One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks

New acorane-sesequiterpenes and anti-retinoblastoma constituents from the marine algicolous fungus Trichoderma harzianum NTU2180 guided by molecular networking strategy

BACKGROUND

Trichoderma species, known as biocontrol agents against plant diseases, contain diverse compounds, especially terpenoids, with various bioactivities. To facilitate the exploration of bioactive secondary metabolites of Trichoderma harzianum NTU2180, the OSMAC approach MS/MS molecular networking was applied in the current study.

RESULTS

The feature-based molecular networking (FBMN) analysis showed that T. harzianum NTU2180 fermented on germinated brown rice (GBR) produced more terpenoids. Here, two new acorane-sesequiterpenes, trichospirols A (1) and B (2), and 12 known compounds (3 - 14) were isolated from the EtOAc layer of T. harzianum NTU2180 fermentation on GBR. Structures of these compounds were determined through NMR, UV, IR, and MS analyses. The absolute configuration of trichospirols A (1) was also elucidated by x-ray with Cu K-α radiation. Among them, six compounds (1, 2, 3, 4, 5, and 11) were annotated as terpenoids by the NPClassifier on FBMN. 5-Hydroxy-3-hydroxmethyl-2-methyl-7-methoxychromone (7) and ergosterol peroxide (11) showed significant anti-angiogenic activity in ex vivo experiments with respective 0.57 ± 0.12- and 0.20 ± 0.12-fold changes. In addition, compound 11 displayed cytotoxicity against Y79 retinoblastoma cells with IC50 value of 35.3 ± 6.9 µM.

CONCLUSIONS

The current study utilizes FBMN concept with OSMAC approach to accelerate the exploration of potential metabolites of the fungus Trichoderma harzianum NTU2180. Through a series of FBMN-guided isolation and purification, two new acorane-sesequiterpenes and 12 known compounds were obtained. The ex vivo and in vitro experiments were evaluated to assess anticancer isolates. It is worth noting that compound 11 was identified as a dual inhibitor targeting both angiogenesis and proliferation of retinoblastomas. Altogether, the results revealed the novel potential of T. harzianum for developing natural therapeutics against retinoblastomas.

Assessment of the effects of cadmium, samarium and gadolinium on the blue mussel (Mytilus edulis): A biochemical, transcriptomic and metabolomic approach

Improving the understanding of how chemicals affect on organisms and assessing the associated environmental risks is of major interest in environmental studies. This can be achieved by using complementary approaches based on the study of the molecular responses of organisms. Because of the known chemical pressures on the environment, regulations on the content of some chemicals, such as cadmium, have been mostly completed. In contrast, the environmental toxicity of rare earth elements (REEs), which are widely used in industry, has only recently begun to receive attention. Here, we investigated the effects of cadmium, and two REEs, samarium and gadolinium, on marine mussels under laboratory exposures. We found that after an 8-day exposure at 500 µg/L, the metals were bioaccumulated by the mussels. Furthermore, samarium and gadolinium affected two oxidative stress biomarkers, GST and SOD. Lipidomic analysis showed that lipid content was modulated by the REEs, but not by cadmium. Interestingly, several compounds belonging to the phosphoinositide metabolism were more abundant, suggesting a pro-mitotic or cell survival response, while a higher abundance of cardiolipins after samarium exposure suggested an alteration of mitochondrial activity. Moreover, depending on the tissue and the metal considered, transcriptional analyses revealed an effect on metallothionein, hsp70/90, energy metabolism enzymes, as well as pro-mitotic transcript accumulation. Thus, this study sheds a new light on metal toxicity and in particularl REEs by highlighting the accumulation and toxicity of cadmium, samarium and gadolinium at 500 µg/L at different molecular levels, from gene expression to the lipidome of blue mussels.

Identification and occurrence of microcystins in freshwaters and fish from a eutrophic dam through LC-HRMS

Microcystins (MCs) are cyclic heptapeptides originating from various cyanobacteria in eutrophic aquatic environments. Their potential consequences on ecosystems and public health underscores the need to explore MCs' occurrence. In this study, liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) analysis and a suspect screening workflow supported by open-source tools were employed for the determination of MCs in freshwater and biota samples from a eutrophic dam in Uruguay. MS2 spectra were acquired using higher-energy collisional dissociation (HCD), data-dependent acquisition (DDA), and stepped NCE of [M + H]+ and [M + 2H]2+ ions. In addition to the confirmation of 3 MCs in the samples, a comprehensive study of the acquired spectra led to the tentative identification of 30 MCs, including 2 partially described variants not previously reported. 33 MCs were determined in freshwaters, exhibiting a maximum MC concentration in a sample of 12731 μg/L. Regarding fish, the 61 % of the samples exhibited at least a positive determination. 8 MCs were detected and [D-Leu1]MC-LR, [seco-4/5]MC-LR, MC-LR, MC-RR, MC-WR, and [D-Asp3]MC-RR could be semi-quantified (3-127 μg/kg, w.w). In 2 samples, the MC-LR content per 100 g of fish was found to be close to 80 % the tolerable daily intake for chronic exposure recommended by the WHO. The identification of [seco-4/5]MC-LR in biota highlights the labile byproducts of MCs and the need for wide-scope analytical approaches. This study emphasizes the extensive range of MCs present in eutrophic freshwater environments, their accumulation in exposed biota, and their potential entry into the food web.

Nextflow4MS-DIAL: A Reproducible Nextflow-Based Workflow for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing

Reproducibility in untargeted metabolomics data processing remains a significant challenge due to software limitations and the complex series of steps required. To address these issues, we developed Nextflow4MS-DIAL, a reproducible workflow for liquid chromatography-mass spectrometry (LC-MS) metabolomics data processing, validated with publicly available data from MetaboLights (MTBLS733). Nextflow4MS-DIAL automates LC-MS data processing to minimize human errors from manual data handling. The workflow supports software containerization, ensuring computational reproducibility and enabling collaborative research. Nextflow4MS-DIAL is compatible with any Unix-like system and supports multiple job schedulers, offering flexibility and ease of use. The Nextflow4MS-DIAL workflow is available under the permissive MIT license: https://github.com/Nextflow4Metabolomics/nextflow4ms-dial.

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.

Feature-based molecular networking and MS2LDA analysis for the dereplication of adjacent bis-tetrahydrofuran Annonaceous acetogenins

INTRODUCTION

Annonaceous acetogenins are a group of natural polyketide compounds possessing notable cytotoxic and antitumor properties. Mass spectrometry (MS) techniques can be used for the structural determination of these compounds, including the location of functional groups along the long alkyl chain.

OBJECTIVE

This study aims to develop a convenient liquid chromatography (LC)-MS-based method for the dereplication of acetogenins in plant extracts using a molecular networking approach.

METHODOLOGY

The LC-electrospray ionization (ESI)-MS/MS spectra of pure adjacent bis-tetrahydrofuran (THF) acetogenins isolated from Uvaria rufa (Annonaceae) were acquired, along with those of the crude ethyl acetate and hexanes fractions of the plant extract, followed by dereplication and molecular networking analysis using the Global Natural Products Social Molecular Networking (GNPS) platform.

RESULTS

A high level of fragmentation of the protonated molecules [M + H]+ was observed at collision energies of 37.5 and 25.0 eV. The application of feature-based molecular networking (FBMN) allowed for distinguishing diastereoisomers based on different retention times in the reversed-phase high-performance liquid chromatography method. The acetogenin possessing one or more additional OH groups on the methyl-terminal chain side of the OH-flanked bis-THF ring unit were grouped separately from those lacking such substructure. Furthermore, the MS2LDA analysis revealed shared Mass2Motifs among acetogenins, confirming the structural relations within the molecular network.

CONCLUSIONS

The ESI-MS/MS-based molecular networking method provided an effective strategy for the dereplication of acetogenins in plant extracts. It is anticipated that this molecular networking approach could be extended to other types of acetogenins to facilitate rapid identification of this class of compounds.

Identification of compounds contributing to glucocorticoid activity in indoor dust supported by orthogonal fractionation

Indoor dust contains various endocrine-disrupting contaminants, yet the effect drivers of observed glucocorticoid activity are completely unknown. This study conducted an effect-directed analysis using orthogonal fractionation to identify effect drivers of glucocorticoid activity in indoor dust. After the detection of bioactivity using a human cell line stably transfected with a reporter gene, the sample underwent parallel HPLC fractionations with octadecyl, pentafluorophenyl, and aminopropyl columns to obtain orthogonal fractions. The bioassays were utilized to screen the fractions and guide efforts towards prioritization of the bioactive chemicals using targeted and non-targeted analysis with LC-HRMS. The glucocorticoid activity of the identified potential candidates was confirmed by their testing in the same bioassay. To assess their contribution to the detected mixture effects, we calculated their relative potencies. This approach led to the identification of two pharmaceuticals, clobetasol propionate and mometasone furoate, at concentrations ranging from ng to μg per gram of dust, which together accounted for up to 77% of the observed glucocorticoid activity. This is the first report documenting the effect drivers of glucocorticoid receptor agonism in indoor dust; however, together with previous studies of various environmental samples, it documents that in cases when glucocorticoid receptor-agonistic activity is detected, drugs should be considered as likely relevant contaminants. The discovery of potent drugs in household dust highlights concerns for individuals exposed within domestic environments and emphasizes the need to consider pharmaceuticals as relevant contributors to indoor contamination.

Protein-metabolite Interactions Based on Chemical Targeting Methods

The importance of the protein-metabolite interaction network extends beyond its relevance to life sciences focused on proteins, it also profoundly influences its mechanisms related to disease targets, drug screening, and clinical diagnosis and treatment. Research methods targeting protein-metabolite interaction focus on enhancing the detectable signals of specific interactions by examining the structural characteristics of both proteins and metabolites in conjunction with chemical molecules, playing a crucial role in elucidating the protein-metabolite interaction network. Consequently, this article outlines several chemical targeting strategies developed in recent years and provides examples of their applications in the discovery and interpretation of new protein-metabolite interaction pathways. Finally, a brief summary will be presented regarding technological advances, research prospects, and current challenges of protein-metabolite interaction research.

Longitudinal analyses of infants' microbiome and metabolome reveal microbes and metabolites with seemingly coordinated dynamics

Population studies have shown that the infant's microbiome and metabolome undergo significant changes in early childhood. However, no previous study has investigated how diverse these changes are across subjects and whether the subject-specific dynamics of some microbes correlate with the over-time dynamics of specific metabolites. Using mixed-effects models, and data from the ABC study, we investigated the early childhood dynamics of fecal microbiome and metabolome and identified 83 amplicon sequence variants (ASVs) and 753 metabolites with seemingly coordinated trajectories. Enrichment analysis of these microbes and molecules revealed eight ASV families and 23 metabolite groups involving 1032 ASV-metabolite pairs with their presence-absence changing in a coordinated fashion. Members of the Lachnospiraceae (464/1032) and metabolites related to cholestane steroids (309/1032) dominated proportional shifts within the fecal microbiome and metabolome as infants aged.

A fungal phospholipase C involved in the degradation of plant glycosylinositol phosphorylceramides during Arabidopsis/Botrytis interaction

This study investigates the presence and significance of phosphorylated oligosaccharides that accumulate during the interaction between Arabidopsis thaliana and Botrytis cinerea, a necrotrophic fungus that poses a major threat to crops worldwide. While previous research has extensively characterized cell wall-derived molecules during fungal infection, the role of plasma membrane-derived ones remains unclear. Here, we reveal the discovery of inositol phosphate glycans (IPGs) released during infection, originating from plant sphingolipids, specifically glycosylinositol phosphorylceramides (GIPC). Advanced chromatography, mass spectrometry techniques and molecular biology were employed to identify these IPGs, and determine their origins. In addition to the well-characterized role of B. cinerea in releasing cell wall-degrading enzymes, this research suggests that B. cinerea's enzymatic machinery may also target the degradation of the plant plasma membrane. As a consequence of this, IPGs identical to those generated by the host plant are released, most likely due to activity of a putative phospholipase C that acts on GIPC plasma membrane lipids. These insights could pave the way for developing new strategies to enhance crop resistance by focusing on membrane integrity in addition to cell wall fortification.

Divergent Metabolic Fates of Aromatic Amino Acid-Derived Isomers: Insights from Ex Vivo Metabolomics and HDX-HRMS/MS-Based Resolution of Tautomers

Tautomers are one of the many types of isomers, and differences in tautomeric structures confer altered chemical and biological properties. Using ultrahigh-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) ex vivo metabolomics, we investigate, in whole blood, the divergent metabolism of enol and keto forms of indole-3-pyruvate (IPyA), a tautomeric product of aromatic amino acid metabolism. Two new compounds resulting from IPyA metabolism were discovered, 3-(1H-indol-3-yl)-2,3-dioxopropanoic acid or "indole-3-oxopyruvic acid" and glutathionyl-indole pyruvate (GSHIPyA), which were characterized via ultraviolet photodissociation (UVPD) and higher-energy collisional dissociation (HCD). Computational calculations support the hypothesis that GSHIPyA forms specifically through the enol form of IPyA. GSHIPyA is also hypothesized to be tautomeric, and a hydrogen-deuterium exchange-high-resolution tandem mass spectrometry (HDX-HRMS/MS) approach is developed to prove the presence of an enol and keto tautomer. HDX of GSHIPyA labels the keto form with an additional deuterium, relative to the enol form. HRMS/MS of the labeled isomers is employed to leverage the relationship of resolving power scaling inversely with the square root of m/z, for Orbitrap mass analyzers. HRMS/MS yields a smaller-molecular-weight deuterated tautomeric product ion, reducing the analyte ion m/z and thus lowering the resolving power necessary to separate the deuterated keto tautomer product ion from the [13]C product ion.

Feature-Based Molecular Network for New Psychoactive Substance Identification: The Case of Synthetic Cannabinoids in a Seized e-Liquid and Biological Samples

The comprehensive detection of new psychoactive substances, including synthetic cannabinoids along with their associated metabolites in biological samples, remains an analytical challenge. To detect these chemicals, untargeted approaches using appropriate bioinformatic tools such as molecular networks are useful, albeit it necessitates as a prerequisite the identification of a node of interest within the cluster. To illustrate it, we reported in this study the identification of synthetic cannabinoids and some of their metabolites in seized e-liquid, urine, and hair collected from an 18-year-old poisoned patient hospitalized for neuropsychiatric disorders. A comprehensive analysis of the seized e-liquid was performed using gas chromatography coupled with electron ionization mass spectrometry, 1H NMR, and liquid chromatography coupled with high resolution tandem mass spectrometry combined with data processing based on molecular network strategy. It allowed researchers to detect in the e-liquid known synthetic cannabinoids including MDMB-4en-PINACA, EDMB-4en-PINACA, MMB-4en-PINACA, and MDMB-5F-PICA. Compounds corresponding to transesterification of MDMB-4en-PINACA with pentenol, glycerol, and propylene glycol were also identified. Regarding the urine sample of the patient, metabolites of MDMB-4en-PINACA were detected, including MDMB-4en-PINACA butanoic acid, dihydroxylated MDMB-4en-PINACA butanoic acid, and glucurono-conjugated MDMB-4en-PINACA butanoic acid. Hair analysis of the patient allowed the detection of MDMB-4en-PINACA and MDMB-5F-PICA in the two investigated hair segments. This untargeted analysis of seized materials and biological samples demonstrates the utility of the molecular network strategy in identifying closely related compounds and metabolites of synthetic cannabinoids. It also emphasizes the need for developing strategies to anchor molecular networks, especially for new psychoactive substances.

LC-MS/MS-based phospholipid profiling of plant-pathogenic bacteria with tailored separation of methyl-branched species

Plant-pathogenic bacteria are one of the major constraints on agricultural yield. In order to selectively treat these bacteria, it is essential to understand the molecular structure of their cell membrane. Previous studies have focused on analyzing hydrolyzed fatty acids (FA) due to the complexity of bacterial membrane lipids. These studies have highlighted the occurrence of branched-chain fatty acids (BCFA) alongside normal-chain fatty acids (NCFA) in many bacteria. As several FA are bound in the intact phospholipids of the bacterial membrane, the presence of isomeric FA complicates lipid analysis. Furthermore, commercially available reference standards do not fully cover potential lipid isomers. To address this issue, we have developed a reversed-phase high-performance liquid chromatography (RP-HPLC) method with tandem mass spectrometry (MS/MS) to analyze the phospholipids of various plant-pathogenic bacteria with a focus on BCFA containing phospholipids. The study revealed the separation of three isomeric phosphatidylethanolamines (PE) depending on the number of bound BCFA to NCFA. The validation of the retention order was based on available reference standards in combination with the analysis of hydrolyzed fatty acids through gas chromatography with mass spectrometry (GC/MS) after fractionation. Additionally, the transferability of the retention order to other major lipid classes, such as phosphatidylglycerols (PG) and cardiolipins (CL), was thoroughly examined. Using the information regarding the retention behavior, the phospholipid profile of six plant-pathogenic bacteria was structurally elucidated. Furthermore, the developed LC-MS/MS method was used to classify the plant-pathogenic bacteria based on the number of bound BCFA in the phospholipidome.

Identifying Quinones in Complex Aqueous Environmental Media (Biochar Extracts) through Tagging with Cysteine and Cysteine-Contained Peptides and High Resolution Mass Spectrometry Analysis

Quinones are among the most important components in natural organic matter (NOM) for redox reactions; however, no quinones in complex environmental media have been identified. To aid the identification of quinone-containing molecules in ultracomplex environmental samples, we developed a chemical tagging method that makes use of a Michael addition reaction between quinones and thiols (-SH) in cysteine (Cys) and cysteine-contained peptides (CCP). After the tagging, candidates of quinones in representative aqueous environmental samples (water extractions of biochar) were identified through high-resolution mass spectrometry (HRMS) analysis. The MS and UV spectra analysis showed rapid reactions between Cys/CCP and model quinones with β-carbon from the same benzene ring available for Michael addition. The tagging efficiency was not influenced by other co-occurring nonquinone representative compounds, including caffeic acid, cinnamic acid, and coumaric acid. Cys and CCP were used to tag quinones in water extractions of biochars, and possible candidates of quinones (20 and 53 based on tagging with Cys and CCP, respectively) were identified based on the HRMS features for products of reactions with Cys/CCP. This study has successfully demonstrated that such a Michael addition reaction can be used to tag quinones in complex environmental media and potentially determine their identities. The method will enable an in-depth understanding of the redox chemistry of NOM and its critical chemical compositions and structures.

Application of a molecular networking approach using LC-HRMS combined with the MetWork webserver for clinical and forensic toxicology

Backgrounds and aims

In toxicology, LC-HRMS for untargeted screening yields a great deal of high quality spectral data. However, there we lack tools to visualize/organize the MS data. We applied molecular networking (MN) to untargeted screening interpretation. Our aims were to compare theoretical MS libraries obtained in silico with our experimental dataset in patients to broaden its application, and to use the MetWork web application for metabolite identification.

Methods

Samples were analyzed using an LC-HRMS system. For MN, data was generated using MZmine, and analyzed and visualized using MetGem. MetWork annotations were filtered and this file was used for annotation of the previously obtained MN.

Results

155 compounds including drugs found in patients were recorded. Using this dataset, we confirmed in 60 patients intake of tramadol, amitriptyline bromazepam, and cocaine. The results obtained by the reference methods were confirmed by MN approaches. Eighty percent of the compounds were common to both conventional and MN approaches. Using MetWork, metabolites and parent drugs such as amitriptyline, its metabolite nortriptyline and amitriptyline glucuronide phase 2 metabolites were anticipated and proposed as putative annotations.

Conclusion

The workflow increases confidence in toxicological screening by highlighting putative structures in biological matrices in combination with CFM-ID (Competitive Fragmentation Modeling for Metabolite Identification) and MetWork to extend the annotation of potential drugs even without a reference standard.

Impact of three different peak picking software tools on the quality of untargeted metabolomics data

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Identification of Granatane Alkaloids from Duboisia myoporoides (Solanaceae) using Molecular Networking and Semisynthesis

The Solanaceae plant family contains at least 98 genera and over 2700 species. The Duboisia genus stands out for its ability to produce pyridine and tropane alkaloids, which are relatively poorly characterized at the phytochemical level. In this study, we analyzed dried leaves of Duboisia spp. using supercritical CO2 extraction and ultra-high-pressure liquid chromatography coupled to high-resolution tandem mass spectrometry, followed by feature-based molecular networking. Thirty-one known tropane alkaloids were putatively annotated, and the identity of six (atropine, scopolamine, anisodamine, aposcopolamine, apoatropine, and noratropine) were identified using reference standards. Two new granatane alkaloids connected in the molecular network were highlighted from Duboisia myoporoides, and their α-granatane tropate and α-granatane isovalerate structures were unambiguously established by semisynthesis.

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin-Iron Complexes: Novel Pipeline and Complex Structures

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeLxCly (L: organic ligand, x = 1-4, y = 0-3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

Discovery of the Lipopeptides Albubactins A-H from Streptomyces albidoflavus RKJM0023 via Chemical Elicitation with Rhamnolipids and Synthesis of Albubactin A

The marine tunicate-derived Streptomyces albidoflavus RKJM0023 was cultured in the presence of a rhamnolipid mixture in an effort to elicit the production of silent natural products. MS/MS-based molecular networking analysis enhanced with nonparametric statistics highlighted the upregulation of a molecular cluster (Kruskal-Wallis p = 1.6 e-6 for 1) in which no MS/MS features had library matches. Targeted isolation of these features resulted in the discovery of nine new N-acylated lipopeptides, albubactins A-H (1-8) each containing a unique glutamine tripeptide and a C-terminal ethyl ester moiety. Three related albubactin acids A-C (9-11) lacking the ethyl ester were also identified. NMR spectroscopy and UPLC-HR-ESI-MS/MS demonstrated that the albubactins were obtained as mixtures that shared a common m/z and differed only in their acylated terminal groups. Due to the complex spectroscopic elucidation with many overlapping shifts, a total synthesis of albubactin A (1) was completed and used to determine the absolute configuration of the new albubactins.

Unveiling the fungal diversity and associated secondary metabolism on black apples

Black apples are the result of late-stage microbial decomposition after falling to the ground. This phenomenon is highly comparable from year to year, with the filamentous fungus Monilinia fructigena most commonly being the first invader, followed by Penicillium expansum. Motivated by the fact that only little chemistry has been reported from apple microbiomes, we set out to investigate the chemical diversity and potential ecological roles of secondary metabolites (SMs) in a total of 38 black apples. Metabolomics analyses were conducted on either whole apples or small excisions of fungal biomass derived from black apples. Annotation of fungal SMs in black apple extracts was aided by the cultivation of 15 recently isolated fungal strains on 9 different substrates in a One Strain Many Compounds (OSMAC) approach, leading to the identification of 3,319 unique chemical features. Only 6.4% were attributable to known compounds based on analysis of high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS/MS) data using spectral library matching tools. Of the 1,606 features detected in the black apple extracts, 32% could be assigned as fungal-derived, due to their presence in the OSMAC-based training data set. Notably, the detection of several antifungal compounds indicates the importance of such compounds for the invasion of and control of other microbial competitors on apples. In conclusion, the diversity and abundance of microbial SMs on black apples were found to be much higher than that typically observed for other environmental microbiomes. Detection of SMs known to be produced by the six fungal species tested also highlights a succession of fungal growth following the initial invader M. fructigena.IMPORTANCEMicrobial secondary metabolites constitute a significant reservoir of biologically potent and clinically valuable chemical scaffolds. However, their usefulness is hampered by rapidly developing resistance, resulting in reduced profitability of such research endeavors. Hence, the ecological role of such microbial secondary metabolites must be considered to understand how best to utilize such compounds as chemotherapeutics. Here, we explore an under-investigated environmental microbiome in the case of black apples; a veritable "low-hanging fruit," with relatively high abundances and diversity of microbially produced secondary metabolites. Using both a targeted and untargeted metabolomics approach, the interplay between metabolites, other microbes, and the apple host itself was investigated. This study highlights the surprisingly low incidence of known secondary metabolites in such a system, highlighting the need to study the functionality of secondary metabolites in microbial interactions and complex microbiomes.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

Indole-3-pyruvate: Analysis and Control of Tautomerism and Reactivity

It is well-known in biochemistry that structure confers function, meaning that chemical structural elucidation is critical to truly understanding the function of a given metabolite. Indole-3-pyruvate (IPyA) exists in an equilibrium between the keto and enol tautomeric forms. IPyA is suggested to play a role in immune function; however, determining whether the tautomeric forms function differently can only be studied if an analytical method is capable of distinguishing between the two forms. Herein, we describe the use of UHPLC-HRMS to gain insight into the physical variables that govern IPyA tautomer equilibrium, reactivity, and detection limit. We use hydrogen-deuterium exchange (HDX) to identify enol and keto peaks, and we show that tautomers exhibit a valley of fronting followed by a tailing peak shape (though separation is still attainable) and identical MS/MS spectra. We observed drastically different ratios of keto and enol forms in different solvents, which is an important consideration for in vitro studies. IPyA was found to be highly unstable with accelerated reactivity in peroxides. Through in vitro reactivity studies, IPyA produced a myriad of known and unknown metabolites via nonenzymatic processes, many of which were mapped in vivo via the analysis of human plasma. Finally, we show that vitamin C (ascorbic acid) can slow this reactivity and enable sensitive detection in whole blood.

AntDAS-GCMS: A New Comprehensive Data Analysis Platform for GC-MS-Based Untargeted Metabolomics with the Advantage of Addressing the Time Shift Problem

Over the years, a number of state-of-the-art data analysis tools have been developed to provide a comprehensive analysis of data collected from gas chromatography-mass spectrometry (GC-MS). Unfortunately, the time shift problem remains unsolved in these tools. Here, we developed a novel comprehensive data analysis strategy for GC-MS-based untargeted metabolomics (AntDAS-GCMS) to perform total ion chromatogram peak detection, peak resolution, time shift correction, component registration, statistical analysis, and compound identification. Time shift correction was specifically optimized in this work. The information on mass spectra and elution profiles of compounds was used to search for inherent landmarks within analyzed samples to resolve the time shift problem across samples efficiently and accurately. The performance of our AntDAS-GCMS was comprehensively investigated by using four complex GC-MS data sets with various types of time shift problems. Meanwhile, AntDAS-GCMS was compared with advanced GC-MS data analysis tools and classic time shift correction methods. Results indicated that AntDAS-GCMS could achieve the best performance compared to the other methods.

Deep learning framework for peak detection at the intact level of therapeutic proteins

While automated peak detection functionalities are available in commercially accessible software, achieving optimal true positive rates frequently necessitates visual inspection and manual adjustments. In the initial phase of this study, hetero-variants (glycoforms) of a monoclonal antibody were distinguished using liquid chromatography-mass spectrometry, revealing discernible peaks at the intact level. To comprehensively identify each peak (hetero-variant) in the intact-level analysis, a deep learning approach utilizing convolutional neural networks (CNNs) was employed in the subsequent phase of the study. In the current case study, utilizing conventional software for peak identification, five peaks were detected using a 0.5 threshold, whereas seven peaks were identified using the CNN model. The model exhibited strong performance with a probability area under the curve (AUC) of 0.9949, surpassing that of partial least squares discriminant analysis (PLS-DA) (probability AUC of 0.8041), and locally weighted regression (LWR) (probability AUC of 0.6885) on the data acquired during experimentation in real-time. The AUC of the receiver operating characteristic curve also illustrated the superior performance of the CNN over PLS-DA and LWR.

Untargeted metabolomics and metagenomics reveal signatures for intramammary ceftiofur treatment and lactation stage in the cattle hindgut

The gut microbiota in cattle is essential for protein, energy, and vitamin production and hence, microbiota perturbations can affect cattle performance. This study evaluated the effect of intramammary (IMM) ceftiofur treatment and lactation stage on the functional gut microbiome and metabolome. Forty dairy cows were enrolled at dry-off. Half received IMM ceftiofur and a non-antibiotic teat sealant containing bismuth subnitrate (cases), while the other half received the teat sealant (controls). Fecal samples were collected before treatment at dry off, during the dry period (weeks 1 and 5) and the first week after calving (week 9). Shotgun metagenomic sequencing was applied to predict microbial metabolic pathways whereas untargeted metabolomics was used identify polar and nonpolar metabolites. Compared to controls, long-term changes were observed in the cows given ceftiofur, including a lower abundance of microbial pathways linked to energy production, amino acid biosynthesis, and other vital molecules. The metabolome of treated cows had elevated levels of stachyose, phosphatidylethanolamine diacylglycerol (PE-DAG), and inosine a week after the IMM ceftiofur application, indicating alterations in microbial fermentation, lipid metabolism, energy, and cellular signaling. Differences were also observed by sampling, with cows in late lactation having more diverse metabolic pathways and a unique metabolome containing higher levels of histamine and histamine-producing bacteria. These data illustrate how IMM ceftiofur treatment can alter the functionality of the hindgut metabolome and microbiome. Understanding how antibiotics and lactation stages, which are each characterized by unique diets and physiology, impact the function of resident microbes is critical to define normal gut function in dairy cattle.

Shifts in the functional capacity and metabolite composition of the gut microbiome during recovery from enteric infection

While enteric pathogens have been widely studied for their roles in causing foodborne infection, their impacts on the gut microbial community have yet to be fully characterized. Previous work has identified notable changes in the gut microbiome related to pathogen invasion, both taxonomically and genetically. Characterization of the metabolic landscape during and after enteric infection, however, has not been explored. Consequently, we investigated the metabolome of paired stools recovered from 60 patients (cases) during and after recovery from enteric bacterial infections (follow-ups). Shotgun metagenomics was applied to predict functional microbial pathways combined with untargeted metametabolomics classified by Liquid Chromatography Mass Spectrometry. Notably, cases had a greater overall metabolic capacity with significantly higher pathway richness and evenness relative to the follow-ups (p<0.05). Metabolic pathways related to central carbon metabolism, amino acid metabolism, and lipid and fatty acid biosynthesis were more highly represented in cases and distinct signatures for menaquinone production were detected. By contrast, the follow-up samples had a more diverse metabolic landscape with enhanced richness of polar metabolites (p<0.0001) and significantly greater richness, evenness, and overall diversity of nonpolar metabolites (p<0.0001). Although many metabolites could not be annotated with existing databases, a marked increase in certain clusters of metabolites was observed in the follow-up samples when compared to the case samples and vice versa. These findings suggest the importance of key metabolites in gut health and recovery and enhance understanding of metabolic fluctuations during enteric infections.

qPeaks: A Linear Regression-Based Asymmetric Peak Model for Parameter-Free Automatized Detection and Characterization of Chromatographic Peaks in Non-Target Screening Data

We present qPeaks (quality peaks), a novel, user-parameter-free algorithm for peak detection and peak characterization applicable to chromatographic data. The algorithm is based on a linearizable regression model that analyzes asymmetric peaks and estimates the specific uncertainties associated with the peak regression parameters. The uncertainties of the parameters are used to derive a data quality score DQSpeak, rendering low reliability results more transparent during processing and allowing for the prioritization of generated features. High DQSpeak chromatographic peaks have a lower chance of being classified as false-positive and show higher repeatability over multiple measurements. The high efficiency of the algorithm makes it particularly useful for application within processing routines of nontarget screening through chromatography coupled with high-resolution mass spectrometry. qPeaks is integrated into the qAlgorithms nontarget screening processing toolbox and appends a parameter-free chromatographic peak detection and characterization step to it. With qAlgorithms, now high-resolution mass spectra are centroided using the qCentroids algorithms, centroids are clustered to form extracted ion chromatograms (EICs) with the qBinning algorithm, and chromatographic peaks are found on the generated EICs with qPeaks. However, all tools from qAlgorithms can also be used independently.

Targeting of Antifungal Metabolites from Grapevine Byproducts by UPLC-HRMS/MS Approaches Using Bioactivity-Based Molecular Networking

This study focuses on countering Fusarium graminearum, a harmful fungal pathogen impacting cereal crops and human health through mycotoxin production. These mycotoxins, categorized as type B trichothecenes, pose significant health risks. Research explores natural alternatives to synthetic fungicides, particularly investigating phenolics in grapevine byproducts. Thirteen eco-extracts from five French grape varieties (Merlot, Cabernet Sauvignon, Sauvignon blanc, Tannat, and Artaban) exhibited substantial antifungal properties, with ten extracts displaying remarkable effects. Extracts from grapevine stems and roots notably reduced fungal growth by over 91% after five days. Through UHPLC-HRMS/MS analysis and metabolomics, the study identified potent antifungal compounds such as ampelopsin A and cyphostemmin B, among other oligomeric stilbenes. Interestingly, this approach showed that flavan-3-ols have been identified as markers for extracts that induce fungal growth. Root extracts from rootstocks, rich in oligostilbenes, demonstrated the highest antifungal activity. This research underscores grapevine byproducts' potential both as a sustainable approach to control F. graminearum and mycotoxin contamination in cereal crops and the presence of different metabolites from the cultivars of grapevine, suggesting different activities.

A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery

The ENPKG framework organizes large heterogeneous metabolomics data sets as a knowledge graph, offering exciting opportunities for drug discovery and chemodiversity characterization.

Chemical diversity of Brittonodoxa subpinnata, a Brazilian native species of moss

Plants should be probably thought of as the most formidable chemical laboratory that can be exploited for the production of an incredible number of molecules with remarkable structural and chemical diversity that cannot be matched by any synthetic libraries of small molecules. The bryophytes chemistry has been neglected for too long, but in the last ten years, this scenery is changing, with several studies being made using extracts from bryophytes, aimed at the characterization of interesting metabolites, with their metabolome screened. The main objective of this study was to analyze the metabolome of Brittonodoxa subpinnata, a native Brazilian moss species, which occurs in the two Brazilian hotspots. GC-MS and LC-MS2 were performed. All extracts were analyzed using the molecular networking approach. The four extracts of B. subpinnata (polar, non-polar, soluble, and insoluble) resulted in 928 features detected within the established parameters. 189 (20.4%) compounds were annotated, with sugars, fatty acids, flavonoids, and biflavonoids as the major constituents. Sucrose was the sugar with the highest quantity; palmitic acid the major fatty acid but with great presence of very long-chain fatty acids rarely found in higher plants, glycosylated flavonoids were the major flavonoids, and biflavonoids majorly composed by units of flavones and flavanones, exclusively found in the cell wall. Despite the high percentage, this work leaves a significant gap for future works using other structure elucidation techniques, such as NMR.

Computational Methods Reveal a Series of Cyclic and Linear Lichenysins and Surfactins from the Vietnamese Marine Sediment-Derived Streptomyces Strain G222

The Streptomyces strain G222, isolated from a Vietnamese marine sediment, was confidently identified by 16S rRNA gene sequencing. Its AcOEt crude extract was successfully analyzed using non-targeted LC-MS/MS analysis, and molecular networking, leading to a putative annotation of its chemical diversity thanks to spectral libraries from GNPS and in silico metabolite structure prediction obtained from SIRIUS combined with the bioinformatics tool conCISE (Consensus Annotation Propagation of in silico Elucidations). This dereplication strategy allowed the identification of an interesting cluster of a series of putative cyclic and linear lipopeptides of the lichenysin and surfactin families. Lichenysins (3-7) were isolated from the sub-fraction, which showed significant anti-biofilm activity against Pseudomonas aeruginosa MUC-N1. Their structures were confirmed by detailed 1D and 2D NMR spectroscopy (COSY, HSQC, HMBC, TOCSY, ROESY) recorded in CD3OH, and their absolute configurations were determined using the modified Marfey's method. The isolated lichenysins showed anti-biofilm activity at a minimum concentration of 100 µM. When evaluated for antibacterial activity against a panel of Gram-positive and Gram-negative strains, two isolated lichenysins exhibited selective activity against the MRSA strain without affecting its growth curve and without membranotropic activity. This study highlights the power of the MS/MS spectral similarity strategy using computational methods to obtain a cross-validation of the annotated molecules from the complex metabolic profile of a marine sediment-derived Streptomyces extract. This work provides the first report from a Streptomyces strain of combined cyclic and linear lichenysins and surfactins, known to be characteristic compounds of the genus Bacillus.

Momordica charantia fruit reduces plasma fructosamine whereas stems and leaves increase plasma insulin in adult mildly diabetic obese Göttingen Minipigs

BACKGROUND

Traditionally Momordica charantia (Bitter gourd) is known for its blood glucose lowering potential. This has been validated by many previous studies based on rodent models but human trials are less convincing and the physiological mechanisms underlying the bioactivity of Bitter gourd are still unclear. The present study compared the effects of whole fruit or stems-leaves from five different Bitter gourd cultivars on metabolic control in adult diabetic obese Göttingen Minipigs.

METHODS

Twenty streptozotocin-induced diabetic (D) obese Minipigs (body weight ~85 kg) were subdivided in mildly and overtly D pigs and fed 500 g of obesogenic diet per day for a period of three weeks, supplemented with 20 g dried powdered Bitter gourd or 20 g dried powdered grass as isoenergetic control in a cross-over, within-subject design.

RESULTS

Bitter gourd fruit from the cultivars "Palee" and "Good healthy" reduced plasma fructosamine concentrations in all pigs combined (from 450±48 to 423±53 and 490±50 to 404±48 μmol/L, both p<0.03, respectively) indicating improved glycemic control by 6% and 17%. These effects were statistically confirmed in mildly D pigs but not in overtly D pigs. In mildly D pigs, the other three cultivars of fruit showed consistent numerical but no significant improvements in glycemic control. The composition of Bitter gourd fruit was studied by metabolomics profiling and analysis identified three metabolites from the class of triterpenoids (Xuedanoside H, Acutoside A, Karaviloside IX) that were increased in the cultivars "Palee" (>3.9-fold) and "Good healthy" (>8.9-fold) compared to the mean of the other three cultivars. Bitter gourd stems and leaves from the cultivar "Bilai" increased plasma insulin concentrations in all pigs combined by 28% (from 53±6 to 67±9 pmol/L, p<0.03). The other two cultivars of stems and leaves showed consistent numerical but no significant increases in plasma insulin concentrations. The effects on plasma insulin concentrations were confirmed in mildly D pigs but not in overtly D pigs.

CONCLUSIONS

Fruits of Bitter gourd improve glycemic control and stems-leaves of Bitter gourd increase plasma insulin concentrations in an obese pig model for mild diabetes. The effects of Bitter gourd fruit on glycemic control seem consistent but relatively small and cultivar specific which may explain the varying results of human trials reported in the literature.

Chemical Profile of Ocotea delicata (Lauraceae) Using Ultra High-Performance Liquid Chromatography-High-Resolution Mass Spectrometry-Global Natural Products Social Molecular Networking Workflow

Ocotea, the largest genus in the Lauraceae family, encompasses numerous species of scientific interest. However, most Ocotea species have only been described morphologically. This study used an untargeted metabolomics workflow with UHPLC-HRMS and GNPS-FBMN to provide the first chemical evaluation of the polar specialized metabolites of O. delicata leaves. Leaves from three O. delicata specimens were extracted using ultrasound-assisted extraction with 70% ethanol. Among the examined samples, 44 metabolites, including alkaloids and flavonoids, were identified. In contrast to other Ocotea species, O. delicata has a wider diversity of kaempferol derivatives than quercetin. The biomass of the specimens showed a significant correlation with the chemical profile. The similarity among specimens was mostly determined by the concentrations of quinic acid, kaempferol glycosides, and boldine. The evaluated specimens exhibited chemical features similar to those of species classified as New World Ocotea, with the coexistence of aporphine and benzylisoquinoline alkaloids.

Importance of quorum sensing crosstalk in the brown alga Saccharina latissima epimicrobiome

Brown macroalgae are colonized by diverse microorganisms influencing the physiology of their host. However, cell-cell interactions within the surface microbiome (epimicrobiome) are largely unexplored, despite the significance of specific chemical mediators in maintaining host-microbiome homeostasis. In this study, by combining liquid chromatography coupled to mass spectrometry (LC-MS) analysis and bioassays, we demonstrated that the widely diverse fungal epimicrobiota of the brown alga Saccharina latissima can affect quorum sensing (QS), a type of cell-cell interaction, as well as bacterial biofilm formation. We also showed the ability of the bacterial epimicrobiota to form and inhibit biofilm growth, as well as to activate or inhibit QS pathways. Overall, we demonstrate that QS and anti-QS compounds produced by the epimicrobiota are key metabolites in these brown algal epimicrobiota communities and highlight the importance of exploring this epimicrobiome for the discovery of new bioactive compounds, including potentially anti-QS molecules with antifouling properties.

Bioassay-Driven, Fractionation-Empowered, Focused Metabolomics for Discovering Bacterial Activators of Aryl Hydrocarbon Receptor

Aryl hydrocarbon receptor (AhR) is a transcription factor that regulates gene expression upon ligand activation, enabling microbiota-dependent induction, training, and function of the host immune system. A spectrum of metabolites, encompassing indole and tryptophan derivatives, have been recognized as activators. This work introduces an integrated, mass spectrometry-centric workflow that employs a bioassay-guided, fractionation-based methodology for the identification of AhR activators derived from human bacterial isolates. By leveraging the workflow efficiency, the complexities inherent in metabolomics profiling are significantly reduced, paving the way for an in-depth and focused mass spectrometry analysis of bioactive fractions isolated from bacterial culture supernatants. Validation of AhR activator candidates used multiple criteria─MS/MS of the synthetic reference compound, bioassay of AhR activity, and elution time confirmation using a C-13 isotopic reference─and was demonstrated for N-formylkynurenine (NFK). The workflow reported provides a roadmap update for improved efficiency of identifying bioactive metabolites using mass spectrometry-based metabolomics. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (PR001479, Project DOI: 10.21228/M8JM7Q).

Exploring preferred binding domains of IgG1 mAbs to multimodal adsorbents using a combined biophysics and simulation approach

In this work, we employ a recently developed biophysical technique that uses diethylpyrocarbonate (DEPC) covalent labeling and mass spectrometry for the identification of mAb binding patches to two multimodal cation exchange resins at different pH. This approach compares the labeling results obtained in the bound and unbound states to identify residues that are sterically shielded and thus located in the mAb binding domains. The results at pH 6 for one mAb (mAb B) indicated that while the complementarity determining region (CDR) had minimal interactions with both resins, the FC domain was actively involved in binding. In contrast, DEPC/MS data with another mAb (mAb C) indicated that both the CDR and FC domains were actively involved in binding. These results corroborated chromatographic retention data with these two mAbs and their fragments and helped to explain the significantly stronger retention of both the intact mAb C and its Fab fragment. In contrast, labeling results with mAb C at pH 7, indicated that only the CDR played a significant role in resin binding, again corroborating chromatographic data. The binding domains identified from the DEPC/MS experiments were also examined using protein surface hydrophobicity maps obtained using a recently developed sparse sampling molecular dynamics (MD) approach in concert with electrostatic potential maps. These results demonstrate that the DEPC covalent labeling/mass spectrometry technique can provide important information about the domain contributions of multidomain proteins such as monoclonal antibodies when interacting with multimodal resins over a range of pH conditions.

Molecular signatures of angiogenesis inhibitors: a single-embryo untargeted metabolomics approach in zebrafish

Angiogenesis is a key process in embryonic development, a disruption of this process can lead to severe developmental defects, such as limb malformations. The identification of molecular perturbations representative of antiangiogenesis in zebrafish embryo (ZFE) may guide the assessment of developmental toxicity from an endpoint- to a mechanism-based approach, thereby improving the extrapolation of findings to humans. Thus, the aim of the study was to discover molecular changes characteristic of antiangiogenesis and developmental toxicity. We exposed ZFEs to two antiangiogenic drugs (SU4312, sorafenib) and two developmental toxicants (methotrexate, rotenone) with putative antiangiogenic action. Molecular changes were measured by performing untargeted metabolomics in single embryos. The metabolome response was accompanied by the occurrence of morphological alterations. Two distinct metabolic effect patterns were observed. The first pattern comprised common effects of two specific angiogenesis inhibitors and the known teratogen methotrexate, strongly suggesting a shared mode of action of antiangiogenesis and developmental toxicity. The second pattern involved joint effects of methotrexate and rotenone, likely related to disturbances in energy metabolism. The metabolites of the first pattern, such as phosphatidylserines, pterines, retinol, or coenzyme Q precursors, represented potential links to antiangiogenesis and related developmental toxicity. The metabolic effect pattern can contribute to biomarker identification for a mechanism-based toxicological testing.

metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

Antidiabetic Potential of a Trimeric Anthranilic Acid Peptide Isolated from Malbranchea flocciformis

Compound 3, a trimeric anthranilic acid peptide, and another three metabolites were isolated from an organic extract from the culture medium of Malbranchea flocciformis ATCC 34530. The chemical structure proposed previously for 3 was unequivocally assigned via synthesis and X-ray diffraction analysis. Tripeptide 3 showed insulinotropic properties by decreasing the postprandial peak in healthy and hyperglycemic mice. It also increased glucose-induced insulin secretion in INS-1E at 5 μM, specifically at higher glucose concentrations. These results revealed that 3 might act as an insulin sensitizer and a non-classical insulin secretagogue. Altogether, these findings are in harmony with the in vivo oral glucose tolerance test and acute oral hypoglycemic assay. Finally, the chemical composition of the extract was established by the Global Natural Products Social Molecular Network platform. Phylogenetic analysis using the internal transcribed spacer region revealed that M. flocciformis ATCC 34530 is related to the Malbrancheaceae.

Accelerating the discovery of alkyl halide-derived natural products using halide depletion

Even in the genomic era, microbial natural product discovery workflows can be laborious and limited in their ability to target molecules with specific structural features. Here we leverage an understanding of biosynthesis to develop a workflow that targets the discovery of alkyl halide-derived natural products by depleting halide anions, a key biosynthetic substrate for enzymatic halogenation, from microbial growth media. By comparing the metabolomes of bacterial cultures grown in halide-replete and deficient media, we rapidly discovered the nostochlorosides, the products of an orphan halogenase-encoding gene cluster from Nostoc punctiforme ATCC 29133. We further found that these products, a family of unusual chlorinated glycolipids featuring the rare sugar gulose, are polymerized via an unprecedented enzymatic etherification reaction. Together, our results highlight the power of leveraging an understanding of biosynthetic logic to streamline natural product discovery.

Molecular networking unveils anti-SARS-CoV-2 constituents from traditionally used remedies

ETHNOPHARMACOLOGICAL RELEVANCE

Plants and fungi have a long tradition in ethnopharmacology for the treatment of infectious diseases including viruses. Many of these natural products have also been used to combat SARS-CoV-2 infections or symptoms of the post- and long-COVID form, owing to the scarcity of clinically approved therapeutics.

AIM OF THE STUDY

The ongoing threat posed by SARS-CoV-2, along with the rapidly evolving new variants, requires the development of new antiviral compounds. The aim of this study was to identify anti-SARS-CoV-2 herbal and fungal extracts used in traditional medicine against acute respiratory infection, inflammation, and related symptoms. Additionally, we sought to characterize their bioactive constituents.

MATERIALS AND METHODS

The antiviral activity and cell cytotoxicity of 179 herbal and fungal extracts were evaluated using two SARS-CoV-2 infection assays in Caco-2 cells. 19 plant extracts with and without anti-SARS-CoV-2 activity underwent detailed dereplication using molecular networking.

RESULTS

Extracts from Angelica sinensis (Oliv.) Diels roots, Annona squamosa L. seeds, Azadirachta indica A. Juss. fruits, Buddleja officinalis Maxim. flowers, Burkea africana Hook. bark and Clinopodium menthifolium (Host) Stace aerial parts showed a potent anti SARS-CoV-2 activity (IC50 < 5 μg/ml) with only moderate cytotoxicity (CC50 > 60 μg/ml, Caco-2). By performing the dereplication with a bioactivity-featured molecular network (MN) on the extract library level, rather than on the level of individual extracts, we could pinpoint compounds characteristic for active extracts. Thus, a straight-forward identification of potential anti-SARS-CoV-2 natural compounds was achieved prior to any fractionation or isolation efforts.

CONCLUSIONS

A sophisticated hyphenation of empirical knowledge with MS-based bioinformatics and automated compound annotation was applied to decipher the chemical space of the investigated extracts. The correlation with experimentally assessed anti-SARS-CoV-2 activities helped in predicting compound classes and structural elements relevant for the antiviral activities. Consequently, this accelerated the identification of constituents from the investigated mixtures with inhibitory effects against SARS-CoV-2.

Atlas of fetal metabolism during mid-to-late gestation and diabetic pregnancy

Mounting evidence suggests metabolism instructs stem cell fate decisions. However, how fetal metabolism changes during development and how altered maternal metabolism shapes fetal metabolism remain unexplored. We present a descriptive atlas of in vivo fetal murine metabolism during mid-to-late gestation in normal and diabetic pregnancy. Using 13C-glucose and liquid chromatography-mass spectrometry (LC-MS), we profiled the metabolism of fetal brains, hearts, livers, and placentas harvested from pregnant dams between embryonic days (E)10.5 and 18.5. Our analysis revealed metabolic features specific to a hyperglycemic environment and signatures that may denote developmental transitions during euglycemic development. We observed sorbitol accumulation in fetal tissues and altered neurotransmitter levels in fetal brains isolated from hyperglycemic dams. Tracing 13C-glucose revealed disparate fetal nutrient sourcing depending on maternal glycemic states. Regardless of glycemic state, histidine-derived metabolites accumulated in late-stage fetal tissues. Our rich dataset presents a comprehensive overview of in vivo fetal tissue metabolism and alterations due to maternal hyperglycemia.

Assessing background contamination of sample tubes used in human biomonitoring by non-targeted liquid chromatography-high resolution mass spectrometry

Controlling and minimising background contamination is crucial for maintaining a high quality of samples in human biomonitoring targeting organic chemicals. We assessed the contamination of three previous types and one newly introduced medical-grade type of sample tubes used for storing human body fluids at the German Environmental Specimen Bank. Aqueous extracts from these tubes were analysed by non-targeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) before and after a dedicated cleaning procedure. After peak detection using MZmine, Bayesian hypothesis testing was used to group peaks into those originating either from instrumental and laboratory background contamination, or actual tube contaminants, based on if their peak height was reduced, increased or not affected by the cleaning procedure. For all four tube types 80-90% of the 2475 peaks (1549 in positive and 926 in negative mode) were assigned to laboratory/instrumental background, which we have to consider as potential sample tube contaminants. Among the tube contaminants, results suggest a considerable difference in the contaminant peak inventory and the absolute level of contamination among the different sample tube types. The cleaning procedure did not affect the largest fraction of peaks (50-70%). For the medical grade tubes, the removal of contaminants by the cleaning procedure was strongest compared to the previous tubes, but in all cases a small fraction increased in intensity after cleaning, probably due to a release of oligomers or additives. The identified laboratory background contaminants were mainly semi-volatile polymer additives such as phthalates and phosphate esters. A few compounds could be assigned solely as tube-specific contaminants, such as N,N-dibutylformamide and several constituents of the oligomeric light stabiliser Tinuvin-622. A cleaning procedure before use is an effective way to standardise the used sample tubes and minimises the background contamination, and therefore increases sample quality and therewith analytical results.

OSMAC Method to Assess Impact of Culture Parameters on Metabolomic Diversity and Biological Activity of Marine-Derived Actinobacteria

Actinobacteria are known for their production of bioactive specialized metabolites, but they are still under-exploited. This study uses the "One Strain Many Compounds" (OSMAC) method to explore the potential of three preselected marine-derived actinobacteria: Salinispora arenicola (SH-78) and two Micromonospora sp. strains (SH-82 and SH-57). Various parameters, including the duration of the culture and the nature of the growth medium, were modified to assess their impact on the production of specialized metabolites. This approach involved a characterization based on chemical analysis completed with the construction of molecular networks and biological testing to evaluate cytotoxic and antiplasmodial activities. The results indicated that the influence of culture parameters depended on the studied species and also varied in relation with the microbial metabolites targeted. However, common favorable parameters could be observed for all strains such as an increase in the duration of the culture or the use of the A1 medium. For Micromonospora sp. SH-82, the solid A1 medium culture over 21 days favored a greater chemical diversity. A rise in the antiplasmodial activity was observed with this culture duration, with a IC50 twice as low as for the 14-day culture. Micromonospora sp. SH-57 produced more diverse natural products in liquid culture, with approximately 54% of nodes from the molecular network specifically linked to the type of culture support. Enhanced biological activities were also observed with specific sets of parameters. Finally, for Salinispora arenicola SH-78, liquid culture allowed a greater diversity of metabolites, but intensity variations were specifically observed for some metabolites under other conditions. Notably, compounds related to staurosporine were more abundant in solid culture. Consequently, in the range of the chosen parameters, optimal conditions to enhance metabolic diversity and biological activities in these three marine-derived actinobacteria were identified, paving the way for future isolation works.

Metabolic and Microbial Profiling of Soil Microbial Community under Per- and Polyfluoroalkyl Substance (PFAS) Stress

Per- and polyfluoroalkyl substances (PFAS) represent significant stress to organisms and are known to disrupt microbial community structure and function. Nevertheless, a detailed knowledge of the soil microbial community responding to PFAS stress at the metabolism level is required. Here we integrated UPLC-HRMS-based metabolomics data with 16S rRNA and ITS amplicon data across soil samples collected adjacent to a fluoropolymer production facility to directly identify the biochemical intermediates in microbial metabolic pathways and the interactions with microbial community structure under PFAS stress. A strong correlation between metabolite and microbial diversity was observed, which demonstrated significant variations in soil metabolite profiles and microbial community structures along with the sampling locations relative to the facility. Certain key metabolites were identified in the metabolite-PFAS co-occurrence network, functioning on microbial metabolisms including lipid metabolism, amino acid metabolism, and secondary metabolite biosynthesis. These results provide novel insights into the impacts of PFAS contamination on soil metabolomes and microbiomes. We suggest that soil metabolomics is an informative and useful tool that could be applied to reinforce the chemical evidence on the disruption of microbial ecological traits.

An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing

Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.

Comparative genomic and metabolomic study of three Streptomyces sp. differing in biological activity

The Streptomyces genus is known to produce many specialized metabolites of value for medicine, but the potential of these metabolites in agronomy remains largely unexplored. In this study, we investigated three phylogenetically closely related Streptomyces strains (B5, B91, and B135) isolated from three distinct soil samples in Sudan. Despite belonging to the same species, these strains exhibited different ranges of Phytophthora infestans inhibition. The objective of this work was to identify the active compound(s) responsible for the inhibition of P. infestans and of other plant pathogens by comparing the genomes and metabolomes of the three strains which showed distinct activity patterns: B5 was the strongest inhibitor of oomycetes, B5 and B91 both inhibited most fungi and B135 was the only strain showing antibacterial activity. Our comparative genomic and metabolomic analysis identified borrelidin as the bioactive compound underlying B5's strong anti-oomycete activity and highlighted a few other metabolites as putative candidates underlying the strains' antifungal and antibacterial activities. This study illustrates the power of comparative genomics and metabolomics on phylogenetically closely related strains of differing activities to highlight bioactive compounds that could contribute to new sustainable crop protection strategies.

A multivariate data analysis approach for the investigation of in vitro derived metabolites of ACP-105 in comparison with human in vivo metabolites

Selective androgen receptor modulators (SARMs) such as ACP-105 are prohibited in sports due to their anabolic properties. ACP-105 has in previous equine studies shown to undergo extensive metabolism, which makes its metabolite profile important to investigate in humans, since the metabolism is unknown in this species. The aims of the study were to systematically optimize in vitro microsome incubations for improved metabolite yield and to utilize a multivariate data analysis (MVDA) approach to aid the metabolite discovery. Microsomes together with S9 fractions were used at optimal conditions, both with and without phase II additives. Furthermore, the relevance of the in vitro derived metabolites was evaluated as analytical targets in doping control by comparison with results from a human post-administration urine sample collected after a single dose of 100 µg ACP-105. All samples were analyzed with liquid chromatography - Orbitrap mass spectrometry. The use of the systematical optimization and MVDA greatly simplified the search and a total of 18 in vitro metabolites were tentatively identified. The yield of the two main monohydroxylated isomers increased by 24 and 10 times, respectively. In the human urine sample, a total of seven metabolites of ACP-105, formed by a combination of hydroxylations and glucuronic acid conjugations, were tentatively identified. The main metabolites were two monohydroxylated forms that are suggested as analytical targets for human doping control after hydrolysis. All the in vivo metabolites could be detected with the MVDA approach on the in vitro models, demonstrating its usefulness for prediction of the in vivo metabolite profile.

Simultaneous extraction of crocin and geniposide from gardenia fruits (Gardenia jasminoides Ellis) by probe-type ultrasound-assisted natural deep eutectic solvents and their inhibition effects on low density lipoprotein oxidation

The simultaneous extraction of crocin and geniposide from gardenia fruits (Gardenia jasminoides Ellis) was performed by integrating natural deep eutectic solvents (NADES) and ultrasound-assisted extraction (UAE). Among the eight kinds of NADES screened, choline chloride-1,2-propylene glycol was the most suitable extractant. The probe-type ultrasound-assisted NADES extraction system (pr-UAE-NADES) demonstrated higher extraction efficiency compared with plate-type ultrasound-assisted NADES extraction system (pl-UAE-NADES). Orthogonal experimental design and a modified multi-index synthetic weighted scoring method were adopted to optimize pr-UAE-NADES extraction process. The optimal extraction conditions that had a maximum synthetic weighted score of 29.46 were determined to be 25 °C for extraction temperature, 600 W for ultrasonic power, 20 min for extraction time, and 25% (w/w) for water content in NADES, leading to the maximum yields (7.39 ± 0.20 mg/g and 57.99 ± 0.91 mg/g, respectively) of crocin and geniposide. Thirty-three compounds including iridoids, carotenoids, phenolic acids, flavonoids, and triterpenes in the NADES extract were identified by LC-Q-TOF-MS2 coupled with a feature-based molecular networking workflow. The kinetics evaluation of the conjugated dienes generation on Cu2+-induced low density lipoprotein (LDL) oxidation via the four-parameter logistic regression model showed that crocin increased the lag time of LDL oxidation in a concentration-dependent manner (15 μg/mL, 30 μg/mL, 45 μg/mL) by 12.66%, 35.44%, and 73.42%, respectively. The quantitative determination for fluorescence properties alteration of the apolipoprotein B-100 exhibited that crocin effectively inhibited the fluorescence quenching of tryptophan residues and the modification of lysine residues caused by reactive aldehydes and malondialdehydes. The pr-UAE-NADES showed significant efficiency toward the simultaneous extraction of crocin and geniposide from gardenia fruits. And this study demonstrates the potential utility of gardenia fruits in developing anti-atherogenic functional food.