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Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net . Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

Metabolism is involved in both pharmacology and toxicology of most xenobiotics including drugs. Yet, visualization tools facilitating metabolism exploration are still underused, despite the availibility of pertinent bioinformatics solutions. Since molecular networking appears as a suitable tool to explore structurally related molecules, we aimed to investigate its interest in in vitro metabolism exploration. Quetiapine, a widely prescribed antipsychotic drug, undergoes well-described extensive metabolism, and is therefore an ideal candidate for such a proof of concept. Quetiapine was incubated in metabolically competent human liver cell models (HepaRG) for different times (0 h, 3 h, 8 h, 24 h) with or without cytochrom P450 (CYP) inhibitor (ketoconazole as CYP3A4/5 inhibitor and quinidine as CYP2D6 inhibitor), in order to study its metabolism kinetic and pathways. HepaRG culture supernatants were analyzed on an ultra-high performance liquid chromatography coupled with tandem mass spectrometry (LC-HRMS/MS). Molecular networking approach on LC-HRMS/MS data allowed to quickly visualize the quetiapine metabolism kinetics and determine the major metabolic pathways (CYP3A4/5 and/or CYP2D6) involved in metabolite formation. In addition, two unknown putative metabolites have been detected. In vitro metabolite findings were confirmed in blood sample from a patient treated with quetiapine. This is the first report using LC-HRMS/MS untargeted screening and molecular networking to explore in vitro drug metabolism. Our data provide new evidences of the interest of molecular networking in drug metabolism exploration and allow our in vitro model consistency assessment.

Mass spectrometry (MS) offers unrivalled sensitivity for the metabolite profiling of complex biological matrices encountered in natural products (NP) research. The massive and complex sets of spectral data generated by such platforms require computational approaches for their interpretation. Within such approaches, computational metabolite annotation automatically links spectral data to candidate structures via a score, which is usually established between the acquired data and experimental or theoretical spectral databases (DB). This process leads to various candidate structures for each MS features. However, at this stage, obtaining high annotation confidence level remains a challenge notably due to the extensive chemodiversity of specialized metabolomes. The design of a metascore is a way to capture complementary experimental attributes and improve the annotation process. Here, we show that integrating the taxonomic position of the biological source of the analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and complement the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5- to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata, particularly biological sources, is timely and critical for the NP research community.

Actinomycetes are well‐known for producing a diverse array of specialised metabolites with various bioactivities; yet, identifying metabolites with targeted activity against specific pathogens remains challenging. In this study, we employed a comparative metabolomic and genomic approach on 63 actinomycete strains differing in their ability to inhibit or alter the mycelial growth of Phytophthora infestans, the causal agent of potato late blight. This comparative approach efficiently pinpointed approximately 1000 mass spectrometry features linked to active extracts, out of 16,500 detected features. Our analysis putatively identified over 75 compounds with potential activity against P. infestans, including borrelidin, actinomycin D, antimycin A, macbecin I, myriocin and ikarugamycin. Our study shows that leveraging multi‐omics analysis of phylogenetically related strains with differential activity is a promising strategy which, combined with a relatively high throughput metabolite extraction method, advanced mass spectrometry and cutting‐edge tools for bacterial metabolite annotation and prediction, allowed a straightforward selection of interesting candidate compounds for the biological control of an important plant pathogen such as P. infestans. The methodology outlined here offers broader applicability for identifying bioactive compounds underlying any phenotype of interest, provided this phenotype varies in phylogenetically closely related strains. Using a comparative metabolomic and genomic approach on 63 actinomycetes, this study identified 75 compounds associated with the strains' ability to inhibit the late blight‐causing agent Phytophthora infestans. This study highlighted the promise of applying multi‐omics techniques on closely related strains differing in biological activity to pinpoint the responsible metabolites.

To anticipate potential seedling damage, plants block seed germination under unfavorable conditions. Previous studies investigated how seed germination is controlled in response to abiotic stresses through gibberellic and abscisic acid signaling. However, little is known about whether seeds respond to rhizosphere bacterial pathogens. We found that Arabidopsis seed germination is blocked in the vicinity of the plant pathogen Pseudomonas aeruginosa. We identified L-2-amino-4-methoxy-trans-3-butenoic acid (AMB), released by P. aeruginosa, as a biotic compound triggering germination arrest. We provide genetic evidence that in AMB-treated seeds DELLA factors promote the accumulation of the germination repressor ABI5 in a GA-independent manner. AMB production is controlled by the quorum sensing system IQS. In vitro experiments show that the AMB-dependent germination arrest protects seedlings from damage induced by AMB. We discuss the possibility that this could serve as a protective response to avoid severe seedling damage induced by AMB and exposure to a pathogen. The plant embryo within a seed is well protected. While it cannot stay within the seed forever, the embryo can often wait for the right conditions before it develops into a seedling and continues its life cycle. Indeed, plants have evolved several ways to time this process – which is known as germination – to maximize the chances that their seedlings will survive. For example, if the environment is too hot or too dark, the seed will make a hormone that stops it from germinating. In addition to environmental factors like light and temperature, a seed in the real word is continuously confronted with soil microbes that may harm or benefit the plant. However, few researchers have asked whether seeds control their germination in response to other living organisms. The bacterium Pseudomonas aeruginosa lives in a wide spectrum of environments, including the soil, and can cause diseases in both and plants and animals. Chahtane et al. now report that seeds of the model plant Arabidopsis thaliana do indeed repress their germination when this microbe is present. Specifically, the seeds respond to a molecule released from the bacteria called L-2-amino-4-methoxy-trans-3-butenoic acid, or AMB for short. Like the bacteria, AMB is harmful to young seedlings, but Chahtane et al. showed that the embryo within the seed is protected from its toxic effects. Further experiments revealed that the seed's response to the bacterial molecule requires many of the same signaling components that repress germination when environmental conditions are unfavorable. However, Chahtane et al. note that AMB activates these components in an unusual way that they still do not understand. The genes that control the production of AMB are known to also control how bacterial populations behave as they accumulate to high densities. It is therefore likely that Pseudomonas aeruginosa would make AMB if it reached a high density in the soil. This raises the possibility that plants have specifically evolved to stop germination if there are enough microbes nearby to pose a risk of disease. This hypothesis, however, is only one of several possible explanations and remains speculative at this stage; further work is now needed to evaluate it. Nevertheless, identifying how AMB interferes with the signaling components that control germination and plant growth may guide the design of new herbicides that could, for example, control weeds in the farming industry.

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. This work demonstrates that the analysis of the metabolomes and the genomes of phylogenetically closely related strains which show different phenotypes can lead in a straightforward way to elucidate the compounds responsible for the observed phenotypes. Using comparative metabolomics coupled to molecular networking on three Streptomyces strains, we identified borrelidin as the active compound underlying the inhibition of Phytophthora infestans and other fungal plant pathogens.

Acebutolol is a β1-selective adrenergic receptor antagonist with moderate membrane-stabilizing activity and intrinsic sympathomimetic activity; accordingly, the drug is indicated in hypertension, angina pectoris, and arrhythmia. However, acebutolol’s beta-blocking properties also extend the QRS and QTc intervals, and may predispose the patient to ventricular tachydysrhythmia. Here, we report autopsy and toxicological findings on a fatal case of acebutolol self-poisoning in a 70-year-old woman. Toxicological analyses of post-mortem samples (using a liquid chromatography high-resolution mass spectrometry (LC-HR-MS) method) highlighted high concentrations of acebutolol and its metabolite diacetolol in femoral blood (92.8 mg/L and 21.2 mg/L, respectively) and other matrices (cardiac blood, urine, bile, and gastric contents). A molecular networking approach provided useful information on acebutolol’s metabolism and revealed the existence of an unknown phase II metabolite of acebutolol. Molecular networking also facilitated visualization of the complex LC-HR-MS/MS datasets and the sample-to-sample comparisons that confirmed massive acebutolol intoxication by ingestion.

While analytical techniques in natural products research massively shifted to liquid chromatography-mass spectrometry, lichen chemistry remains reliant on limited analytical methods, Thin Layer Chromatography being the gold standard. To meet the modern standards of metabolomics within lichenochemistry, we announce the publication of an open access MS/MS library with 250 metabolites, coined LDB for Lichen DataBase, providing a comprehensive coverage of lichen chemodiversity. These were donated by the Berlin Garden and Botanical Museum from the collection of Siegfried Huneck to be analyzed by LC-MS/MS. Spectra at individual collision energies were submitted to MetaboLights (https://www.ebi.ac.uk/metabolights/MTBLS999) while merged spectra were uploaded to the GNPS platform (CCMSLIB00004751209 to CCMSLIB00004751517). Technical validation was achieved by dereplicating three lichen extracts using a Molecular Networking approach, revealing the detection of eleven unique molecules that would have been missed without LDB implementation to the GNPS. From a chemist’s viewpoint, this database should help streamlining the isolation of formerly unreported metabolites. From a taxonomist perspective, the LDB offers a versatile tool for the chemical profiling of newly reported species.

This study reports the isolation of new Streptomyces strains with strong plant-protective potential mediated by their production of specialized metabolites. Using the broad host range pathogenic fungus Botrytis cinerea , we demonstrate that the cell-free filtrate of selected Streptomyces isolates efficiently inhibits different developmental stages of the fungus, including mycelial growth and the epidemiologically relevant spore germination. Beyond in vitro experiments, the strains and their metabolites also efficiently protected plants against the disease caused by this pathogen. This work further identifies oligomycins as active compounds involved in the observed antifungal activity of the strains. This work shows that we can harness the natural ability of soil-borne microbes and of their metabolites to efficiently fight other microbes responsible for significant crop losses. This opens the way to the development of environmentally friendly health protection measures for crops of agronomical relevance, based on these newly isolated strains or their metabolic extracts containing oligomycins.

Many targeted natural product isolation approaches rely on the use of pre-existing bioactivity information to inform the strategy used for the isolation of new bioactive compounds. Bioactivity information can be available either in the form of prior assay data or via Structure Activity Relationship (SAR) information which can indicate a potential chemotype that exhibits a desired bioactivity. The work described herein utilizes a unique method of targeted isolation using structure-based virtual screening to identify potential antibacterial compounds active against MRSA within the marine sponge order Verongiida. This is coupled with molecular networking-guided, targeted isolation to provide a novel drug discovery procedure. A total of 12 previously reported bromotyrosine-derived alkaloids were isolated from the marine sponge species Pseudoceratina durissima, and the compound, (+)-aeroplysinin-1 (1) displayed activity against the MRSA pathogen (MIC: <32 µg/mL). The compounds (1–3, 6 and 9) were assessed for their central nervous system (CNS) interaction and behavioral toxicity to zebrafish (Danio rerio) larvae, whereby several of the compounds were shown to induce significant hyperactivity. Anthelmintic activity against the parasitic nematode Haemonchus contorutus was also evaluated (2–4, 6–8).