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

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.

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.

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.

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.

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.

Pharmacokinetics of Novel Dopamine Transporter Inhibitor CE-123 and Modafinil with a Focus on Central Nervous System Distribution

S-CE-123, a novel dopamine transporter inhibitor, has emerged as a potential candidate for cognitive enhancement. The objective of this study was to compare the tissue distribution profiles, with a specific focus on central nervous system distribution and metabolism, of S-CE-123 and R-modafinil. To address this objective, a precise liquid chromatography-high resolution mass spectrometry method was developed and partially validated. Neuropharmacokinetic parameters were assessed using the Combinatory Mapping Approach. Our findings reveal distinct differences between the two compounds. Notably, S-CE-123 demonstrates a significantly superior extent of transport across the blood-brain barrier (BBB), with an unbound brain-to-plasma concentration ratio (Kp,uu,brain) of 0.5, compared to R-modafinil's Kp,uu,brain of 0.1. A similar pattern was observed for the transport across the blood-spinal cord barrier. Concerning the drug transport across cellular membranes, we observed that S-CE-123 primarily localizes in the brain interstitial space, whereas R-modafinil distributes more evenly across both sides of the plasma membrane of the brain's parenchymal cells (Kp,uu,cell). Furthermore, our study highlights the substantial differences in hepatic metabolic stability, with S-CE-123 having a 9.3-fold faster metabolism compared to R-modafinil. In summary, the combination of improved BBB transport and higher affinity of S-CE-123 to dopamine transporters in comparison to R-modafinil makes S-CE-123 a promising candidate for further testing for the treatment of cognitive decline.

Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults

The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.

Persistent Biofluid Small-Molecule Alterations Induced by Trypanosoma cruzi Infection Are Not Restored by Parasite Elimination

Chagas disease (CD), caused by Trypanosoma cruzi (T. cruzi) protozoa, is a complicated parasitic illness with inadequate medical measures for diagnosing infection and monitoring treatment success. To address this gap, we analyzed changes in the metabolome of T. cruzi-infected mice via liquid chromatography tandem mass spectrometry of clinically accessible biofluids: saliva, urine, and plasma. Urine was the most indicative of infection status across mouse and parasite genotypes. Metabolites perturbed by infection in urine include kynurenate, acylcarnitines, and threonylcarbamoyladenosine. Based on these results, we sought to implement urine as a tool for the assessment of CD treatment success. Strikingly, it was found that mice with parasite clearance following benznidazole antiparasitic treatment had an overall urine metabolome comparable to that of mice that failed to clear parasites. These results provide a complementary hypothesis to explain clinical trial data in which benznidazole treatment did not improve patient outcomes in late-stage disease, even in patients with successful parasite clearance. Overall, this study provides insights into new small-molecule-based CD diagnostic methods and a new approach to assess functional responses to treatment.

Untargeted metabolomic analyses support the main phylogenetic groups of the common plant-associated Alternaria fungi isolated from grapevine (Vitis vinifera)

Alternaria, a cosmopolitan fungal genus is a dominant member of the grapevine (Vitis vinifera) microbiome. Several Alternaria species are known to produce a variety of secondary metabolites, which are particularly relevant to plant protection and food safety in field crops. According to previous findings, the majority of Alternaria species inhabiting grapevine belong to Alternaria sect. Alternaria. However, the phylogenetic diversity and secondary metabolite production of the distinct Alternaria species has remained unclear. In this study, our aim was to examine the genetic and metabolic diversity of endophytic Alternaria isolates associated with the above-ground tissues of the grapevine. Altogether, 270 Alternaria isolates were collected from asymptomatic leaves and grape clusters of different grapevine varieties in the Eger wine region of Hungary. After analyses of the nuclear ribosomal DNA internal transcribed spacer (ITS) and RNA polymerase second largest subunit (rpb2) sequences, 170 isolates were chosen for further analyses. Sequences of the Alternaria major allergen gene (Alt a 1), endopolygalacturonase (endoPG), OPA10-2, and KOG1058 were also included in the phylogenetic analyses. Identification of secondary metabolites and metabolite profiling of the isolates were performed using high-performance liquid chromatography (HPLC)-high-resolution tandem mass spectrometry (HR-MS/MS). The multilocus phylogeny results revealed two distinct groups in grapevine, namely A. alternata and the A. arborescens species complex (AASC). Eight main metabolites were identified in all collected Alternaria isolates, regardless of their affiliation to the species and lineages. Multivariate analyses of untargeted metabolites found no clear separations; however, a partial least squares-discriminant analysis model was able to successfully discriminate between the metabolic datasets from isolates belonging to the AASC and A. alternata. By conducting univariate analysis based on the discriminant ability of the metabolites, we also identified several features exhibiting large and significant variation between A. alternata and the AASC. The separation of these groups may suggest functional differences, which may also play a role in the functioning of the plant microbiome.

Yeast-based heterologous production of the Colletochlorin family of fungal secondary metabolites

Transcriptomic studies have revealed that fungal pathogens of plants activate the expression of numerous biosynthetic gene clusters (BGC) exclusively when in presence of a living host plant. The identification and structural elucidation of the corresponding secondary metabolites remain challenging. The aim was to develop a polycistronic system for heterologous expression of fungal BGCs in Saccharomyces cerevisiae. Here we adapted a polycistronic vector for efficient, seamless and cost-effective cloning of biosynthetic genes using in vivo assembly (also called transformation-assisted recombination) directly in Escherichia coli followed by heterologous expression in S. cerevisiae. Two vectors were generated with different auto-inducible yeast promoters and selection markers. The effectiveness of these vectors was validated with fluorescent proteins. As a proof-of-principle, we applied our approach to the Colletochlorin family of molecules. These polyketide secondary metabolites were known from the phytopathogenic fungus Colletotrichum higginsianum but had never been linked to their biosynthetic genes. Considering the requirement for a halogenase, and by applying comparative genomics, we identified a BGC putatively involved in the biosynthesis of Colletochlorins in C. higginsianum. Following the expression of those genes in S. cerevisiae, we could identify the presence of the precursor Orsellinic acid, Colletochlorins and their non-chlorinated counterparts, the Colletorins. In conclusion, the polycistronic vectors described herein were adapted for the host S. cerevisiae and allowed to link the Colletochlorin compound family to their corresponding biosynthetic genes. This system will now enable the production and purification of infection-specific secondary metabolites of fungal phytopathogens. More widely, this system could be applied to any fungal BGC of interest.

MYC is a regulator of androgen receptor inhibition-induced metabolic requirements in prostate cancer

Advanced prostate cancers are treated with therapies targeting the androgen receptor (AR) signaling pathway. While many tumors initially respond to AR inhibition, nearly all develop resistance. It is critical to understand how prostate tumor cells respond to AR inhibition in order to exploit therapy-induced phenotypes prior to the outgrowth of treatment-resistant disease. Here, we comprehensively characterize the effects of AR blockade on prostate cancer metabolism using transcriptomics, metabolomics, and bioenergetics approaches. The metabolic response to AR inhibition is defined by reduced glycolysis, robust elongation of mitochondria, and increased reliance on mitochondrial oxidative metabolism. We establish DRP1 activity and MYC signaling as mediators of AR-blockade-induced metabolic phenotypes. Rescuing DRP1 phosphorylation after AR inhibition restores mitochondrial fission, while rescuing MYC restores glycolytic activity and prevents sensitivity to complex I inhibition. Our study provides insight into the regulation of treatment-induced metabolic phenotypes and vulnerabilities in prostate cancer.

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics

BACKGROUND

Chromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality.

RESULTS

Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram, and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone.

CONCLUSIONS

We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation.

Diversity of organic components in airborne waste discharged from sewer pipe repairs

Air-discharged waste from commonly used trenchless technologies of sewer pipe repairs is an emerging and poorly characterized source of urban pollution. This study reports on the molecular-level characterization of the atmospherically discharged aqueous-phase waste condensate samples collected at four field sites of the sewer pipe repairs. The molecular composition of organic species in these samples was investigated using reversed-phase liquid chromatography coupled with a photodiode array detector and a high-resolution mass spectrometer equipped with interchangeable atmospheric pressure photoionization and electrospray ionization sources. The waste condensate components comprise a complex mixture of organic species that can partition between gas-, aqueous-, and solid-phases when water evaporates from the air-discharged waste. Identified organic species have broad variability in molecular weight, molecular structures, and carbon oxidation state, which also varied between the waste samples. All condensates contained complex mixtures of oxidized organics, N- and S-containing organics, condensed aromatics, and their functionalized derivatives that are directly released to the atmospheric environment during installations. Furthermore, semi-volatile, low volatility, and extremely low volatility organic compounds comprise 75-85% of the total compounds identified in the waste condensates. Estimates of the component-specific viscosities suggest that upon evaporation of water waste material would form the semi-solid and solid phases. The low volatilities and high viscosities of chemical components in these waste condensates will contribute to the formation of atmospheric secondary organic aerosols and atmospheric solid nanoplastic particles. Lastly, selected components expected in the condensates were quantified and found to be present at high concentrations (1-20 mg L-1) that may exceed regulatory limits.

Evaluation of the biological responses of silver nanoparticles synthesized using Pelargonium x hortorum extract

Silver nanoparticles (AgNPs) are one of the widely studied nanomaterials for diverse biomedical applications, in particular, as antimicrobial agents to kill bacteria, fungi, and viruses. In this report, AgNPs were synthesized using a geranium (Pelargonium x hortorum) leaves extract and tested for their antimicrobial and cytotoxic activity and reactive oxygen species (ROS) production. Using green biosynthesis, the leaves extract was employed as a reducing and stabilizing agent. Synthesis parameters like reaction time and precursor (silver nitrate AgNO3) volume final were modified, and the products were tested against Streptococcus mutans. For the first time, the metabolomic analysis of extract, we have identified more than 50 metabolites. The UV-Vis analysis showed a peak ranging from 410-430 nm, and TEM confirmed their nearly spherical morphology for all NPs. The antimicrobial activity of the NPs revealed a minimum inhibitory concentration (MIC) of 10 μg mL-1. Concerning cytotoxicity, a dose-time-dependent effect was observed with a 50% cellular cytotoxicity concentration (CC50) of 4.51 μg mL-1 at 24 h. Interestingly, the cell nuclei were visualized using fluorescence microscopy, and no significant changes were observed. These results suggest that synthesized spherical AgNPs are promising potential candidates for medical applications.

Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.

Plasma lipidomic analysis to investigate putative biomarkers of P-glycoprotein activity in healthy volunteers

P-glycoprotein (P-gp) is an efflux transporter involved in the bioavailability of many drugs currently on the market. P-gp is responsible for several drug-drug interactions encountered in clinical practice leading to iatrogenic hospital admissions, especially in polypharmacy situations. ABCB1 genotyping only reflects an indirect estimate of P-gp activity. Therefore, it would be useful to identify endogenous biomarkers to determine the P-gp phenotype to predict in vivo activity prior to the initiation of treatment and to assess the effects of drugs on P-gp activity. The objective of this study was to assess changes in plasma lipidome composition among healthy volunteers selected on the basis of their ABCB1 genotype and who received clarithromycin, a known inhibitor of P-gp. Untargeted lipidomic analysis based on liquid chromatography-tandem mass spectrometry was performed before and after clarithromycin administration. Our results revealed changes in plasma levels of some ceramides (Cers) {Cer(d18:1/22:0), Cer(d18:1/22:1), and Cer(d18:1/20:0) by ~38% (p < 0.0001), 13% (p < 0.0001), and 13% (p < 0.0001), respectively} and phosphatidylcholines (PCs) {PC(17:0/14:1), PC(16:0/18:3), and PC(14:0/18:3) by ~24% (p < 0.001), 10% (p < 0.001), and 23.6% (p < 0.001)} associated with both ABCB1 genotype and clarithromycin intake. Through the examination of plasma lipids, our results highlight the relevance of untargeted lipidomics for studying in vivo P-gp activity and, more generally, to safely phenotyping transporters.

The MprF homolog LysX synthesizes lysyl-diacylglycerol contributing to antibiotic resistance and virulence

Lysyl-diacylglycerol (Lys-DAG) was identified three decades ago in Mycobacterium phlei, but the biosynthetic pathway and function of this aminoacylated lipid have since remained uncharacterized. Combining genetic methods, mass spectrometry, and biochemical approaches, we show that the multiple peptide resistance factor (MprF) homolog LysX from Corynebacterium pseudotuberculosis and two mycobacterial species is responsible for Lys-DAG synthesis. LysX is conserved in most Actinobacteria and was previously implicated in the synthesis of another modified lipid, lysyl-phosphatidylglycerol (Lys-PG), in Mycobacterium tuberculosis. Although we detected low levels of Lys-PG in the membrane of C. pseudotuberculosis, our data suggest that Lys-PG is not directly synthesized by LysX and may require an additional downstream pathway, which is as yet undefined. Our results show that LysX in C. pseudotuberculosis is a major factor of resistance against a variety of positively charged antibacterial agents, including cationic antimicrobial peptides (e.g., human peptide LL-37 and polymyxin B) and aminoglycosides (e.g., gentamycin and apramycin). Deletion of lysX caused an increase in cellular membrane permeability without dissipation of the membrane potential, suggesting that loss of the protein does not result in mechanical damage to the cell membrane. Furthermore, lysX-deficient cells exhibited an attenuated virulence phenotype in a Galleria mellonella infection model, supporting a role for LysX during infection. Altogether, Lys-DAG represents a novel molecular determinant for antimicrobial resistance and virulence that may be widespread in Actinobacteria and points to a richer landscape than previously realized of lipid components contributing to overall membrane physiology in this important bacterial phylum. IMPORTANCE In the past two decades, tRNA-dependent modification of membrane phosphatidylglycerol has been implicated in altering the biochemical properties of the cell surface, thereby enhancing the antimicrobial resistance and virulence of various bacterial pathogens. Here, we show that in several Actinobacteria, the multifunctional protein LysX attaches lysine to diacylglycerol instead of phosphatidylglycerol. We found that lysyl-diacylglycerol (Lys-DAG) confers high levels of resistance against various cationic antimicrobial peptides and aminoglycosides and also enhances virulence. Our data show that Lys-DAG is a lipid commonly found in important actinobacterial pathogens, including Mycobacterium and Corynebacterium species.

qBinning: Data Quality-Based Algorithm for Automized Ion Chromatogram Extraction from High-Resolution Mass Spectrometry

Due to the complexity and volume of data generated through non-target screening (NTS) using chromatographic couplings with high-resolution mass spectrometry, automized processing routines are necessary. The processing routines usually consist of many individual steps that are user-parameter-dependent and, thus, require labor-intensive optimization. Additionally, the effect of variations in raw data quality on the processing results is unclear and not fully understood. Within this work, we present qBinning, a novel algorithm for constructing extracted ion chromatograms (EICs) based on statistical principles and, thus, without the need to set user parameters. Furthermore, we give the user feedback on the specific qualities of the generated EICs using a scoring system (DQSbin). The DQSbin measures reliability as it correlates with the probability of correct classification of masses into EICs and the degree of overlap between different EIC construction algorithms. This work is a big step forward in understanding the behavior of NTS data and increasing the overall transparency in the results of NTS.

Leaf metabolic traits reveal hidden dimensions of plant form and function

The metabolome is the biochemical basis of plant form and function, but we know little about its macroecological variation across the plant kingdom. Here, we used the plant functional trait concept to interpret leaf metabolome variation among 457 tropical and 339 temperate plant species. Distilling metabolite chemistry into five metabolic functional traits reveals that plants vary on two major axes of leaf metabolic specialization-a leaf chemical defense spectrum and an expression of leaf longevity. Axes are similar for tropical and temperate species, with many trait combinations being viable. However, metabolic traits vary orthogonally to life-history strategies described by widely used functional traits. The metabolome thus expands the functional trait concept by providing additional axes of metabolic specialization for examining plant form and function.

Identification of Ecdysteroid Sinapate Esters with COX-2 Inhibitory Effects from Fibraurea recisa Using Molecular Networking and MS2LDA

The roots of Fibraurea recisa are recognized as a rich source of protoberberine and aporphine alkaloids, but the non-alkaloidal metabolites in this plant are underexplored. The present study investigated the chemical composition of the plant roots using untargeted metabolomics-based molecular networking and MS2LDA motif annotation, revealing the presence of a characteristic fragment motif related to several sinapoyl-functionalized metabolites. Guided by the targeted motif, two new sinapic acid-ecdysteroid hybrids, named 3-O-sinapoyl makisterone A (1) and 2-O-sinapoyl makisterone A (2), were isolated. The structures of these compounds, including their absolute configuration, were elucidated by HR-ESIQTOFMS, MS2 fragmentation, NMR spectroscopy, and chemical degradation coupled with optical rotation measurements. Although neither compound inhibited nitric oxide (NO) production or inducible nitric oxide synthase (iNOS) protein expression on lipopolysaccharide-induced RAW 264 cells, 2 significantly suppressed cyclooxygenase 2 (COX-2) protein expression at 1-30 μM. Additionally, decreased expression of COX-2 protein was barely observed after treatment with methyl sinapate or makisterone A, the steroid skeleton of 1 and 2. These results indicated that the presence of the sinapoyl moiety at C-2 on the C28-ecdysteroid skeleton played a key role in the selectivity for the suppression of the COX-2 protein expression.

Decreased metabolic diversity in common beans associated with domestication revealed by untargeted metabolomics, information theory, and molecular networking

The process of crop domestication leads to a dramatic reduction in the gene expression associated with metabolic diversity. Genes involved in specialized metabolism appear to be particularly affected. Although there is ample evidence of these effects at the genetic level, a reduction in diversity at the metabolite level has been taken for granted despite having never been adequately accessed and quantified. Here we leveraged the high coverage of ultra high performance liquid chromatography-high-resolution mass spectrometry based metabolomics to investigate the metabolic diversity in the common bean (Phaseolus vulgaris). Information theory highlights a shift towards lower metabolic diversity and specialization when comparing wild and domesticated bean accessions. Moreover, molecular networking approaches facilitated a broader metabolite annotation than achieved to date, and its integration with gene expression data uncovers a metabolic shift from specialized metabolism towards central metabolism upon domestication of this crop.

Using UHPLC-HRMS-based comprehensive strategy to efficiently and accurately screen and identify illegal additives in health-care foods

Accurately and high-thoroughly screening illegal additives in health-care foods continues to be a challenging task in routine analysis for the ultrahigh performance liquid chromatography-high resolution mass spectrometry based techniques. In this work, we proposed a new strategy to identify additives in complex food matrices, which consists of both experimental design and advanced chemometric data analysis. At first, reliable features in the analyzed samples were screened based on a simple but efficient sample weighting design, and those related to illegal additives were screened with robust statistical analysis. After the MS¹ in-source fragment ion identification, both MS¹ and MS/MS spectra were constructed for each underlying compound, based on which illegal additives can be precisely identified. The performance of the developed strategy was demonstrated by using mixture and synthetic sample datasets, indicating an improvement of data analysis efficiency up to 70.3 %. Finally, the developed strategy was applied for the screening of unknown additives in 21 batches of commercially available health-care foods. Results indicated that at least 80 % of false-positive results can be reduced and 4 additives were screened and confirmed.

New Phenolic Lipids from the Leaves of Clausena harmandiana Inhibit SARS-CoV-2 Entry into Host Cells

Induced by the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the COVID-19 pandemic underlined the clear need for antivirals against coronaviruses. In an effort to identify new inhibitors of SARS-CoV-2, a screening of 824 extracts prepared from various parts of 400 plant species belonging to the Rutaceae and Annonaceae families was conducted using a cell-based HCoV-229E inhibition assay. Due to its significant activity, the ethyl acetate extract of the leaves of Clausena harmandiana was selected for further chemical and biological investigations. Mass spectrometry-guided fractionation afforded three undescribed phenolic lipids (1-3), whose structures were determined via spectroscopic analysis. The absolute configurations of 1 and 2 were determined by analyzing Mosher ester derivatives. The antiviral activity against SARS-CoV-2 was subsequently shown, with IC₅₀ values of 0.20 and 0.05 µM for 2 and 3, respectively. The mechanism of action was further assessed, showing that both 2 and 3 are inhibitors of coronavirus entry by acting directly on the viral particle. Phenolic lipids from Clausena harmandiana might be a source of new antiviral agents against human coronaviruses.

Trackable and scalable LC-MS metabolomics data processing using asari

Significant challenges remain in the computational processing of data from liquid chomratography-mass spectrometry (LC-MS)-based metabolomic experiments into metabolite features. In this study, we examine the issues of provenance and reproducibility using the current software tools. Inconsistency among the tools examined is attributed to the deficiencies of mass alignment and controls of feature quality. To address these issues, we develop the open-source software tool asari for LC-MS metabolomics data processing. Asari is designed with a set of specific algorithmic framework and data structures, and all steps are explicitly trackable. Asari compares favorably to other tools in feature detection and quantification. It offers substantial improvement in computational performance over current tools, and it is highly scalable.

Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability

Non-target screening (NTS) is a powerful environmental and analytical chemistry approach for detecting and identifying unknown compounds in complex samples. High-resolution mass spectrometry has enhanced NTS capabilities but created challenges in data analysis, including data preprocessing, peak detection, and feature extraction. This review provides an in-depth understanding of NTS data processing methods, focusing on centroiding, extracted ion chromatogram (XIC) building, chromatographic peak characterization, alignment, componentization, and prioritization of features. We discuss the strengths and weaknesses of various algorithms, the influence of user input parameters on the results, and the need for automated parameter optimization. We address uncertainty and data quality issues, emphasizing the importance of incorporating confidence intervals and raw data quality assessment in data processing workflows. Furthermore, we highlight the need for cross-study comparability and propose potential solutions, such as utilizing standardized statistics and open-access data exchange platforms. In conclusion, we offer future perspectives and recommendations for developers and users of NTS data processing algorithms and workflows. By addressing these challenges and capitalizing on the opportunities presented, the NTS community can advance the field, improve the reliability of results, and enhance data comparability across different studies.

Breath Biopsy® to Identify Exhaled Volatile Organic Compounds Biomarkers for Liver Cirrhosis Detection

Background and Aims

The prevalence of chronic liver disease in adults exceeds 30% in some countries and there is significant interest in developing tests and treatments to help control disease progression and reduce healthcare burden. Breath is a rich sampling matrix that offers non-invasive solutions suitable for early-stage detection and disease monitoring. Having previously investigated targeted analysis of a single biomarker, here we investigated a multiparametric approach to breath testing that would provide more robust and reliable results for clinical use.

Methods

To identify candidate biomarkers we compared 46 breath samples from cirrhosis patients and 42 from controls. Collection and analysis used Breath Biopsy OMNI™, maximizing signal and contrast to background to provide high confidence biomarker detection based upon gas chromatography mass spectrometry (GC-MS). Blank samples were also analyzed to provide detailed information on background volatile organic compounds (VOCs) levels.

Results

A set of 29 breath VOCs differed significantly between cirrhosis and controls. A classification model based on these VOCs had an area under the curve (AUC) of 0.95±0.04 in cross-validated test sets. The seven best performing VOCs were sufficient to maximize classification performance. A subset of 11 VOCs was correlated with blood metrics of liver function (bilirubin, albumin, prothrombin time) and separated patients by cirrhosis severity using principal component analysis.

Conclusions

A set of seven VOCs consisting of previously reported and novel candidates show promise as a panel for liver disease detection and monitoring, showing correlation to disease severity and serum biomarkers at late stage.

Discovery of anti-SARS-CoV-2 secondary metabolites from the heartwood of Pterocarpus santalinus using multi-informative molecular networking

A pigment-depleted extract from the heartwood of Pterocarpus santalinus L. f. (PS-DE) showed promising anti-SARS-CoV-2 activity with an IC₅₀ of 29.9 μg/mL in Caco-2-F03 cells. To determine the potential active constituents within the extract prior to isolation, multi-informative molecular network (MN) was applied. Therefore, the extract was separated by high-performance counter-current chromatography (HPCCC) into 11 fractions which were subsequently tested for anti-SARS-CoV-2 activity and analysed by UPLC-tandem mass spectrometry (MS²). The resulting MN combines the bioactivity data of the fractions with the MS² data. The MN analysis led to the targeted isolation of seven compounds including one pterocarpan (7) reported for the first time as constituent of P. santalinus and four so far undescribed natural products (NPs) that belong to the compound classes of arylpropanes (9), isoflavanones (10) coumestans (16) and 3-arylcoumarins (17), respectively. In total, 15 constituents from the heartwood of P. santalinus and one synthetic isoflavonoid that is structurally related to the natural metabolites were tested for anti-SARS-CoV-2 activity. Thereby, the two pterocarpans (-)-homopterocarpin (5) and (-)-medicarpin (2), the stilbene (E)-pterostilbene (1) and the isoflavonoid 7-O-methylgenistein (11) showed a distinct antiviral activity with IC₅₀ values of 17.2, 33.4, 34.7, and 37.9 µM, respectively, and no cytotoxic effects against Caco-2-F03 cells (CC₅₀ > 100 µM). In addition, a structure-activity relationship (SAR) was proposed indicating structural requirements of pterocarpans for anti-SARS-CoV-2 activity. The herein presented results support the implementation of multi-informative molecular networks as powerful tool for dereplication and targeted isolation of bioactive NPs.