthesis. Five key phenylpropane structural genes (PAL, 4CL, ANS, 3AT, HCT) were highly differentially expressed, and four transcription factor genes (MYB, WRKY, bHLH, bZiP) were identified. In addition, six GST genes, nine ABC transporters, 22 MATE genes, and three SNARE genes were detected with key roles in flavonoid transport. According to LSCM, flavonoids were mainly distributed in epidermis, cortex, and collenchyma. Thus, comprehensive and systematic analyses were used to determine biosynthesis, accumulation, and transport of flavonoids in stems and leaves of different S. miltiorrhiza phenotypes. The findings will provide a reference for flavonoid production and cultivar selection.

Discovery of potential hypoglycemic metabolites in Cassiae Semen by coupling UHPLC-QTOF-MS/MS combined plant metabolomics and spectrum-effect relationship analyses

Cassiae Semen (CS) is consumed as fried tea or medicinal food in Asian areas. Its two commercial forms, namely raw and fried CS, exert different clinical applications, in which fried CS is commonly applied as a functional tea for losing weight. To prevent confusion in the use of the two forms of CS, a comprehensive strategy by combining plant metabolomics and spectrum-effect relationship analyses was developed for the fast and efficient discrimination of raw and fried CS, and further for the discovery of the potential hypoglycemic metabolites of CS to control its quality. First, the plant metabolic profiling of raw and processed samples was performed by UHPLC-QTOF-MS/MS. A total of 1111 differential metabolites were found to well distinguish the raw and fried CS after analyzing by MPP software. Subsequently, α-glucosidase inhibition of raw and fried CS was investigated. As a result, fried CS demonstrated much stronger α-glucosidase inhibition activity than the raw sample. By analyzing the spectrum-effect relationship with GRA, BCA, and PLSR, 14 potential hypoglycemic-related compounds were discovered. As anticipated, they were also found as the differential metabolites of the raw and fried samples with a potential hypoglycemic effect, which might be beneficial for the quality control of CS tea. Additionally, molecular docking analysis was conducted to reveal the underlying inhibition mechanisms of the four most critical constituents, including physcion, chrysoobtusin, aurantio-obtusin, and obtusifolin. This study provides a powerful tool for the discrimination of processed samples and fast screening of the active constituents in complex natural products on a high-throughput basis.

Study on plasma metabolomics for HIV/AIDS patients treated by HAART based on LC/MS-MS

Background: Metabolomics can be applied to the clinical diagnosis and treatment evaluation of acquired immune deficiency syndrome (AIDS). AIDS biomarkers have become a new direction of AIDS research providing clinical guidance for diagnosis. Objective: We sought to apply both untargeted and targeted metabolomic profiling to identify potential biomarkers for AIDS patients. Methods: A liquid chromatography-tandem mass spectrometry (LC-MS/MS) based untargeted metabolomic profiling was performed on plasma samples of patients before and after highly active antiretroviral therapy (HAART) treatment as well as healthy volunteers to identify potential AIDS biomarkers. Targeted quantitative analysis was performed on the potential biomarkers screened from untargeted metabolic profiling for verification. Results: Using the Mass Profiler Professional and the MassHunter, several potential biomarkers have been found by LC-MS/MS in the untargeted metabolomic study. High-resolution MS and MS/MS were used to analyze fragmentation rules of the metabolites, with comparisons of related standards. Several potential biomarkers have been identified, including PS(O-18:0/0:0), sphingosine, PE (21:0/0:0), and 1-Linoleoyl Glycerol. Targeted quantitative analysis showed that sphingosine and 1-Linoleoyl Glycerol might be closely related to HIV/AIDS, which may be the potential biomarkers to the diagnosis. Conclusion: We conducted untargeted metabolomic profiling, which indicates that several metabolites should be considered potential biomarkers for AIDS patients. Further targeted metabolomic research verified that d-Sphingosine and 1-Linoleoyl glycerol as the diagnostic biomarker of AIDS.

Metabolomics-Guided Comparison of Pollen and Microalgae-Based Artificial Diets in Honey Bees

Managed honey bee colonies used for crop pollination are fed artificial diets to offset nutritional deficiencies related to land-use intensification and climate change. In this study, we formulated novel microalgae diets using Chlorella vulgaris and Arthrospira platensis (spirulina) biomass and fed them to young adult honey bee workers. Diet-induced changes in bee metabolite profiles were studied relative to a natural pollen diet using liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) metabolomics. Untargeted analyses of pollen- and microalgae-fed bees revealed significant overlap, with 248 shared features determined by LC-MS and 87 shared features determined by GC-MS. Further metabolomic commonalities were evident upon subtraction of unique diet features. Twenty-five identified metabolites were influenced by diet, which included complex lipids, essential fatty acids, vitamins, and phytochemicals. The metabolomics results are useful to understand mechanisms underlying favorable growth performance as well as increased antioxidant and heat shock protein gene expression in bees fed the microalgae diets. We conclude that the tested microalgae have potential as sustainable feed additives and as a source of bee health-modulating natural products. Metabolomics-guided diet development could eventually help tailor feed interventions to achieve precision nutrition in honey bees and other livestock animals.

Metabolomics Analysis of Bacterial Pathogen Burkholderia thailandensis and Mammalian Host Cells in Co-culture

The Tier 1 HHS/USDA Select Agent Burkholderia pseudomallei is a bacterial pathogen that is highly virulent when introduced into the respiratory tract and intrinsically resistant to many antibiotics. Transcriptomic- and proteomic-based methodologies have been used to investigate mechanisms of virulence employed by B. pseudomallei and Burkholderia thailandensis, a convenient surrogate; however, analysis of the pathogen and host metabolomes during infection is lacking. Changes in the metabolites produced can be a result of altered gene expression and/or post-transcriptional processes. Thus, metabolomics complements transcriptomics and proteomics by providing a chemical readout of a biological phenotype, which serves as a snapshot of an organism's physiological state. However, the poor signal from bacterial metabolites in the context of infection poses a challenge in their detection and robust annotation. In this study, we coupled mammalian cell culture-based metabolomics with feature-based molecular networking of mono- and co-cultures to annotate the pathogen's secondary metabolome during infection of mammalian cells. These methods enabled us to identify several key secondary metabolites produced by B. thailandensis during infection of airway epithelial and macrophage cell lines. Additionally, the use of in silico approaches provided insights into shifts in host biochemical pathways relevant to defense against infection. Using chemical class enrichment analysis, for example, we identified changes in a number of host-derived compounds including immune lipids such as prostaglandins, which were detected exclusively upon pathogen challenge. Taken together, our findings indicate that co-culture of B. thailandensis with mammalian cells alters the metabolome of both pathogen and host and provides a new dimension of information for in-depth analysis of the host-pathogen interactions underlying Burkholderia infection.

Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti-doping and veterinary residues.

A procedure to detect and identify specific chemicals of potential inhalation toxicity concern in aerosols

OBJECTIVE

Understanding the potential inhalation toxicity of poorly characterized aerosols is challenging both because aerosols may contain numerous chemicals and because it is difficult to predict which chemicals may present significant inhalation toxicity concerns at the observed levels. We have developed a novel systematic procedure to address these challenges through non-targeted chemical analysis by two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) and assessment of the results using publicly available toxicity data to prioritize the tentatively identified detected chemicals according to potential inhalation toxicity.

MATERIALS AND METHODS

The procedure involves non-targeted chemical analysis of aerosol samples utilizing GC × GC-TOFMS, which is selected because it is an effective technique for detecting chemicals in complex samples and assigning tentative identities according to the mass spectra. For data evaluation, existing toxicity data (e.g. from the U.S. Environmental Protection Agency CompTox Chemicals Dashboard) are used to calculate multiple toxicity metrics that can be compared among the tentatively identified chemicals. These metrics include hazard quotient, incremental lifetime cancer risk, and metrics analogous to hazard quotient that we designated as exposure-(toxicology endpoint) ratios.

RESULTS AND DISCUSSION

We demonstrated the utility of our procedure by detecting, identifying, and prioritizing specific chemicals of potential inhalation toxicity concern in the mainstream smoke generated from the machine-smoking of marijuana blunts.

CONCLUSION

By designing a systematic approach for detecting and identifying numerous chemicals in complex aerosol samples and prioritizing the chemicals in relation to different inhalation toxicology endpoints, we have developed an effective approach to elucidate the potential inhalation toxicity of aerosols.

Circular Network of Coregulated Sphingolipids Dictates Chronic Hypoxia Damage in Patients With Tetralogy of Fallot

Background: Tetralogy of Fallot (TOF) is the most common cyanotic heart disease. However, the association of cardiac metabolic reprogramming changes and underlying molecular mechanisms in TOF-related chronic myocardial hypoxia damage are still unclear.

Methods: In this study, we combined microarray transcriptomics analysis with liquid chromatography tandem-mass spectrometry (LC–MS/MS) spectrum metabolomics analysis to establish the metabolic reprogramming that occurs in response to chronic hypoxia damage. Two Gene Expression Omnibus (GEO) datasets, GSE132176 and GSE141955, were downloaded to analyze the metabolic pathway in TOF. Then, a metabolomics analysis of the clinical samples (right atrial tissue and plasma) was performed. Additionally, an association analysis between differential metabolites and clinical phenotypes was performed. Next, four key genes related to sphingomyelin metabolism were screened and their expression was validated by real-time quantitative PCR (QT-PCR).

Results: The gene set enrichment analysis (GSEA) showed that sphingolipid metabolism was downregulated in TOF and the metabolomics analysis showed that multiple sphingolipids were dysregulated. Additionally, genes related to sphingomyelin metabolism were identified. We found that four core genes, UDP-Glucose Ceramide Glucosyltransferase (UGCG), Sphingosine-1-Phosphate Phosphatase 2 (SGPP2), Fatty Acid 2-Hydroxylase (FA2H), and Sphingosine-1-Phosphate Phosphatase 1 (SGPP1), were downregulated in TOF.

Conclusion: Sphingolipid metabolism was downregulated in TOF; however, the detailed mechanism needs further investigation.

Sample preparation techniques for suspect and non-target screening of emerging contaminants

The progress in sensitivity and resolution in mass spectrometers in recent years provides the possibility to detect a broad range of organic compounds in a single procedure. For this reason, suspect and non-target screening techniques are gaining attention since they enable the detection of hundreds of known and unknown emerging contaminants in various matrices of environmental, food and human sources. Sample preparation is a critical step before analysis as it can significantly affect selectivity, sensitivity and reproducibility. The lack of generic sample preparation protocols is obvious in this fast-growing analytical field, and most studies use those of traditional targeted analysis methods. Among them, solvent extraction and solid phase extraction (SPE) are widely used to extract emerging contaminants from solid and liquid sample types, respectively. Sequential solvent extraction and a combination of different SPE sorbents can cover a broad range of chemicals in the samples. Gel permeation chromatography (GPC) and adsorption chromatography, including acidification, are typically used to remove matrix components such as lipids from complex matrices, but usually at the expense of compound losses. Ideally, the purification of samples intended for non-target analysis should be selective of matrix interferences. Recent studies have suggested quality assurance/quality control measures for suspect and non-target screening, based on expansion and extrapolation of target compound lists, but method validations remain challenging in the absence of analytical standards and harmonized sample preparation approaches.

"Q-markers targeted screening" strategy for comprehensive qualitative and quantitative analysis in fingerprints of Angelica dahurica with chemometric methods

Angelica dahurica is a famous functional food and herb. To guarantee quality of A. dahurica, a strategy “Q-markers targeted screening” was successfully developed by sufficient extraction of compounds and the targeted screening of qualitative and quantitative markers calculated through chemometric methods based fingerprints. Accelerated solvent extraction was selected due to its prominent advantages exhibiting the maximum extraction yields and varieties of compounds and especially excellent reproducibility (RSD < 1). After extraction, the fingerprints of A. dahuricae samples were established. For the preliminary herb authenticity, the targeted screening of 23 quantitative markers were performed by similarity analysis and hierarchical cluster analysis based on the fingerprints, which were identified by liquid chromatography tandem mass spectrometry (LC-MS). Subsequently, for further quality control, the targeted screening of nine quantitative markers were done by similarity analysis & linear discriminant analysis, which were determined by LC. Lastly, the strategy was successfully applied to quality assessment of A. dahurica samples.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

Benefiting from big data in natural products: importance of preserving foundational skills and prioritizing data quality

Systematic, large-scale, studies at the genomic, metabolomic, and functional level have transformed the natural product sciences. Improvements in technology and reduction in cost for obtaining spectroscopic, chromatographic, and genomic data coupled with the creation of readily accessible curated and functionally annotated data sets have altered the practices of virtually all natural product research laboratories. Gone are the days when the natural products researchers were expected to devote themselves exclusively to the isolation, purification, and structure elucidation of small molecules. We now also engage with big data in taxonomic, genomic, proteomic, and/or metabolomic collections, and use these data to generate and test hypotheses. While the oft stated aim for the use of large-scale -omics data in the natural products sciences is to achieve a rapid increase in the rate of discovery of new drugs, this has not yet come to pass. At the same time, new technologies have provided unexpected opportunities for natural products chemists to ask and answer new and different questions. With this viewpoint, we discuss the evolution of big data as a part of natural products research and provide a few examples of how discoveries have been enabled by access to big data. We also draw attention to some of the limitations in our existing engagement with large datasets and consider what would be necessary to overcome them.

Untargeted and targeted analysis of sarin poisoning biomarkers in rat urine by liquid chromatography and tandem mass spectrometry

Chemical warfare agents continue to pose a real threat to humanity, despite their prohibition under the Chemical Weapons Convention. Sarin is one of the most toxic and lethal representatives of nerve agents. The methodology for the targeted analysis of known sarin metabolites has reached great heights, but little attention has been paid to the untargeted analysis of biological samples of victims exposed to this deadly poisonous substance. At present, the development of computational and statistical methods of analysis offers great opportunities for finding new metabolites or understanding the mechanisms of action or effect of toxic substances on the organism. This study presents the targeted LC-MS/MS determination of methylphosphonic acid and isopropyl methylphosphonic acid in the urine of rats exposed to a non-lethal dose of sarin, as well as the untarget urine analysis by LC-HRMS. Targeted analysis of polar acidic sarin metabolites was performed on a mixed-mode reversed-phase anion-exchange column, and untargeted analysis on a conventional reversed-phase C18 column. Isopropyl methylphosphonic acid was detected and quantified within 5 days after subcutaneous injection of sarin at a dose of 1/4 LD₅₀. A combination of generalized additive mixed models and dose-response analysis with database searches using accurate mass of precursor ions and corresponding MS/MS spectra enabled us to propose new six potential biomarkers of biological response to exposure. The results confirm the well-known fact that sarin poisoning has a significant impact on the victims' metabolome, with inhibition of acetylcholinesterase being just the first step and trigger of the complex toxicodynamic response.

Spatial pattern analysis reveals multiple sources of organophosphorus flame retardants in coastal waters

Organophosphorus flame retardants (PFRs) are a group of emerging contaminants which have been detected in worldwide waters. However, source of various PFRs in the large-scale area like coastal water environment have not been clearly revealed. In this study, fifteen PFRs in coast of Guangdong-Hong Kong-Macao Greater Bay area (GBA), China were investigated, and a method of spatial pattern analysis was firstly used for pollution source identification. Seawater samples from different segments of GBA coast were analyzed and thirteen PFRs were quantified with total concentrations ranging from 32.7 to 1032.7 ng L^-1. GBA coasts have been seriously polluted by PFRs. A hierarchical cluster analysis of the PFR concentrations in different GBA sites showed significant spatial distributions for different types of PFRs. A series of correlation analysis between PFRs distributions and spatial pattern of GBA socio-economic indicators were performed, and multiple sources including human settlement, wastewater, manufacture, construction industry, vehicles, and shipping transport were found to be correlated to PFRs pollutions in the coasts. This study indicates that spatial pattern analysis based on statistical analysis would be a promising method of analyzing environmental data and exploring pollution source in large-scale area.

Genomic and Phenotypic Analysis of COVID-19-Associated Pulmonary Aspergillosis Isolates of Aspergillus fumigatus

The ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for coronavirus disease 2019 (COVID-19), first described in Wuhan, China. A subset of COVID-19 patients has been reported to have acquired secondary infections by microbial pathogens, such as opportunistic fungal pathogens from the genus Aspergillus. To gain insight into COVID-19-associated pulmonary aspergillosis (CAPA), we analyzed the genomes and characterized the phenotypic profiles of four CAPA isolates of Aspergillus fumigatus obtained from patients treated in the area of North Rhine-Westphalia, Germany. By examining the mutational spectrum of single nucleotide polymorphisms, insertion-deletion polymorphisms, and copy number variants among 206 genes known to modulate A. fumigatus virulence, we found that CAPA isolate genomes do not exhibit significant differences from the genome of the Af293 reference strain. By examining a number of factors, including virulence in an invertebrate moth model, growth in the presence of osmotic, cell wall, and oxidative stressors, secondary metabolite biosynthesis, and the MIC of antifungal drugs, we found that CAPA isolates were generally, but not always, similar to A. fumigatus reference strains Af293 and CEA17. Notably, CAPA isolate D had more putative loss-of-function mutations in genes known to increase virulence when deleted. Moreover, CAPA isolate D was significantly more virulent than the other three CAPA isolates and the A. fumigatus reference strains Af293 and CEA17, but similarly virulent to two other clinical strains of A. fumigatus. These findings expand our understanding of the genomic and phenotypic characteristics of isolates that cause CAPA. IMPORTANCE The global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronavirus disease 2019 (COVID-19), has already killed millions of people. COVID-19 patient outcome can be further complicated by secondary infections, such as COVID-19-associated pulmonary aspergillosis (CAPA). CAPA is caused by Aspergillus fungal pathogens, but there is little information about the genomic and phenotypic characteristics of CAPA isolates. We conducted genome sequencing and extensive phenotyping of four CAPA isolates of Aspergillus fumigatus from Germany. We found that CAPA isolates were often, but not always, similar to other reference strains of A. fumigatus across 206 genetic determinants of infection-relevant phenotypes, including virulence. For example, CAPA isolate D was more virulent than other CAPA isolates and reference strains in an invertebrate model of fungal disease, but similarly virulent to two other clinical strains. These results expand our understanding of COVID-19-associated pulmonary aspergillosis.

Identification and Differentiation of Wide Edible Mushrooms Based on Lipidomics Profiling Combined with Principal Component Analysis

Mushroom, as a kind of higher fungus, is a precious homology resource of medicine and foods. In this study, total lipids were extracted from eight wild edible mushrooms and subsequently characterized by ultra-high-performance liquid chromatography-Quadrupole-Exactive Orbitrap mass spectrometry. 20 lipid classes and 173 molecular species were identified and quantified. Lipid molecules and their concentrations in Boletus speciosus, Boletus bainiugan, and Tricholoma matsutake exhibited significantly different behaviors compared with the remaining mushrooms. Hierarchical cluster analysis revealed that lipid profiles of B. bainiugan were most similar to B. speciosus followed by T. matsutake, Canthar-ellus cibarius, Sarcodon aspratu, Termitomyces eurrhizus, Laccaria laccata, and Thelephora ganbajun. In addition, several differential lipids can be considered as potential biomarkers to distinguish different mushroom species, for instance, lysophosphatidylethanolamine (16:1) and ceramide non-hydroxy fatty acid-dihydrosphingosine (d23:0-10:0). This study provided a new perspective to discriminate the mushroom species from the perspective of lipidomics.

Non-Targeted Chemical Characterization of JUUL Virginia Tobacco Flavored Aerosols Using Liquid and Gas Chromatography

The chemical constituents of JUUL Virginia Tobacco pods with 3.0% and 5.0% nicotine by weight (VT3 and VT5) were characterized by non-targeted analyses, an approach to detect chemicals that are not otherwise measured with dedicated methods or that are not known beforehand. Aerosols were generated using intense and non-intense puffing regimens and analyzed by gas chromatography electron ionization mass spectrometry and liquid chromatography electrospray ionization high resolving power mass spectrometry. All compounds above 0.7 µg/g for GC–MS analysis or above 0.5 µg/g for LC–HRMS analysis and differing from blank measurements were identified and semi-quantified. All identifications were evaluated and categorized into five groups: flavorants, harmful and potentially harmful constituents, extractables and/or leachables, reaction products, and compounds that could not be identified/rationalized. For VT3, 79 compounds were identified using an intense puffing regimen and 69 using a non-intense puffing regimen. There were 60 compounds common between both regimens. For VT5, 85 compounds were identified with an intense puffing regimen and 73 with a non-intense puffing regimen; 67 compounds were in common. For all nicotine concentrations, formulations and puffing regimens, reaction products accounted for the greatest number of compounds (ranging from 70% to 75%; 0.08% to 0.1% by mass), and flavorants comprised the second largest number of compounds (ranging from for 15% to 16%; 0.1 to 0.2% by mass). A global comparison of the compounds detected in JUUL aerosol to those catalogued in cigarette smoke indicated an approximate 50-fold decrease in chemical complexity. Both VT3 and VT5 aerosols contained 59 unique compounds not identified in cigarette smoke.

Comprehensive feature-based molecular networking and metabolomics approaches to reveal the differences components in Cinnamomum cassia and Cinnamomum verum

Cinnamon was been a widely used plant in medicinal and spices for a long time and has spread all over the world. However, the differences in the components of the bark from Cinnamomum cassia and Cinnamomum verum, the two most common types of cinnamon, have not been thoroughly investigated. In the present experiment, ultra-high-performance liquid chromatography LTQ-Orbitrap Velos Pro hybrid mass spectrometer-based metabolomics coupled with chemometrics and feature-based molecular networking were employed to dramatically distinguish and annotate Cinnamomum cassia Bark and Cinnamomum verum bark. As a consequence, principal component analysis, orthogonal projection to latent structures discriminates analysis, and heat map analysis demonstrated clear discrimination between the profiles of metabolites in cinnamon. Besides, as the known compounds, proanthocyanidins (cinnamtannin B1 and procyanidin B2) and alkaloids (norboldine, norisoboldine) with variable importance in the projection scores >6, and an unknown alkaloid (formula C₂₄ H₃₃ NO₆ ) were selected as the best markers to discriminate cinnamon. Furthermore, large numbers of proanthocyanidins and alkaloids components were identified through feature-based molecular networking for the first time. Our investigation provides new ideas for the discovery of quality markers and identification of unknown components in natural products.

Individual variability in human urinary metabolites identifies age-related, body mass index-related, and sex-related biomarkers

Background

Metabolites present in human urine can be influenced by individual physiological parameters (e.g., body mass index [BMI], age, and sex). Observation of altered metabolites concentrations could provide insight into underlying disease pathology, disease prognosis and diagnosis, and facilitate discovery of novel biomarkers.

Methods

Quantitative metabolomics analysis in the urine of 183 healthy individuals was performed based on high‐resolution liquid chromatography–mass spectrometry (LC–MS). Coefficients of variation were obtained for 109 urine metabolites of all the 183 human healthy subjects.

Results

Three urine metabolites (such as dehydroepiandrosterone sulfate, acetaminophen glucuronide, and p‐anisic acid) with CV₁₈₃ > 0.3, for which metabolomics studies have been scarce, are considered highly variable here. We identified 30 age‐related metabolites, 18 BMI‐related metabolites, and 42 sex‐related metabolites. Among the identified metabolites, three metabolites were found to be associated with all three physiological parameters (age, BMI, and sex), which included dehydroepiandrosterone sulfate, 3‐methylcrotonylglycine and N‐acetyl‐aspartic acid. Pearson's coefficients demonstrated that some age‐, BMI‐, and sex‐related compounds are strongly correlated, suggesting that age, BMI, and sex could affect them concomitantly.

Conclusion

Metabolic differences between distinct physiological statuses were found to be related to several metabolic pathways (such as the caffeine metabolism, the amino acid metabolism, and the carbohydrate metabolism), and these findings may be key for the discovery of new diagnostics and treatments as well as new understandings on the mechanisms of some related diseases.

Soybean Metabolomics Based in Mass Spectrometry: Decoding the Plant's Signaling and Defense Responses under Biotic Stress

Metabolomics is an omics technology that is extremely valuable to analyze all small-molecule metabolites in organisms. Recent advances in analytical instrumentation, such as mass spectrometry combined with data processing tools, chemometrics, and spectral data libraries, allow plant metabolomics studies to play a fundamental role in the agriculture field and food security. Few studies are found in the literature using the metabolomics approach in soybean plants on biotic stress. In this review, we provide a new perspective highlighting the potential of metabolomics-based mass spectrometry for soybean in response to biotic stress. Furthermore, we highlight the response and adaptation mechanisms of soybean on biotic stress about primary and secondary metabolism. Consequently, we provide subsidies for further studies of the resistance and improvement of the crop.

Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability

Despite the value of mass spectrometry in modern natural products discovery workflows, it remains very difficult to compare data sets between laboratories. In this study we compared mass spectrometry data for the same sample set from two different laboratories (quadrupole time-of-flight and quadrupole-Orbitrap) and evaluated the similarity between these two data sets in terms of both mass spectrometry features and their ability to describe the chemical composition of the sample set. Somewhat surprisingly, the two data sets, collected with appropriate controls and replication, had very low feature overlap (25.7% of Laboratory A features overlapping 21.8% of Laboratory B features). Our data clearly demonstrate that differences in fragmentation, charge state, and adduct formation in the ionization source are a major underlying cause for these differences. Consistent with other recent literature, these findings challenge the conventional wisdom that electrospray ionization mass spectrometry (ESI-MS) yields a simple one-to-one correspondence between analytes in solution and features in the data set. Importantly, despite low overlap in feature lists, principal component analysis (PCA) generated qualitatively similar PCA plots. Overall, our findings demonstrate that comparing untargeted metabolomics data between laboratories is challenging, but that data sets with low feature overlap can yield the same qualitative description of a sample set using PCA.

Creation and filtering of a recurrent spectral library of CHO cell metabolites and media components

This paper reports the first implementation of a new type of mass spectral library for the analysis of Chinese hamster ovary (CHO) cell metabolites that allows users to quickly identify most compounds in any complex metabolite sample. We also describe an annotation methodology developed to filter out artifacts and low‐quality spectra from recurrent unidentified spectra of metabolites. CHO cells are commonly used to produce biological therapeutics. Metabolic profiles of CHO cells and media can be used to monitor process variability and look for markers that discriminate between batches of product. We have created a comprehensive library of both identified and unidentified metabolites derived from CHO cells that can be used in conjunction with tandem mass spectrometry to identify metabolites. In addition, we present a workflow that can be used for assigning confidence to a NIST MS/MS Library search match based on prior probability of general utility. The goal of our work is to annotate and identify (when possible), all liquid chromatography‐mass spectrometry generated metabolite ions as well as create automatable library building and identification pipelines for use by others in the field.

Metabolic Variation Dictates Cardiac Pathogenesis in Patients With Tetralogy of Fallot

BACKGROUND

Herein, we aimed to analyze cardiac metabolic reprogramming in patients with tetralogy of Fallot (ToF).

METHODS

Cardiac metabolic reprogramming was analyzed through comprehensive bioinformatics analysis, which included gene set enrichment, gene set variation, and consensus clustering analyses, so as to assess changes in metabolic pathways. In addition, full-spectrum metabolomics analysis was performed using right atrial biopsy samples obtained from patients with ToF and atrial septal defect (ASD) before cardiopulmonary bypass; ultrahigh performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was used to construct a metabolic map of cardiac metabolic reprogramming in cyanotic congenital heart disease.

RESULTS

The metabolic maps of carbohydrate metabolic process and heme metabolism were significantly activated, while bile acid metabolism, lipid droplet, and lipid binding were primarily restrained in ToF samples as compared with that in ASD samples. The reprogramming of butanoate metabolism was identified basing on the UPLC-MS/MS detection and analysis in myocardial hypoxia damage in cyanotic heart disease. Finally, the butanoate metabolism-related hub regulators ALDH5A1 and EHHADH were identified and they were significantly downregulated in ToF samples.

CONCLUSIONS

The metabolic network of butanoate metabolism involved ALDH5A1 and EHHADH, which could contribute to myocardial tissue damage in cyanotic congenital heart of ToF. Our results provide further insights into the mechanisms underlying metabolic reprogramming in cyanotic congenital heart disease and could lead to the identification of potential therapeutic targets.

Nutritional components characterization of Goji berries from different regions in China

Goji berries are used as functional food for hundreds of years in Asia, Europe, North America and Austria, and are popular for nutritive properties in global. Commercial Goji berries are mainly produced in Ningxia, Xinjiang, Gansu, Qinghai and Inner Mongolia of China. However, the Goji berries produced in these regions exhibited different appearance and taste. Thus, characterization of the nutritional components in Goji berries produced in these regions could provide the guidance for application of them. In this study, 94 samples were collected, and a total of 20 amino acids, 17 nucleosides and nucleobases, 4 sugars and protein were determined by UHPLC-MS/MS, HPLC-ELSD or UV, and the variation was illustrated through heatmap clustering analysis, PCA and PLS-DA. The results showed that Goji berries from Xinjiang were rich in protein than the samples from other regions; those from Gansu and Ningxia were rich in amino acids, nucleosides and nucleobases; and those from Jiuquan of Gansu and Qinghai were rich in sugars. Heatmap clustering and PCA analysis results showed that all the samples exhibited a significant spatial aggregation, and the producing regions located along the Yellow River (belonging to the Hetao plain) produced Goji berries with the similar chemical profile. Additionally, PLS-DA analysis results showed that fructose and glucose were the predominant markers to distinguish Goji berries from different producing regions.

Impact of ion suppression by sample cap liners in lipidomics

Contamination from the polymeric material released by vial caps used for sample introduction in liquid chromatography can significantly affect the signal of the analyte of interest. In particular, repeated injections from the same sample vial can enhance this suppressing effect. Multiple injections of the same sample are often used in metabolomics and lipidomics during routine analyses. Here we demonstrate how the presence of contaminant polymers, originating from the vial closures, significantly influences the estimation of the relative amount of endogenous lipids in human plasma. Furthermore, this can negatively impact other operations in mass spectrometric analysis, such as instrument equilibration and tuning or the common use of technical replicates to improve confidence in data interpretation. Our observations provide critical information on how to improve future analyses through the use of appropriate vial caps, solvents, chromatographic separations and equipment.

Chemical composition and biological effects of kratom (Mitragyna speciosa): In vitro studies with implications for efficacy and drug interactions

The safety and efficacy of kratom (Mitragyna speciosa) for treatment of pain is highly controversial. Kratom produces more than 40 structurally related alkaloids, but most studies have focused on just two of these, mitragynine and 7-hydroxymitragynine. Here, we profiled 53 commercial kratom products using untargeted LC-MS metabolomics, revealing two distinct chemotypes that contain different levels of the alkaloid speciofoline. Both chemotypes were confirmed with DNA barcoding to be M. speciosa. To evaluate the biological relevance of variable speciofoline levels in kratom, we compared the opioid receptor binding activity of speciofoline, mitragynine, and 7-hydroxymitragynine. Mitragynine and 7-hydroxymitragynine function as partial agonists of the human µ-opioid receptor, while speciofoline does not exhibit measurable binding affinity at the µ-, δ- or ƙ-opioid receptors. Importantly, mitragynine and 7-hydroxymitragynine demonstrate functional selectivity for G-protein signaling, with no measurable recruitment of β-arrestin. Overall, the study demonstrates the unique binding and functional profiles of the kratom alkaloids, suggesting potential utility for managing pain, but further studies are needed to follow up on these in vitro findings. All three kratom alkaloids tested inhibited select cytochrome P450 enzymes, suggesting a potential risk for adverse interactions when kratom is co-consumed with drugs metabolized by these enzymes.

Variation Among Biosynthetic Gene Clusters, Secondary Metabolite Profiles, and Cards of Virulence Across Aspergillus Species

Aspergillus fumigatus is a major human pathogen. In contrast, Aspergillus fischeri and the recently described Aspergillus oerlinghausenensis, the two species most closely related to A. fumigatus, are not known to be pathogenic. Some of the genetic determinants of virulence (or "cards of virulence") that A. fumigatus possesses are secondary metabolites that impair the host immune system, protect from host immune cell attacks, or acquire key nutrients. To examine whether secondary metabolism-associated cards of virulence vary between these species, we conducted extensive genomic and secondary metabolite profiling analyses of multiple A. fumigatus, one A. oerlinghausenensis, and multiple A. fischeri strains. We identified two cards of virulence (gliotoxin and fumitremorgin) shared by all three species and three cards of virulence (trypacidin, pseurotin, and fumagillin) that are variable. For example, we found that all species and strains examined biosynthesized gliotoxin, which is known to contribute to virulence, consistent with the conservation of the gliotoxin biosynthetic gene cluster (BGC) across genomes. For other secondary metabolites, such as fumitremorgin, a modulator of host biology, we found that all species produced the metabolite but that there was strain heterogeneity in its production within species. Finally, species differed in their biosynthesis of fumagillin and pseurotin, both contributors to host tissue damage during invasive aspergillosis. A. fumigatus biosynthesized fumagillin and pseurotin, while A. oerlinghausenensis biosynthesized fumagillin and A. fischeri biosynthesized neither. These biochemical differences were reflected in sequence divergence of the intertwined fumagillin/pseurotin BGCs across genomes. These results delineate the similarities and differences in secondary metabolism-associated cards of virulence between a major fungal pathogen and its nonpathogenic closest relatives, shedding light onto the genetic and phenotypic changes associated with the evolution of fungal pathogenicity.

Identification of adulteration in botanical samples with untargeted metabolomics

Adulteration remains an issue in the dietary supplement industry, including botanical supplements. While it is common to employ a targeted analysis to detect known adulterants, this is difficult when little is known about the sample set. With this study, untargeted metabolomics using liquid chromatography coupled to ultraviolet-visible spectroscopy (LC-UV) or high-resolution mass spectrometry (LC-MS) was employed to detect adulteration in botanical dietary supplements. A training set was prepared by combining Hydrastis canadensis L. with a known adulterant, Coptis chinensis Franch., in ratios ranging from 5 to 95% adulteration. The metabolomics datasets were analyzed using both unsupervised (principal component analysis and composite score) and supervised (SIMCA) techniques. Palmatine, a known H. canadensis metabolite, was quantified as a targeted analysis comparison. While the targeted analysis was the most sensitive method tested in detecting adulteration, statistical analyses of the untargeted metabolomics datasets detected adulteration of the goldenseal samples, with SIMCA providing the greatest discriminating potential. Graphical abstract.

Suspect and non-targeted screening of chemicals of emerging concern for human biomonitoring, environmental health studies and support to risk assessment: From promises to challenges and harmonisation issues

Large-scale suspect and non-targeted screening approaches based on high-resolution mass spectrometry (HRMS) are today available for chemical profiling and holistic characterisation of biological samples. These advanced techniques allow the simultaneous detection of a large number of chemical features, including markers of human chemical exposure. Such markers are of interest for biomonitoring, environmental health studies and support to risk assessment. Furthermore, these screening approaches have the promising capability to detect chemicals of emerging concern (CECs), document the extent of human chemical exposure, generate new research hypotheses and provide early warning support to policy. Whilst of growing importance in the environment and food safety areas, respectively, CECs remain poorly addressed in the field of human biomonitoring. This shortfall is due to several scientific and methodological reasons, including a global lack of harmonisation. In this context, the main aim of this paper is to present an overview of the basic principles, promises and challenges of suspect and non-targeted screening approaches applied to human samples as this specific field introduce major specificities compared to other fields. Focused on liquid chromatography coupled to HRMS-based data acquisition methods, this overview addresses all steps of these new analytical workflows. Beyond this general picture, the main activities carried out on this topic within the particular framework of the European Human Biomonitoring initiative (project HBM4EU, 2017-2021) are described, with an emphasis on harmonisation measures.

Identification of quality control markers in Suhuang antitussive capsule based on HPLC-PDA fingerprint and anti-inflammatory screening

Suhuang antitussive capsule (SH), one of traditional Chinese patent medicines, has been widely used for treating cough variant asthma and postinfectious cough in clinic. The objective of this work is to identify the characteristic and active ingredients as the quality control markers for SH based on high performance liquid chromatography with photodiode array detector (HPLC-PDA) fingerprint and screening of anti-inflammatory components. Similarity analysis (SA), hierarchical clustering analysis (HCA) and principal component analysis (PCA) were used to evaluate 16 different batches of SH. 13 compounds accounting for 36% of the total components in the fingerprint were identified and semi-quantitatively analyzed, which anti-inflammatory activity was tested with the in vitro assay. The results showed that the established chemical fingerprint could clearly distinguish different batches of SH by SA, HCA, and PCA analysis. Furthermore, four known compounds (chlorogenic acid, schisandrin, angeloylgomisin H and praeruptorin A) were screened out to be the most discriminant variables, which could be applied to quality control of SH by quantitative analysis. The semi-quantitative results showed that six compounds were major components, i.e. arctiin (10.28 ± 3.18 mg/g), ephedrine (9.26 ± 1.58 mg/g), schisandrin (3.09 ± 0.83 mg/g), pseudoephedrine (2.34 ± 1.04 mg/g), schisandrin B (1.48 ± 0.16 mg/g), and 1-caffeoylquinic acid (1.36 ± 0.42 mg/g). The anti-inflammatory results showed that SH extract, praeruptorin A, schisandrin, arctigenin and pseudoephedrine could significantly inhibit inflammatory mediator NO production in LPS-stimulated RAW264.7 macrophages. These findings indicated that praeruptorin A, schisandrin, arctiin and pseudoephedrine could be proposed as the quality control markers for SH.

The effect of temperature on sulfate release from Pearl River sediments in South China

Sulfate (SO₄^2-) has received attention as means of monitoring water quality and pollution. However, the SO₄^2- content of rivers, lakes, and reservoirs varies significantly by season, so environmental factors such as temperature can affect it. An experiment was conducted with a series of aerobic and anaerobic tanks containing Pearl River sediments and distilled water to assess the release of SO₄^2- from sediments under controlled conditions. "Black-odor river" refers to near anoxic conditions in the water column and foul odors emanating from affected rivers in southeastern China. These river systems typical have sediments containing ammonia (NH₃), hydrogen sulfide (H₂S), and organic sulfide compounds in excess, and precipitates of sulfide (S^2-), with ferrous (Fe²⁺) or manganese (Mn²⁺). SO₄^2- concentration was measured at various depths in pore water and in the water column while controlling temperature and dissolved oxygen (DO) concentrations. Interpolation of study results revealed that SO₄^2- content was highest between temperatures of 20 °C and 25 °C. The relationship between SO₄^2- concentration, which varied with temperature and time, was fit using a linearized Michaelis-Menten function (R² = 0.69). The release of SO₄^2- to the water column was accelerated during the experiment (for temperatures higher than 20 °C), and led to higher SO₄^2- content in the water column than in pore water. The maximum concentration of SO₄^2- within the sediment occurred at a temperature of 20 °C. Comparing aerated and non-aerated tanks at 20 °C, we found that O₂ restricted SO₄^2- content in the water column; DO could, in turn, also be controlled by temperature. Fe²⁺ and Mn²⁺ had a negative correlation with SO₄^2-.

Composite score analysis for unsupervised comparison and network visualization of metabolomics data

Metabolomics-based approaches are becoming increasingly popular to interrogate the chemical basis for phenotypic differences in biological systems. Successful metabolomics studies employ multivariate data analysis to compare large and highly complex datasets. A primary tool for unsupervised statistical analyses, principal component analysis (PCA), relies on the selection of a subsection of a maximum of three components from a larger model to visually represent similarity. The use of only three principal components limits the comprehensiveness of the model and can mask discrimination between samples. We have developed a new statistical metric, the composite score (CS), as a univariate statistic that incorporates multiple principal components to calculate a correlation matrix that enables quantitative comparisons of sample similarity between samples within one dataset based upon measured metabolome profiles. Composite score values were tabulated using profiles of complex extracts of dietary supplements from the plant Hydrastis canadensis (goldenseal) as a case study. Several outliers were unambiguously identified, and a PCA composite score network was developed to provide a graphical representation of the composite score matrix. Comparison with visualization using PCA score plots or dendrograms from hierarchical clustering analysis (HCA) demonstrates the utility of the composite score to as a tool for metabolomics studies that seek to quantify similarity among samples. An R-script for the calculation of composite score has been made available.

Chemometrics: a complementary tool to guide the isolation of pharmacologically active natural products

Chemometrics offers an important complementary tool to enhance the searching and isolation of bioactive natural products from natural sources.

Metabolomics: a promising diagnostic and therapeutic implement for breast cancer

Breast cancer (BC) is the most commonly diagnosed cancer among women and the leading cause of cancer death. Despite the advent of numerous diagnosis and treatment methods in recent years, this heterogeneous disease still presents great challenges in early diagnosis, curative treatments and prognosis monitoring. Thus, finding promising early diagnostic biomarkers and therapeutic targets and approaches is meaningful. Metabolomics, which focuses on the analysis of metabolites that change during metabolism, can reveal even a subtle abnormal change in an individual. In recent decades, the exploration of cancer-related metabolomics has increased. Metabolites can be promising biomarkers for the screening, response evaluation and prognosis of BC. In this review, we summarized the workflow of metabolomics, described metabolite signatures based on molecular subtype as well as reclassification and then discussed the application of metabolomics in the early diagnosis, monitoring and prognosis of BC to offer new insights for clinicians in breast cancer diagnosis and treatment.

Simplify: A Mass Spectrometry Metabolomics Approach to Identify Additives and Synergists from Complex Mixtures

In fields ranging from environmental toxicology to drug discovery, it is critical to identify how multiple chemical compounds interact to perturb biological systems. Isolation-based approaches fail to incorporate multiconstituent interactions, such as synergy. We have developed an approach called "Simplify", which identifies mixture constituents that interact to achieve biological effects. Simplify combines biological and mass spectrometric data sets and uses an "activity index" to predict mixture interactions. Using the plant Salvia miltiorrhiza as a case study, we employed Simplify to identify four individual constituents that contribute to antimicrobial activity, three additives and one synergist. Our study is the first to enable identification of unknown synergists prior to isolating them, demonstrating the ability of the Simplify workflow to predict key contributors to the biological effect of a complex mixture. While utilized for natural products discovery in this study, this approach is expected to prove useful across multiple disciplines that rely on mixture analysis.

BLANKA: an Algorithm for Blank Subtraction in Mass Spectrometry of Complex Biological Samples

Multispecies microbiome systems are known to be closely linked to human, animal, and plant life processes. The growing field of metabolomics presents the opportunity to detect changes in overall metabolomic profiles of microbial species interactions. These metabolomic changes provide insight into function of metabolites as they correlate to different species presence and the observed phenotypic changes, but detection of subtle changes is often difficult in samples with complex backgrounds. Natural environments such as soil and food contain many molecules that convolute mass spectrometry-based analyses, and identification of microbial metabolites amongst environmental metabolites is an informatics problem we begin to address here. Our microbes are grown on solid or liquid cheese curd media. This medium, which is necessary for microbial growth, contains high amounts of salts, lipids, and casein breakdown products which make statistical analyses using LC-MS/MS data difficult due to the high background from the media. We have developed a simple algorithm to carry out background subtraction from microbes grown on solid or liquid cheese curd media to aid in our ability to conduct statistical analyses so that we may prioritize metabolites for further structure elucidation. Graphical Abstract .