NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

Complementarity of two-dimensional gas chromatography and two-dimensional liquid chromatography for the analysis of depolymerised lignin

Two-dimensional gas chromatography (GC × GC) and two-dimensional liquid chromatography (LC × LC) are nowadays widely used in academia and industry due to their high separation power. However, as far as we know, the complementarity of these two techniques has not yet been thoroughly studied based on the analysis of the same sample. Therefore, this was undertaken here by analysing the liquid fraction obtained after depolymerising a natural waste - lignin - with GC × GC and off-line comprehensive LC × SFC (SFC: supercritical fluid chromatography). Using complementary techniques is also important for lignin valorisation, as thorough structural characterisation of the depolymerised product can aid with developing and improving valorisation processes. For the tentative identification, NIST library was used for GC × GC-MS results and MS-DIAL together with SIRIUS for LC × SFC-MS/MS data. This allowed to study which compounds are detectable with the different 2D methods but also to discuss the limitations of the data analysis processes. The previous knowledge that LC techniques are more suitable than GC × GC for the analysis of larger oligomers and other low volatility compounds was confirmed; however, it was seen that GC × GC enabled the analysis of smaller compounds, such as aliphatic alcohols and saturated compounds. Overall, the study demonstrates the complementarity of the two techniques but also draws attention to the different detectable compound groups and classifications that the two techniques can provide.

Combining transcriptomics and metabolomics to assess neurodevelopmental alteration caused by in utero exposure of mice to three putative thyroid hormone system disruptors

Gestating mice were exposed to three chemicals, tetrabromo-bisphenol A (TBBPA; 2 mg/kg/day), amitrole (25 and 50 mg/kg/day) and pyraclostrobin (0.4 and 2 mg/kg/day) to assess their capacity to act as thyroid hormone disruptors and compromise neurodevelopment. Propyl-thio-uracyl, a known pharmacological inhibitor of thyroid gland secretion, was used at both high and low dose as a reference thyroid hormone system disruptor (1 ppm, 1500 ppm). A combination of plasma metabolomics and striatum transcriptomics revealed the induced change in pups at the postnatal stages. Although the underlying mechanism is unlikely to involve thyroid hormone disruption, these chemicals had a detectable effect on pups' neurodevelopment.

Organic matter composition fluctuations disrupt free-living bacterial communities more than particle-associated bacterial communities in coastal waters

Marine organic matter fuels the growth of microbial communities, shaping the composition of bacteria that specialize in its breakdown. However, responses of free-living (FL) and particle-associated (PA) bacterial communities to the changing pools of dissolved organic matter (DOM) and particulate organic matter (POM) remained unclear. This study investigates the composition of size-fractionated bacterial communities, DOM and POM in coastal waters over a 22-day period that includes a diatom bloom. Co-occurrence analysis showed that the FL bacterial communities were significantly less stable than PA communities. During the diatom bloom, we observed a significant increase in DOM molecules, particularly those derived from amino acids and peptides. In contrast, the relative intensities of major POM molecule classes remained stable despite the algal bloom's influence. Our study revealed a strong negative correlation between bacterial alpha-diversity and the amount of molecules in the organic matter pool. Similarly, bacterial community beta-diversity was found to be related to the composition of organic matter pool. However, the composition of organic matter was more strongly related to the composition of FL bacterial communities compared to PA communities. This suggests that FL bacteria exhibit greater variations in temporal dynamics and higher sensitivity to the specific structure of organic matter molecules.

Integration of network-based approaches for assessing variations in metabolic profiles of alkalized and non-alkalized commercial cocoa powders

Cocoa can undergo an alkalization process to enhance its color and solubility. It reduces astringency and alters its composition, particularly in the phenolic compound content, which is related to cocoa health benefits. This study aimed to investigate the impact of alkalization on the composition of seven commercial cocoa powders. A liquid chromatography-based metabolomic approach was employed to assess the metabolic differences between alkalized and non-alkalized cocoa powders. Supervised orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify the most discriminating variables between groups. A feature-based molecular network (FBMN) was used to explore the chemical space. Three hundred forty-seven metabolites were obtained as the most discriminant, among which 60 were tentatively annotated. Phenolic compounds, lysophosphatidylethanolamines, amino acids, and their derivatives were significantly reduced in alkalized cocoas. In contrast, fatty acids and their derivatives significantly increased with alkalization. Despite the variability among commercial cocoas, chemometrics allowed the elucidation of alterations induced specifically by alkalization in their composition.

Passive accumulation of alkaloids in inconspicuously colored frogs refines the evolutionary paradigm of acquired chemical defenses

Understanding the origins of novel, complex phenotypes is a major goal in evolutionary biology. Poison frogs of the family Dendrobatidae have evolved the novel ability to acquire alkaloids from their diet for chemical defense at least three times. However, taxon sampling for alkaloids has been biased towards colorful species, without similar attention paid to inconspicuous ones that are often assumed to be undefended. As a result, our understanding of how chemical defense evolved in this group is incomplete. Here we provide new data showing that, in contrast to previous studies, species from each undefended poison frog clade have measurable yet low amounts of alkaloids. We confirm that undefended dendrobatids regularly consume mites and ants, which are known sources of alkaloids. Thus, our data suggest that diet is insufficient to explain the defended phenotype. Our data support the existence of a phenotypic intermediate between toxin consumption and sequestration - passive accumulation - that differs from sequestration in that it involves no derived forms of transport and storage mechanisms yet results in low levels of toxin accumulation. We discuss the concept of passive accumulation and its potential role in the origin of chemical defenses in poison frogs and other toxin-sequestering organisms. In light of ideas from pharmacokinetics we incorporate new and old data from poison frogs into an evolutionary model that could help explain the origins of acquired chemical defenses in animals and provide insight into the molecular processes that govern the fate of ingested toxins.

Resumen

Comprender los orígenes de fenotipos novedosos y complejos es un objetivo central en biología evolutiva. Las ranas venenosas de la familia Dendrobatidae han desarrollado una novedosa habilidad para adquirir alcaloides de su dieta como defensas químicas, al menos tres veces. Sin embargo, el muestreo de taxones en busca de alcaloides ha estado sesgado hacia las especies coloridas, sin prestar atención similar a las poco conspicuas que a menudo se presume, no tienen defensas. Como resultado, nuestra comprensión de cómo evolucionan las defensas químicas en este grupo es incompleta. Aquí, proporcionamos nuevos datos que muestran que, en contraste con estudios anteriores, las especies de cada clado de ranas venenosas no defendidas tienen cantidades bajas pero cuantificables de alcaloides. Confirmamos que los dendrobátidos no defendidos consumen regularmente ácaros y hormigas, que son fuentes conocidas de alcaloides. Por lo tanto, nuestros datos sugieren que la dieta es insuficiente para explicar el fenotipo defendido. Nuestros datos respaldan la existencia de un fenotipo intermedio entre consumo y secuestro de toxinas (acumulación pasiva), que difiere del secuestro en que no implica formas derivadas de mecanismos de transporte y almacenamiento, pero da lugar a bajos niveles de acumulación de toxinas. Discutimos el concepto de acumulación pasiva y su potencial rol en el origen de defensas químicas en ranas venenosas y otros organismos que secuestran toxinas. Considerando ideas de farmacocinética, incorporamos datos nuevos y antiguos de ranas venenosas dentro de un modelo evolutivo que podría ayudar a explicar los orígenes de defensas químicas adquiridas en animales, y proporcionar una visión de los procesos moleculares que regulan el destino de las toxinas ingeridas.

The Molecules Gateway: A Homogeneous, Searchable Database of 150k Annotated Molecules from Actinomycetes

Natural products are a sustainable resource for drug discovery, but their identification in complex mixtures remains a daunting task. We present an automated pipeline that compares, harmonizes and ranks the annotations of LC-HRMS data by different tools. When applied to 7,400 extracts derived from 6,566 strains belonging to 86 actinomycete genera, it yielded 150,000 molecules after processing over 50 million MS features. The web-based Molecules Gateway provides a highly interactive access to experimental and calculated data for these molecules, along with the metadata related to extracts and producer strains. We show how the Molecules Gateway can be used to rapidly identify known hard to find microbial products, unreported analogs of known families and not yet described metabolites. The Molecules Gateway, which complements available repositories, contains annotated MS data, both acquired and computationally processed under an identical workflow, making it suitable for global analyses which reveal a large and untapped chemical diversity afforded by actinomycetes.

Push-Pull Intercropping Increases the Antiherbivore Benzoxazinoid Glycoside Content in Maize Leaf Tissue

Push-pull technology refers to a promising mixed cropping practice for sustainable agricultural intensification, which uses properties of intercrop and border crop species to defend a focal crop against pests. Currently, the most widely practiced system uses Desmodium spp. as intercrop and Brachiaria or Napier grass as border crops to protect maize (Zea mays) against both insect pests and parasitic weeds. Several previous studies have demonstrated the efficacy of the push-pull system, but research on the underlying chemical mechanisms has mostly been limited to laboratory and glasshouse experiments that may not fully reproduce the complexity of the system under natural conditions. To address this limitation, we performed a large-scale study in farmer-operated push-pull maize fields in three east African countries. We compared maize leaf extracts from plants grown on push-pull fields with maize from fields employing conventional agricultural practices to assess the influence of push-pull cultivation on the maize metabolome. We identified two benzoxazinoid glycosides, which are known to have antiherbivore properties and were present in greater relative abundance in push-pull-cultivated maize leaves across three countries. Our data thus suggest that maize cultivated under push-pull has an increased resistance to herbivore attack compared to maize grown under conventional local agricultural practices.

BioTRY: A Comprehensive Knowledge Base for Titer, Rate, and Yield of Biosynthesis

Synthetic biology is rapidly evolving into a data-intensive science that increasingly relies on massive data sets; one of its applications is the evaluation of the economic viability of fermentation processes. However, the key economic indicators, namely titer, rate, and yield (TRY), which respectively reflect the downstream processing, reactor size, and raw material costs, are not well captured in bioinformatics databases. In this paper, we present BioTRY, an intuitive and user-friendly tool that contains >5,000 biochemicals and >3,800 strains, along with over 52,000 corresponding TRY entries with original references. It is freely available at http://www.synbiohealth.cn/biotry. To our knowledge, BioTRY is the first available database on biosynthesis TRY data from original research. We anticipate that BioTRY will become a useful tool that aids researchers and decision-makers in understanding the current development state of biosynthesis and allows them to foresee potential prospects and applications for biosynthesis.

Annotating full-scan MS data using tandem MS libraries

Full-scan mass spectrometry (MS) data from both liquid chromatography (LC) and MS imaging capture multiple ion forms, including their in-source fragments. Here we leverage such fragments to structurally annotate full-scan data from LC-MS or MS imaging by matching against peak intensity scaled tandem MS spectral libraries using precursor-tolerant reverse match scoring. Applied to inflammatory bowel disease and imaging datasets, we show the approach facilitates re-analyses of data in public repositories.

Advances, opportunities, and challenges in methods for interrogating the structure activity relationships of natural products

Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.

Engineered reduction of S-adenosylmethionine alters lignin in sorghum

BACKGROUND

Lignin is an aromatic polymer deposited in secondary cell walls of higher plants to provide strength, rigidity, and hydrophobicity to vascular tissues. Due to its interconnections with cell wall polysaccharides, lignin plays important roles during plant growth and defense, but also has a negative impact on industrial processes aimed at obtaining monosaccharides from plant biomass. Engineering lignin offers a solution to this issue. For example, previous work showed that heterologous expression of a coliphage S-adenosylmethionine hydrolase (AdoMetase) was an effective approach to reduce lignin in the model plant Arabidopsis. The efficacy of this engineering strategy remains to be evaluated in bioenergy crops.

RESULTS

We studied the impact of expressing AdoMetase on lignin synthesis in sorghum (Sorghum bicolor L. Moench). Lignin content, monomer composition, and size, as well as biomass saccharification efficiency were determined in transgenic sorghum lines. The transcriptome and metabolome were analyzed in stems at three developmental stages. Plant growth and biomass composition was further evaluated under field conditions. Results evidenced that lignin was reduced by 18% in the best transgenic line, presumably due to reduced activity of the S-adenosylmethionine-dependent O-methyltransferases involved in lignin synthesis. The modified sorghum features altered lignin monomer composition and increased lignin molecular weights. The degree of methylation of glucuronic acid on xylan was reduced. These changes enabled a ~20% increase in glucose yield after biomass pretreatment and saccharification compared to wild type. RNA-seq and untargeted metabolomic analyses evidenced some pleiotropic effects associated with AdoMetase expression. The transgenic sorghum showed developmental delay and reduced biomass yields at harvest, especially under field growing conditions.

CONCLUSIONS

The expression of AdoMetase represents an effective lignin engineering approach in sorghum. However, considering that this strategy potentially impacts multiple S-adenosylmethionine-dependent methyltransferases, adequate promoters for fine-tuning AdoMetase expression will be needed to mitigate yield penalty.

Variation in insect herbivory across an urbanization gradient: The role of abiotic factors and leaf secondary metabolites

Urbanization impacts plant-herbivore interactions, which are crucial for ecosystem functions such as carbon sequestration and nutrient cycling. While some studies have reported reductions in insect herbivory in urban areas (relative to rural or natural forests), this trend is not consistent and the underlying causes for such variation remain unclear. We conducted a continental-scale study on insect herbivory along urbanization gradients for three European tree species: Quercus robur, Tilia cordata, and Fraxinus excelsior, and further investigated their biotic and abiotic correlates to get at mechanisms. To this end, we quantified insect leaf herbivory and foliar secondary metabolites (phenolics, terpenoids, alkaloids) for 176 trees across eight European cities. Additionally, we collected data on microclimate (air temperature) and soil characteristics (pH, carbon, nutrients) to test for abiotic correlates of urbanization effects directly or indirectly (through changes in plant secondary chemistry) linked to herbivory. Our results showed that urbanization was negatively associated with herbivory for Q. robur and F. excelsior, but not for T. cordata. In addition, urbanization was positively associated with secondary metabolite concentrations, but only for Q. robur. Urbanization was positively associated with air temperature for Q. robur and F. excelsior, and negatively with soil nutrients (magnesium) in the case of F. excelsior, but these abiotic variables were not associated with herbivory. Contrary to expectations, we found no evidence for indirect effects of abiotic factors via plant defences on herbivory for either Q. robur or F. excelsior. Additional biotic or abiotic drivers must therefore be accounted for to explain observed urbanization gradients in herbivory and their interspecific variation.

Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST

SUMMARY

Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets that also represent an intuitive and scientifically meaningful first stage of analysis in untargeted metabolomics. Exploiting such feature sets, feature-set testing has emerged as an approach that is widely used in genomics and targeted metabolomics pathway enrichment analyses. It allows for formally combining groupings with statistical testing into more meaningful pathway enrichment conclusions. Here, we present msFeaST (mass spectral Feature Set Testing), a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data. Feature-set testing involves statistically assessing differential abundance patterns for groups of features across experimental conditions. We developed msFeaST to make use of spectral similarity-based feature groupings generated using k-medoids clustering, where the resulting clusters serve as a proxy for grouping structurally similar features with potential biosynthesis pathway relationships. Spectral clustering done in this way allows for feature group-wise statistical testing using the globaltest package, which provides high power to detect small concordant effects via joint modeling and reduced multiplicity adjustment penalties. Hence, msFeaST provides interactive integration of the semi-quantitative experimental information with mass-spectral structural similarity information, enhancing the prioritization of features and feature sets during exploratory data analysis.

AVAILABILITY AND IMPLEMENTATION

The msFeaST workflow is freely available through https://github.com/kevinmildau/msFeaST and built to work on MacOS and Linux systems.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

Screening of Lipid-Reducing Activity and Cytotoxicity of the Exometabolome from Cyanobacteria

Cyanobacteria are rich producers of secondary metabolites, excreting some of these to the culture media. However, the exometabolome of cyanobacteria has been poorly studied, and few studies have dwelled on its characterization and bioactivity assessment. In this work, exometabolomes of 56 cyanobacterial strains were characterized by HR-ESI-LC-MS/MS. Cytotoxicity was assessed on two carcinoma cell lines, HepG2 and HCT116, while the reduction in lipids was tested in zebrafish larvae and in a steatosis model with fatty acid-overloaded human liver cells. The exometabolome analysis using GNPS revealed many complex clusters of unique compounds in several strains, with no identifications in public databases. Three strains reduced viability in HCT116 cells, namely Tolypotrichaceae BACA0428 (30.45%), Aphanizomenonaceae BACA0025 (40.84%), and Microchaetaceae BACA0110 (46.61%). Lipid reduction in zebrafish larvae was only observed by exposure to Dulcicalothrix sp. BACA0344 (60%). The feature-based molecular network shows that this bioactivity was highly correlated with two flavanones, a compound class described in the literature to have lipid reduction activity. The exometabolome characterization of cyanobacteria strains revealed a high chemodiversity, which supports it as a source for novel bioactive compounds, despite most of the time being overlooked.

Network Topology Evaluation and Transitive Alignments for Molecular Networking

Untargeted tandem mass spectrometry (MS/MS) is an essential technique in modern analytical chemistry, providing a comprehensive snapshot of chemical entities in complex samples and identifying unknowns through their fragmentation patterns. This high-throughput approach generates large data sets that can be challenging to interpret. Molecular Networks (MNs) have been developed as a computational tool to aid in the organization and visualization of complex chemical space in untargeted mass spectrometry data, thereby supporting comprehensive data analysis and interpretation. MNs group related compounds with potentially similar structures from MS/MS data by calculating all pairwise MS/MS similarities and filtering these connections to produce a MN. Such networks are instrumental in metabolomics for identifying novel metabolites, elucidating metabolic pathways, and even discovering biomarkers for disease. While MS/MS similarity metrics have been explored in the literature, the influence of network topology approaches on MN construction remains unexplored. This manuscript introduces metrics for evaluating MN construction, benchmarks state-of-the-art approaches, and proposes the Transitive Alignments approach to improve MN construction. The Transitive Alignment technique leverages the MN topology to realign MS/MS spectra of related compounds that differ by multiple structural modifications. Combining this Transitive Alignments approach with pseudoclique finding, a method for identifying highly connected groups of nodes in a network, resulted in more complete and higher-quality molecular families. Finally, we also introduce a targeted network construction technique called induced transitive alignments where we demonstrate effectiveness on a real world natural product discovery application. We release this transitive alignment technique as a high-throughput workflow that can be used by the wider research community.

Expression of dehydroshikimate dehydratase in poplar induces transcriptional and metabolic changes in the phenylpropanoid pathway

Modification of lignin in feedstocks via genetic engineering aims to reduce biomass recalcitrance to facilitate efficient conversion processes. These improvements can be achieved by expressing exogenous enzymes that interfere with native biosynthetic pathways responsible for the production of the lignin precursors. In planta expression of a bacterial 3-dehydroshikimate dehydratase in poplar trees reduced lignin content and altered the monomer composition, which enabled higher yields of sugars after cell wall polysaccharide hydrolysis. Understanding how plants respond to such genetic modifications at the transcriptional and metabolic levels is needed to facilitate further improvement and field deployment. In this work, we acquired fundamental knowledge on lignin-modified poplar expressing 3-dehydroshikimate dehydratase using RNA-seq and metabolomics. The data clearly demonstrate that changes in gene expression and metabolite abundance can occur in a strict spatiotemporal fashion, revealing tissue-specific responses in the xylem, phloem, or periderm. In the poplar line that exhibited the strongest reduction in lignin, we found that 3% of the transcripts had altered expression levels and ~19% of the detected metabolites had differential abundance in the xylem from older stems. The changes affected predominantly the shikimate and phenylpropanoid pathways as well as secondary cell wall metabolism, and resulted in significant accumulation of hydroxybenzoates derived from protocatechuate and salicylate.

Quantitative Single-Cell Mass Spectrometry Provides a Highly Resolved Analysis of Natural Product Biosynthesis Partitioning in Plants

Plants produce an extraordinary array of natural products (specialized metabolites). Notably, these structurally complex molecules are not evenly distributed throughout plant tissues but are instead synthesized and stored in specific cell types. Elucidating both the biosynthesis and function of natural products would be greatly facilitated by tracking the location of these metabolites at the cell-level resolution. However, detection, identification, and quantification of metabolites in single cells, particularly from plants, have remained challenging. Here, we show that we can definitively identify and quantify the concentrations of 16 molecules from four classes of natural products in individual cells of leaf, root, and petal of the medicinal plant Catharanthus roseus using a plate-based single-cell mass spectrometry method. We show that identical natural products show substantially different patterns of cell-type localization in different tissues. Moreover, we show that natural products are often found in a wide range of concentrations across a population of cells, with some natural products at concentrations of over 100 mM per cell. This single-cell mass spectrometry method provides a highly resolved picture of plant natural product biosynthesis partitioning at a cell-specific resolution.

An advanced metabolomic approach on grape skins untangles cultivar preferences by Drosophila suzukii for oviposition

Insects' host preferences are regulated by multiple factors whose interactions are only partly understood. Here we make use of an in-depth, untargeted metabolomic approach combining molecular networking (MN) and supervised Analysis of variance Multiblock Orthogonal Partial Least Squares (AMOPLS) to untangle egg-laying preferences of Drosophila suzukii, an invasive, highly polyphagous and destructive fruit pest originating from Southeast Asia. Based on behavioural experiments in the laboratory as well as field observation, we selected eight genetically related Vitis vinifera cultivars (e.g., Ancellotta, Galotta, Gamaret, Gamay, Gamay précoce, Garanoir, Mara and Reichensteiner) exhibiting significant differences in their susceptibility toward D. suzukii. The two most and the two least attractive red cultivars were chosen for further metabolomic analyses of their grape skins. The combination of MN and statistical AMOPLS findings with semi-quantitative detection information enabled us to identify flavonoids as interesting markers for differences in the attractiveness of the four studied grape cultivars towards D. suzukii. Overall, dihydroflavonols were accumulated in unattractive grape cultivars, while attractive grape cultivars were richer in flavonols. Crucially, both dihydroflavonols and flavonols were abundant metabolites in the semi-quantitative analysis of the extracted molecules from the grape skin. We discuss how these two flavonoid classes might influence the egg-laying behaviour of D. suzukii females and how they could serve as potential markers for D. suzukii infestations in grapes that can be potentially extended to other fruits. We believe that our novel, integrated analytical approach could also be applied to the study of other biological relationships characterised by multiple evolving parameters.

Assessing the stability of polyphenol content in red rooibos herbal tea using traditional methods and high-resolution mass spectrometry: Implications for studying dietary interventions in preclinical rodent studies

Preclinical rodent models are used to examine the relationship between tea consumption and bone health, where tea is available for rodents and typically replaced weekly. However, the extent to which the tea polyphenols change over time remains uncertain, despite its importance in preparing tea during preclinical rodent trials. Using an untargeted molecular approach, we applied a liquid chromatography quadrupole-time-of-flight mass spectrometry (LC-QTOFMS) system to assess the molecular profile of red rooibos teas throughout a 6-day aging period. We found a significant, 3-fold decrease of polyphenols involved in bone metabolism, including m-coumaric acid, catechin derivatives and courmaroyl tartaric acid over 6 days, likely due to photochemical decomposition and autooxidation within tea extracts. Using a novel untargeted workflow for polyphenol characterization, our findings revealed the complexity of polyphenols in red rooibos teas that can inform the evidence-based decisions of how often to change teas during in vivo rodent trials.

Tree phytochemical diversity and herbivory are higher in the tropics

A long-standing but poorly tested hypothesis in plant ecology and evolution is that biotic interactions play a more important role in producing and maintaining species diversity in the tropics than in the temperate zone. A core prediction of this hypothesis is that tropical plants deploy a higher diversity of phytochemicals within and across communities because they experience more herbivore pressure than temperate plants. However, simultaneous comparisons of phytochemical diversity and herbivore pressure in plant communities from the tropical to the temperate zone are lacking. Here we provide clear support for this prediction by examining phytochemical diversity and herbivory in 60 tree communities ranging from species-rich tropical rainforests to species-poor subalpine forests. Using a community metabolomics approach, we show that phytochemical diversity is higher within and among tropical tree communities than within and among subtropical and subalpine communities, and that herbivore pressure and specialization are highest in the tropics. Furthermore, we show that the phytochemical similarity of trees has little phylogenetic signal, indicating rapid divergence between closely related species. In sum, we provide several lines of evidence from entire tree communities showing that biotic interactions probably play an increasingly important role in generating and maintaining tree diversity in the lower latitudes.

Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process

Natural products (NPs) or their derivatives represent a large proportion of drugs that successfully progress through clinical trials to approval. This study explores the presence of NPs in both early- and late-stage drug discovery to determine their success rate, and the factors or features of natural products that contribute to such success. As a proxy for early drug development stages, we analyzed patent applications over several decades, finding a consistent proportion of NP, NP-derived, and synthetic-compound-based patent documents, with the latter group outnumbering NP and NP-derived ones (approximately 77% vs 23%). We next assessed clinical trial data, where we observed a steady increase in NP and NP-derived compounds from clinical trial phases I to III (from approximately 35% in phase I to 45% in phase III), with an inverse trend observed in synthetics (from approximately 65% in phase I to 55% in phase III). Finally, in vitro and in silico toxicity studies revealed that NPs and their derivatives were less toxic alternatives to their synthetic counterparts. These discoveries offer valuable insights for successful NP-based drug development, highlighting the potential benefits of prioritizing NPs and their derivatives as starting points.

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.

FragHub: A Mass Spectral Library Data Integration Workflow

Open mass spectral libraries (OMSLs) are critical for metabolite annotation and machine learning, especially given the rising volume of untargeted metabolomic studies and the development of annotation pipelines. Despite their importance, the practical application of OMSLs is hampered by the lack of standardized file formats, metadata fields, and supporting ontology. Current libraries, often restricted to specific topics or matrices, such as natural products, lipids, or the human metabolome, may limit the discovery potential of untargeted studies. The goal of FragHub is to provide users with the capability to integrate various OMSLs into a single unified format, thereby enhancing the annotation accuracy and reliability. FragHub addresses these challenges by integrating multiple OMSLs into a single comprehensive database, supporting various data formats, and harmonizing metadata. It also proposes some generic filters for the mass spectrum using a graphical user interface. Additionally, a workflow to generate in-house libraries compatible with FragHub is proposed. FragHub dynamically segregates libraries based on ionization modes and chromatography techniques, thereby enhancing data utility in metabolomic research. The FragHub Python code is publicly available under a MIT license, at the following repository: https://github.com/eMetaboHUB/FragHub. Generated data can be accessed at 10.5281/zenodo.11057687.

Cell-Death Metabolites from Cocconeis scutellum var. parva Identified by Integrating Bioactivity-Based Fractionation and Non-Targeted Metabolomic Approaches

Epiphytic diatoms growing in Mediterranean seagrass meadows, particularly those of the genus Cocconeis, are abundant and ecologically significant, even in naturally acidified environments. One intriguing aspect of some benthic diatoms is their production of an unidentified cell-death-promoting compound, which induces destruction of the androgenic gland in Hippolyte inermis Leach, 1816, a shrimp exhibiting protandric hermaphroditism, principally under normal environmental pH levels. The consumption of Cocconeis spp. by this shrimp is vital for maintaining the stability of its natural populations. Although many attempts have been made to reveal the identity of the apoptotic compound, it is still unknown. In this study, we strategically integrated a bioactivity-based fractionation, a metabolomic approach, and two different experimental avenues to identify potential apoptotic metabolites from Cocconeis scutellum var. parva responsible for the sex reversal in H. inermis. Our integrated analysis uncovered two potential candidate metabolites, one putatively identified as a lysophosphatidylglycerol (LPG) (16:1) and the other classified as a fatty acid ester. This is the first time LPG (16:1) has been reported in C. scutellum var. parva and associated with cell-death processes. These candidate metabolites mark substantial progress in elucidating the factors responsible for triggering the removal of the androgenic gland in the early post-larval phases of H. inermis.

Dietary iron intake has long-term effects on the fecal metabolome and microbiome

Iron is essential for life, but its imbalances can lead to severe health implications. Iron deficiency is the most common nutrient disorder worldwide, and iron dysregulation in early life has been found to cause long-lasting behavioral, cognitive, and neural effects. However, little is known about the effects of dietary iron on gut microbiome function and metabolism. In this study, we sought to investigate the impact of dietary iron on the fecal metabolome and microbiome by using mice fed with three diets with different iron content: an iron deficient, an iron sufficient (standard), and an iron overload diet for 7 weeks. Additionally, we sought to understand whether any observed changes would persist past the 7-week period of diet intervention. To assess this, all feeding groups were switched to a standard diet, and this feeding continued for an additional 7 weeks. Analysis of the fecal metabolome revealed that iron overload and deficiency significantly alter levels of peptides, nucleic acids, and lipids, including di- and tri-peptides containing branched-chain amino acids, inosine and guanosine, and several microbial conjugated bile acids. The observed changes in the fecal metabolome persist long after the switch back to a standard diet, with the cecal gut microbiota composition and function of each group distinct after the 7-week standard diet wash-out. Our results highlight the enduring metabolic consequences of nutritional imbalances, mediated by both the host and gut microbiome, which persist after returning to the original standard diets.

Investigation of the antimycobacterial activity of African medicinal plants combined with chemometric analysis to identify potential leads

The emergence of drug-resistant Mycobacterium tuberculosis strains is a threat to global health necessitating the discovery of novel chemotherapeutic agents. Natural products drug discovery, which previously led to the discovery of rifamycins, is a valuable approach in this endeavor. Against this backdrop, we set out to investigate the in vitro antimycobacterial properties of medicinal plants from Ghana and South Africa, evaluating 36 extracts and their 252 corresponding solid phase extraction (SPE) generated fractions primarily against the non-pathogenic Mycobacterium smegmatis and Mycobacterium aurum species. The most potent fraction was further evaluated in vitro against infectious M. tuberculosis strain. Crinum asiaticum (bulb) (Amaryllidaceae) emerged as the most potent plant species with specific fractions showing exceptional, near equipotent activity against the non-pathogenic Mycobacterium species (0.39 µg/ml ≤ MIC ≤ 25 µg/ml) with one fraction being moderately active (MIC = 32.6 µg/ml) against M. tuberculosis. Metabolomic analysis led to the identification of eight compounds predicted to be active against M. smegmatis and M. aurum. In conclusion, from our comprehensive study, we generated data which provided an insight into the antimycobacterial properties of Ghanaian and South African plants. Future work will be focused on the isolation and evaluation of the compounds predicted to be active.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

False-positive MDA findings in HRMS-based screening of putrefied postmortem blood samples-Identification of the interference as N-acetyltyramine

An unidentified compound in putrefied postmortem blood samples showed identical accurate mass and chromatographic behavior as 3,4-methylenedioxyamphetamine (MDA) and led to false-positive preliminary screening results. The aim of the study was to identify this unknown interference. Postmortem blood samples were analyzed after protein precipitation on a QExactive Focus high-resolution mass spectrometer (Thermo Fisher, Germany) coupled to a RP C18 column (Macherey-Nagel, Germany). Based on the analysis of mass spectrometry (MS) adducts and isotope ratios using fullscan (m/z 134-330) information, the empiric formula of the protonated molecule [M + H]+ of the unknown compound was found to be C10H14O2N (+ 0.6 ppm). Product ion spectra recorded using normalized collision energy 22% showed a base peak of C8H9O1 (+ 1.5 ppm) and a low-abundant water loss to C7H9 (+ 1.9 ppm), neutral losses of C2H2O and NH3 were found. Based on fullscan and MS-MS information and under consideration of the observed order of neutral losses, the compound was presumptively identified as N-acetyltyramine. This assumption was supported by SIRIUS software showing a SIRIUS score of 99.43% for N-acetyltyramine. Finally, the putative structure annotation was confirmed by a reference compound. The described false-positive MDA findings could be attributed to the presence of N-acetyltyramine in putrefied blood samples. Being an isomer of MDA, N-acetyltyramine could not be distinguished by high-resolution data of the protonated molecules. The presented results once again highlight that false-positive findings may occur even in hyphenated high-resolution mass spectrometry (HRMS) when using full-scan information only.

A Machine Learning Approach for Predicting Caco-2 Cell Permeability in Natural Products from the Biodiversity in Peru

BACKGROUND

Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure-property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru.

METHODS

A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM-RF-GBM model.

RESULTS

An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39-0.40 and R2 = 0.73-0.74. Notably, the SVM-RF-GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness.

CONCLUSIONS

Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.

Molecular networking-based mass spectral identification of Brucea javanica (L.) Merr. metabolites and their selective binding affinities for dengue virus enzymes

Brucea javanica, a valued traditional medicinal plant in Malaysia, known for its fever-treating properties yet remains underexplored for its potential antiviral properties against dengue. This study aims to simultaneously identify chemical classes and metabolites within B. javanica using molecular networking (MN), by Global Natural Product Social (GNPS), and SIRIUS in silico annotation. Liquid chromatography-mass spectrometry (LC-MS2)-based MN explores chemical diversity across four plant parts (leaves, roots, fruits, and stem bark), revealing diverse metabolites such as tryptophan-derived alkaloids, terpenoids, and octadecadenoids. Simultaneous LC-MS2 and MN analyses reveal a discriminative capacity for individual plant components, with roots accumulating tryptophan alkaloids, fruits concentrating quassinoids, leaves containing fusidanes, and stem bark primarily characterised by simple indoles. Subsequently, extracts were evaluated for dengue antiviral activity using adenosine triphosphate (ATP) and plaque assays, indicates potent efficacy in the dichloromethane (DCM) extract from roots (EC50 = 0.3 μg/mL, SI = 10). Molecular docking analysis of two major compounds; canthin-6-one (264) and 1-hydroxy-11-methoxycanthin-6-one (275) showed potential binding interactions with active sites of NS5 RNA-dependent RNA polymerase (RdRp) of dengue virus (DENV) protein. Subsequent in vitro evaluation revealed compounds 264 and 275 had a promising dengue antiviral activity with SI value of 63 and 1.85. These identified metabolites emerge as potential candidates for further evaluation in dengue antiviral activities.

Synthetic biology advances towards a bio-based society in the era of artificial intelligence

Synthetic biology is a rapidly emerging field with broad underlying applications in health, industry, agriculture, or environment, enabling sustainable solutions for unmet needs of modern society. With the very recent addition of artificial intelligence (AI) approaches, this field is now growing at a rate that can help reach the envisioned goals of bio-based society within the next few decades. Integrating AI with plant-based technologies, such as protein engineering, phytochemicals production, plant system engineering, or microbiome engineering, potentially disruptive applications have already been reported. These include enzymatic synthesis of new-to-nature molecules, bioelectricity generation, or biomass applications as construction material. Thus, in the not-so-distant future, synthetic biologists will help attain the overarching goal of a sustainable yet efficient production system for every aspect of society.

Phytochemical diversity and their adaptations to abiotic and biotic pressures in fine roots across a climatic gradient

Phytochemicals and their ecological significance are long ignored in trait-based ecology. Moreover, the adaptations of phytochemicals produced by fine roots to abiotic and biotic pressures are less understood. Here, we explored the fine roots metabolomes of 315 tree species and their rhizosphere microbiome in southwestern China spanning tropical, subtropical, and subalpine forest ecosystems, to explore phytochemical diversity and endemism patterns of various metabolic pathways and phytochemical-microorganism interactions. We found that subalpine species showed higher phytochemical diversity but lower interspecific variation than tropical species, which favors coping with high abiotic pressures. Tropical species harbored higher interspecific phytochemical variation and phytochemical endemism, which favors greater species coexistence and adaptation to complex biotic pressures. Moreover, there was evidence of widespread chemical niche partitioning of closely related species in all regions, and phytochemicals showed a weak phylogenetic signal, but were regulated by abiotic and biotic pressures. Our findings support the Latitudinal Biotic Interaction Hypothesis, i.e., the intensity of phytochemical-microorganism interactions decreases from tropical to subalpine regions, which promotes greater microbial community turnover and phytochemical niche partitioning of host plants in the tropics than in higher latitude forests. Our study reveals the convergent phytochemical diversity patterns of various pathways and their interactions with microorganism, thus promoting species coexistence.

Unveiling the Bioprospecting Efficacy and Textile Dyeing of a Novel Endophytic Mycobial Red Pigment

Natural pigments are becoming increasingly popular owing of their reliability. Microbial pigments provide an alternative to natural colours. A total of 24 fungal cultures were collected from leaf bits of Senna auriculata, with one strain (FNG1) producing an extracellular red orange pigment. Nigrospora oryzae was confirmed by using physical criteria and molecular phylogenetic study by using ITS and β- tubulin analysis. In EtOAc, the crude red pigment was the most soluble. The TLC analysis was used to partly purify the natural pigment. The partially purified fungal pigment was used in successive bioprospecting studies. The antimicrobial activity of the partially purified sample was assessed against eight human pathogens, with Leucobacter AA7 showing the largest zone of inhibition (200-500 µg/mL). The compound's DPPH scavenging activity enhanced from 38.2 to 67.9%, with an IC50 value of 34.195 ± 2.33 µg/mL. Cancer cells were suppressed by partly pure fungal pigment, but non-cancerous HEK 293 cells were unaffected. The GC-MS analysis was used to characterize the molecule present in the partly purified pigment. In addition, the cotton textiles have the greatest staining capability for crude mycobial pigment, which dyes quickly and has a negative cytotoxicity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12088-024-01211-y.

Compendium of Metabolomic and Genomic Datasets for Cyanobacteria: Mined the Gap

Cyanobacterial blooms, producing toxic secondary metabolites, are becoming increasingly common phenomena in the face of rising global temperatures. They are the world's most abundant photosynthetic organisms, largely owing their success to a range of highly diverse and complex natural products possessing a broad spectrum of different bioactivities. Over 2600 compounds have been isolated from cyanobacteria thus far, and their characterisation has revealed unusual and useful chemistries and motifs including alkynes, halogens, and non-canonical amino acids. Genome sequencing of cyanobacteria lags behind natural product isolation, with only 19% of cyanobacterial natural products associated with a sequenced organism. Recent advances in meta(genomics) provide promise to narrow this gap and has also facilitated the uprise of combined genomic and metabolomic approaches, heralding a new era of discovery of novel compounds. Analyses of the datasets described within this manuscript reveal the asynchrony of current genomic and metabolomic data, highlight the chemical diversity of cyanobacterial natural products. Linked to this manuscript, we make these manually curated datasets freely accessible for the public to facilitate further research in this important area.

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

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

SARS-CoV-2 infection unevenly impacts metabolism in the coronal periphery of the lungs

COVID-19 significantly decreases amino acids, fatty acids, and most eicosanoidsSARS-CoV-2 preferentially localizes to central lung tissueMetabolic disturbance is highest in peripheral tissue, not central like viral loadSpatial metabolomics allows detection of metabolites not altered overallSARS-CoV-2, the virus responsible for COVID-19, is a highly contagious virus that can lead to hospitalization and death. COVID-19 is characterized by its involvement in the lungs, particularly the lower lobes. To improve patient outcomes and treatment options, a better understanding of how SARS-CoV-2 impacts the body, particularly the lower respiratory system, is required. In this study, we sought to understand the spatial impact of COVID-19 on the lungs of mice infected with mouse-adapted SARS2-N501Y MA30 . Overall, infection caused a decrease in fatty acids, amino acids, and most eicosanoids. When analyzed by segment, viral loads were highest in central lung tissue, while metabolic disturbance was highest in peripheral tissue. Infected peripheral lung tissue was characterized by lower levels of fatty acids and amino acids when compared to central lung tissue. This study highlights the spatial impacts of SARS-CoV-2 and helps explain why peripheral lung tissue is most damaged by COVID-19.

Environmental metabolomics characterization of modern stromatolites and annotation of ibhayipeptolides

Lithified layers of complex microbial mats known as microbialites are ubiquitous in the fossil record, and modern forms are increasingly identified globally. A key challenge to developing an understanding of microbialite formation and environmental role is how to investigate complex and diverse communities in situ. We selected living, layered microbialites (stromatolites) in a peritidal environment near Schoenmakerskop, Eastern Cape, South Africa to conduct a spatial survey mapping the composition and small molecule production of the microbial communities from environmental samples. Substrate core samples were collected from nine sampling stations ranging from the upper point of the freshwater inflow to the lower marine interface where tidal overtopping takes place. Substrate cores provided material for parallel analyses of microbial community diversity by 16S rRNA gene amplicon sequencing and metabolomics using LC-MS2. Species and metabolite diversities were correlated, and prominent specialized metabolites were targeted for preliminary characterization. A new series of cyclic hexadepsipeptides, named ibhayipeptolides, was most abundant in substrate cores of submerged microbialites. These results demonstrate the detection and identification of metabolites from mass-limited environmental samples and contribute knowledge about microbialite chemistry and biology, which facilitates future targeted studies of specialized metabolite function and biosynthesis.

B. Trivella D. NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

Production of Polyphenolic Natural Products by Bract-Derived Tissue Cultures of Three Medicinal Tilia spp.: A Comparative Untargeted Metabolomics Study

Medicinal plant tissue cultures are potential sources of bioactive compounds. In this study, we report the chemical characterization of the callus cultures of three medicinal Tilia spp. (Tilia cordata, Tilia vulgaris and Tilia tomentosa), along with the comparison to bracts and flowers of the same species. Our aim was to show that calli of Tilia spp. are good alternatives to the calli of T. americana for the production of polyphenols and are better sources of a subset of polyphenolic metabolites, compared to the original organs. Calli were initiated from young bracts and grown on woody plant medium containing 1 mg L-1 2,4-D and 0.1 mg L-1 BAP. For chemical characterization, a quality-controlled untargeted metabolomics approach and the quantification of several bioactive compounds was performed with the use of LC-ESI-MS/MS. While bracts and flowers contained flavonoid glycosides (astragalin, isoquercitrin) as major polyphenols, calli of all species contained catechins, coumarins (fraxin, esculin and scopoletin) and flavane aglyca. T. tomentosa calli contained 5397 µg g DW-1 catechin, 201 µg g DW-1 esculin, 218 µg g DW-1 taxifolin and 273 µg g DW-1 eriodictyol, while calli from other species contained lower amounts. T. cordata and T. tomentosa flowers were rich in isoquercitrin, containing 8134 and 6385 µg g DW-1, respectively. The currently tested species contained many of the bioactive metabolites described from T. americana. The production of catechin was shown to be comparable to the most efficient tissue cultures reported. Flowers and bracts contained flavonoid glycosides, including tiliroside, resembling bioactive fractions of T. americana. In addition, untargeted metabolomics has shown fingerprint-like differences among species, highlighting possible chemotaxonomic and quality control applications, especially for bracts.

Urban and agricultural influences on the coastal dissolved organic matter pool in the Algoa Bay estuaries

While anthropogenic pollution is a major threat to aquatic ecosystem health, our knowledge of the presence of xenobiotics in coastal Dissolved Organic Matter (DOM) is still relatively poor. This is especially true for water bodies in the Global South with limited information gained mostly from targeted studies that rely on comparison with authentic standards. In recent years, non-targeted tandem mass spectrometry has emerged as a powerful tool to collectively detect and identify pollutants and biogenic DOM components in the environment, but this approach has yet to be widely utilized for monitoring ecologically important aquatic systems. In this study we compared the DOM composition of Algoa Bay, Eastern Cape, South Africa, and its two estuaries. The Swartkops Estuary is highly urbanized and severely impacted by anthropogenic pollution, while the Sundays Estuary is impacted by commercial agriculture in its catchment. We employed solid-phase extraction followed by liquid chromatography tandem mass spectrometry to annotate more than 200 pharmaceuticals, pesticides, urban xenobiotics, and natural products based on spectral matching. The identification with authentic standards confirmed the presence of methamphetamine, carbamazepine, sulfamethoxazole, N-acetylsulfamethoxazole, imazapyr, caffeine and hexa(methoxymethyl)melamine, and allowed semi-quantitative estimations for annotated xenobiotics. The Swartkops Estuary DOM composition was strongly impacted by features annotated as urban pollutants including pharmaceuticals such as melamines and antiretrovirals. By contrast, the Sundays Estuary exhibited significant enrichment of molecules annotated as agrochemicals widely used in the citrus farming industry, with predicted concentrations for some of them exceeding predicted no-effect concentrations. This study provides new insight into anthropogenic impact on the Algoa Bay system and demonstrates the utility of non-targeted tandem mass spectrometry as a sensitive tool for assessing the health of ecologically important coastal ecosystems and will serve as a valuable foundation for strategizing long-term monitoring efforts.

Cynipid wasps systematically reprogram host metabolism and restructure cell walls in developing galls

Many insects have evolved the ability to manipulate plant growth to generate extraordinary structures called galls, in which insect larva can develop while being sheltered and feeding on the plant. In particular, cynipid (Hymenoptera: Cynipidae) wasps have evolved to form morphologically complex galls and generate an astonishing array of gall shapes, colors, and sizes. However, the biochemical basis underlying these remarkable cellular and developmental transformations remains poorly understood. A key determinant in plant cellular development is cell wall deposition that dictates the physical form and physiological function of newly developing cells, tissues, and organs. However, it is unclear to what degree cell walls are restructured to initiate and support the formation of new gall tissue. Here, we characterize the molecular alterations underlying gall development using a combination of metabolomic, histological, and biochemical techniques to elucidate how valley oak (Quercus lobata) leaf cells are reprogrammed to form galls. Strikingly, gall development involves an exceptionally coordinated spatial deposition of lignin and xylan to form de novo gall vasculature. Our results highlight how cynipid wasps can radically change the metabolite profile and restructure the cell wall to enable the formation of galls, providing insights into the mechanism of gall induction and the extent to which plants can be entirely reprogrammed to form unique structures and organs.

Metabolomics on Apple (Malus domestica) Cuticle-Search for Authenticity Markers

The profile of secondary metabolites present in the apple cuticular layer is not only characteristic of a particular apple cultivar; it also dynamically reflects various external factors in the growing environment. In this study, the possibility of authenticating apple samples by analyzing their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. A total of 274 authentic apple samples from four cultivars harvested in the Czech Republic and Poland between 2020 and 2022 were analyzed. The complex data generated, processed using univariate and multivariate statistical methods, enabled the building of classification models to distinguish apple cultivars as well as their geographical origin. The models showed very good performance in discriminating Czech and Polish samples for three out of four cultivars: "Gala", "Golden Delicious" and "Idared". Moreover, the validity of the models was tested over several harvest seasons. In addition to metabolites of the triterpene biosynthetic pathway, the diagnostic markers were mainly wax esters. "Jonagold", which is known to be susceptible to mutations, was the only cultivar for which an unambiguous classification of geographical origin was not possible.

In silico biotechnological potential of Bacillus sp. strain MHSD_37 bacterial endophyte

BACKGROUND

Endophytic bacteria possess a range of unique characteristics that enable them to successfully interact with their host and survive in adverse environments. This study employed in silico analysis to identify genes, from Bacillus sp. strain MHSD_37, with potential biotechnological applications.

RESULTS

The strain presented several endophytic lifestyle genes which encode for motility, quorum sensing, stress response, desiccation tolerance and root colonisation. The presence of plant growth promoting genes such as those involved in nitrogen fixation, nitrate assimilation, siderophores synthesis, seed germination and promotion of root nodule symbionts, was detected. Strain MHSD_37 also possessed genes involved in insect virulence and evasion of defence system. The genome analysis also identified the presence of genes involved in heavy metal tolerance, xenobiotic resistance, and the synthesis of siderophores involved in heavy metal tolerance. Furthermore, LC-MS analysis of the excretome identified secondary metabolites with biological activities such as anti-cancer, antimicrobial and applications as surfactants.

CONCLUSIONS

Strain MHSD_37 thereby demonstrated potential biotechnological application in bioremediation, biofertilisation and biocontrol. Moreover, the strain presented genes encoding products with potential novel application in bio-nanotechnology and pharmaceuticals.

Comprehensive mass spectrometric metabolomic profiling of a chemically diverse collection of plants of the Celastraceae family

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family.

NIMO: A Natural Product-Inspired Molecular Generative Model Based on Conditional Transformer

Natural products (NPs) have diverse biological activity and significant medicinal value. The structural diversity of NPs is the mainstay of drug discovery. Expanding the chemical space of NPs is an urgent need. Inspired by the concept of fragment-assembled pseudo-natural products, we developed a computational tool called NIMO, which is based on the transformer neural network model. NIMO employs two tailor-made motif extraction methods to map a molecular graph into a semantic motif sequence. All these generated motif sequences are used to train our molecular generative models. Various NIMO models were trained under different task scenarios by recognizing syntactic patterns and structure-property relationships. We further explored the performance of NIMO in structure-guided, activity-oriented, and pocket-based molecule generation tasks. Our results show that NIMO had excellent performance for molecule generation from scratch and structure optimization from a scaffold.

Integration of Wnt-inhibitory activity and structural novelty scoring results to uncover novel bioactive natural products: new Bicyclo[3.3.1]non-3-ene-2,9-diones from the leaves of Hymenocardia punctata

In natural products (NPs) research, methods for the efficient prioritization of natural extracts (NEs) are key for discovering novel bioactive NPs. In this study a biodiverse collection of 1,600 NEs, previously analyzed by UHPLC-HRMS2 metabolite profiling was screened for Wnt pathway regulation. The results of the biological screening drove the selection of a subset of 30 non-toxic NEs with an inhibitory IC50 ≤ 5 μg/mL. To increase the chance of finding structurally novel bioactive NPs, Inventa, a computational tool for automated scoring of NEs based on structural novelty was used to mine the HRMS2 analysis and dereplication results. After this, four out of the 30 bioactive NEs were shortlisted by this approach. The most promising sample was the ethyl acetate extract of the leaves of Hymenocardia punctata (Phyllanthaceae). Further phytochemical investigations of this species resulted in the isolation of three known prenylated flavones (3, 5, 7) and ten novel bicyclo[3.3.1]non-3-ene-2,9-diones (1, 2, 4, 6, 8-13), named Hymenotamayonins. Assessment of the Wnt inhibitory activity of these compounds revealed that two prenylated flavones and three novel bicyclic compounds showed interesting activity without apparent cytotoxicity. This study highlights the potential of combining Inventa's structural novelty scores with biological screening results to effectively discover novel bioactive NPs in large NE collections.

UHPLC-MS/MS and GC-MS Metabolic Profiling of a Medicinal Flourensia Fiebrigii Chemotype

The comparative metabolic profiling and their biological properties of eight extracts obtained from diverse parts (leaves, flowers, roots) of the medicinal plant Flourensia fiebrigii S.F. Blake, a chemotype growing in highland areas (2750 m a.s.l.) of northwest Argentina, were investigated. The extracts were analysed by GC-MS and UHPLC-MS/MS. GC-MS analysis revealed the presence of encecalin (relative content: 24.86 %) in ethereal flower extract (EF) and this benzopyran (5.93 %) together sitosterol (11.35 %) in the bioactive ethereal leaf exudate (ELE). By UHPLC-MS/MS the main compounds identified in both samples were: limocitrin, (22.31 %), (2Z)-4,6-dihydroxy-2-[(4-hydroxy-3,5-dimethoxyphenyl)methylidene]-1-benzofuran-3-one (21.31 %), isobavachin (14.47 %), naringenin (13.50 %), and sternbin, (12.49 %). Phytocomplexes derived from aerial parts exhibited significant activity against biofilm production of Pseudomonas aeruginosa and Staphylococcus aureus, reaching inhibitions of 74.7-99.9 % with ELE (50 μg/mL). Notably, the extracts did not affect nutraceutical and environmental bacteria, suggesting a selective activity. ELE also showed the highest reactive species scavenging ability. This study provides valuable insights into the potential applications of this chemotype.

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.

Herptile gut microbiomes: a natural system to study multi-kingdom interactions between filamentous fungi and bacteria

Reptiles and amphibians (herptiles) are some of the most endangered and threatened species on the planet and numerous conservation strategies are being implemented with the goal of ensuring species recovery. Little is known, however, about the gut microbiome of wild herptiles and how it relates to the health of these populations. Here, we report results from the gut microbiome characterization of both a broad survey of herptiles, and the correlation between the fungus Basidiobolus, and the bacterial community supported by a deeper, more intensive sampling of Plethodon glutinosus, known as slimy salamanders. We demonstrate that bacterial communities sampled from frogs, lizards, and salamanders are structured by the host taxonomy and that Basidiobolus is a common and natural component of these wild gut microbiomes. Intensive sampling of multiple hosts across the ecoregions of Tennessee revealed that geography and host:geography interactions are strong predictors of distinct Basidiobolus operational taxonomic units present within a given host. Co-occurrence analyses of Basidiobolus and bacterial community diversity support a correlation and interaction between Basidiobolus and bacteria, suggesting that Basidiobolus may play a role in structuring the bacterial community. We further the hypothesis that this interaction is advanced by unique specialized metabolism originating from horizontal gene transfer from bacteria to Basidiobolus and demonstrate that Basidiobolus is capable of producing a diversity of specialized metabolites including small cyclic peptides.IMPORTANCEThis work significantly advances our understanding of biodiversity and microbial interactions in herptile microbiomes, the role that fungi play as a structural and functional members of herptile gut microbiomes, and the chemical functions that structure microbiome phenotypes. We also provide an important observational system of how the gut microbiome represents a unique environment that selects for novel metabolic functions through horizontal gene transfer between fungi and bacteria. Such studies are needed to better understand the complexity of gut microbiomes in nature and will inform conservation strategies for threatened species of herpetofauna.