Combining Activity Profiling with Advanced Annotation to Accelerate the Discovery of Natural Products Targeting Oncogenic Signaling in Melanoma.

The discovery of bioactive natural products remains a time-consuming and challenging task. The ability to link high-confidence metabolite annotations in crude extracts with activity would be highly beneficial to the drug discovery process. To address this challenge, HPLC-based activity profiling and advanced UHPLC-HRMS/MS metabolite profiling for annotation were combined to leverage the information obtained from both approaches on a crude extract scaled down to the submilligram level. This strategy was applied to a subset of an extract library screening aiming to identify natural products inhibiting oncogenic signaling in melanoma. Advanced annotation and data organization enabled the identification of compounds that were likely responsible for the activity in the extracts. These compounds belonged to two different natural product scaffolds, namely, brevipolides from a Hyptis brevipes extract and methoxylated flavonoids identified in three different extracts of Hyptis and Artemisia spp. Targeted isolation of these prioritized compounds led to five brevipolides and seven methoxylated flavonoids. Brevipolide A (1) and 6-methoxytricin (9) were the most potent compounds from each chemical class and displayed AKT activity inhibition with an IC50 of 17.6 ± 1.6 and 4.9 ± 0.2 µM, respectively.

Metabolomics in Ecology and Bioactive Natural Products Discovery: Challenges and Prospects for a Comprehensive Study of the Specialised Metabolome.

Metabolomics is playing an increasingly prominent role in chemical ecology and in the discovery of bioactive natural products (NPs). The identification of metabolites is a common/central objective in both research fields. NPs have significant biological properties and play roles in multiple chemical-ecological interactions. Classically, in pharmacognosy, their chemical structure is determined after a complex process of isolating and interpreting spectroscopic data. With the advent of powerful analytical techniques such as liquid chromatography-mass spectrometry (LC-MS) the annotation process of the specialised metabolome of plants and microorganisms has improved considerably. In this article, we summarise the possibilities opened by these advances and illustrate how we harnessed them in our own research to automate annotations of NPs and target the isolation of key compounds. In addition, we are also discussing the analytical and computational challenges associated with these emerging approaches and their perspective.

Molecular and Functional Analysis of Sunitinib-Resistance Induction in Human Renal Cell Carcinoma Cells.

Resistance in clear cell renal cell carcinoma (ccRCC) against sunitinib is a multifaceted process encompassing numerous molecular aberrations. This induces clinical complications, reducing the treatment success. Understanding these aberrations helps us to select an adapted treatment strategy that surpasses resistance mechanisms, reverting the treatment insensitivity. In this regard, we investigated the dominant mechanisms of resistance to sunitinib and validated an optimized multidrug combination to overcome this resistance. Human ccRCC cells were exposed to single or chronic treatment with sunitinib to obtain three resistant clones. Upon manifestation of sunitinib resistance, morphometric changes in the cells were observed. At the molecular level, the production of cell membrane and extracellular matrix components, chemotaxis, and cell cycle progression were dysregulated. Molecules enforcing the cell cycle progression, i.e., cyclin A, B1, and E, were upregulated. Mass spectrometry analysis revealed the intra- and extracellular presence of N-desethyl sunitinib, the active metabolite. Lysosomal sequestration of sunitinib was confirmed. After treatment with a synergistic optimized drug combination, the cell metabolic activity in Caki-1-sunitinib-resistant cells and 3D heterotypic co-cultures was reduced by >80%, remaining inactive in non-cancerous cells. These results demonstrate geno- and phenotypic changes in response to sunitinib treatment upon resistance induction. Mimicking resistance in the laboratory served as a platform to study drug responses.

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.

microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data.

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health.

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection.

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

Comparative metabolomic study of fungal foliar endophytes and their long-lived host Astrocaryum sciophilum: a model for exploring the chemodiversity of host-microbe interactions.

Introduction: In contrast to the dynamics observed in plant/pathogen interactions, endophytic fungi have the capacity to establish enduring associations within their hosts, leading to the development of a mutually beneficial relationship that relies on specialized chemical interactions. Research indicates that the presence of endophytic fungi has the ability to significantly modify the chemical makeup of the host organism. Our hypothesis proposes the existence of a reciprocal exchange of chemical signals between plants and fungi, facilitated by specialized chemical processes that could potentially manifest within the tissues of the host. This research aimed to precisely quantify the portion of the cumulative fungal endophytic community's metabolome detectable within host leaves, and tentatively evaluate its relevance to the host-endophyte interplay. The understory palm Astrocaryum sciophilum (Miq.) Pulle was used as a interesting host plant because of its notable resilience and prolonged life cycle, in a tropical ecosystem. Method: Using advanced metabolome characterization, including UHPLC-HRMS/MS and molecular networking, the study explored enriched metabolomes of both host leaves and 15 endophytic fungi. The intention was to capture a metabolomic "snapshot" of both host and endophytic community, to achieve a thorough and detailed analysis. Results and discussion: This approach yielded an extended MS-based molecular network, integrating diverse metadata for identifying host- and endophyte-derived metabolites. The exploration of such data (>24000 features in positive ionization mode) enabled effective metabolome comparison, yielding insights into cultivable endophyte chemodiversity and occurrence of common metabolites between the holobiont and its fungal communities. Surprisingly, a minor subset of features overlapped between host leaf and fungal samples despite significant plant metabolome enrichment. This indicated that fungal metabolic signatures produced in vitro remain sparingly detectable in the leaf. Several classes of primary metabolites were possibly shared. Specific fungal metabolites and/or compounds of their chemical classes were only occasionally discernible in the leaf, highlighting endophytes partial contribution to the overall holobiont metabolome. To our knowledge, the metabolomic study of a plant host and its microbiome has rarely been performed in such a comprehensive manner. The general analytical strategy proposed in this paper seems well-adapted for any study in the field of microbial- or microbiome-related MS and can be applied to most host-microbe interactions.

A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data.

MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without a priori knowledge, will vastly enhance the understanding of microorganisms' role in ecology and human health.

Targeted isolation of photoactive pigments from mushrooms yielded a highly potent new photosensitizer: 7,7'-biphyscion.

Pigments of fungi are a fertile ground of inspiration: they spread across various chemical backbones, absorption ranges, and bioactivities. However, basidiomycetes with strikingly colored fruiting bodies have never been explored as agents for photodynamic therapy (PDT), even though known photoactive compound classes (e.g., anthraquinones or alkaloids) are used as chemotaxonomic markers. In this study, we tested the hypothesis that the dyes of skin-heads (dermocyboid Cortinarii) can produce singlet oxygen under irradiation and thus are natural photosensitizers. Three photosensitizers based on anthraquinone structures were isolated and photopharmaceutical tests were conducted. For one of the three, i.e., (-)-7,7'-biphyscion (1), a promising photoyield and photocytotoxicity of EC50 = 0.064 µM against cancer cells (A549) was found under blue light irradiation (λexc = 468 nm, 9.3 J/cm2). The results of molecular biological methods, e.g., a viability assay and a cell cycle analysis, demonstrated the harmlessness of 1 in the dark and highlighted the apoptosis-inducing PDT potential under blue light irradiation. These results demonstrate for the first time that pigments of dermocyboid Cortinarii possess a so far undescribed activity, i.e., photoactivity, with significant potential for the field of PDT. The dimeric anthraquinone (-)-7,7'-biphyscion (1) was identified as a promising natural photosensitizer.

The LOTUS initiative for open knowledge management in natural products research.

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.

Inventa: A computational tool to discover structural novelty in natural extracts libraries.

Collections of natural extracts hold potential for the discovery of novel natural products with original modes of action. The prioritization of extracts from collections remains challenging due to the lack of a workflow that combines multiple-source information to facilitate the data interpretation. Results from different analytical techniques and literature reports need to be organized, processed, and interpreted to enable optimal decision-making for extracts prioritization. Here, we introduce Inventa, a computational tool that highlights the structural novelty potential within extracts, considering untargeted mass spectrometry data, spectral annotation, and literature reports. Based on this information, Inventa calculates multiple scores that inform their structural potential. Thus, Inventa has the potential to accelerate new natural products discovery. Inventa was applied to a set of plants from the Celastraceae family as a proof of concept. The Pristimera indica (Willd.) A.C.Sm roots extract was highlighted as a promising source of potentially novel compounds. Its phytochemical investigation resulted in the isolation and de novo characterization of thirteen new dihydro-ß-agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro-ß-agarofuran base scaffold.

Open and reusable annotated mass spectrometry dataset of a chemodiverse collection of 1,600 plant extracts.

As privileged structures, natural products often display potent biological activities. However, the discovery of novel bioactive scaffolds is often hampered by the chemical complexity of the biological matrices they are found in. Large natural extract collections are thus extremely valuable for their chemical novelty potential but also complicated to exploit in the frame of drug-discovery projects. In the end, it is the pure chemical substances that are desired for structural determination purposes and bioactivity evaluation. Researchers interested in the exploration of large and chemodiverse extract collections should thus establish strategies aiming to efficiently tackle such chemical complexity and access these structures. Establishing carefully crafted digital layers documenting the spectral and chemical complexity as well as bioactivity results of natural extracts collections can help prioritize time-consuming but mandatory isolation efforts. In this note, we report the results of our initial exploration of a collection of 1,600 plant extracts in the frame of a drug-discovery effort. After describing the taxonomic coverage of this collection, we present the results of its liquid chromatography high-resolution mass spectrometric profiling and the exploitation of these profiles using computational solutions. The resulting annotated mass spectral dataset and associated chemical and taxonomic metadata are made available to the community, and data reuse cases are proposed. We are currently continuing our exploration of this plant extract collection for drug-discovery purposes (notably looking for novel antitrypanosomatids, anti-infective and prometabolic compounds) and ecometabolomics insights. We believe that such a dataset can be exploited and reused by researchers interested in computational natural products exploration.

Unifying the identification of biomedical entities with the Bioregistry.

The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 23 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry .

Feature-Based Molecular Networking-An Exciting Tool to Spot Species of the Genus Cortinarius with Hidden Photosensitizers.

Fungi have developed a wide array of defense strategies to overcome mechanical injuries and pathogen infections. Recently, photoactivity has been discovered by showing that pigments isolated from Cortinarius uliginosus produce singlet oxygen under irradiation. To test if this phenomenon is limited to dermocyboid Cortinarii, six colourful Cortinarius species belonging to different classical subgenera (i.e., Dermocybe, Leprocybe, Myxacium, Phlegmacium, and Telamonia) were investigated. Fungal extracts were explored by the combination of in vitro photobiological methods, UHPLC coupled to high-resolution tandem mass spectrometry (UHPLC-HRMS2), feature-based molecular networking (FBMN), and metabolite dereplication techniques. The fungi C. rubrophyllus (Dermocybe) and C. xanthophyllus (Phlegmacium) exhibited promising photobiological activity in a low concentration range (1-7 µg/mL). Using UHPLC-HRMS2-based metabolomic tools, the underlying photoactive principle was investigated. Several monomeric and dimeric anthraquinones were annotated as compounds responsible for the photoactivity. Furthermore, the results showed that light-induced activity is not restricted to a single subgenus, but rather is a trait of Cortinarius species of different phylogenetic lineages and is linked to the presence of fungal anthraquinones. This study highlights the genus Cortinarius as a promising source for novel photopharmaceuticals. Additionally, we showed that putative dereplication of natural photosensitizers can be done by FBMN.

Drug Repurposing to Identify a Synergistic High-Order Drug Combination to Treat Sunitinib-Resistant Renal Cell Carcinoma.

Repurposed drugs have been evaluated for the management of clear cell renal cell carcinoma (ccRCC), but only a few have influenced the overall survival of patients with advanced disease. To combine repurposed non-oncology with oncological drugs, we applied our validated phenotypic method, which consisted of a reduced experimental part and data modeling. A synergistic optimized multidrug combination (ODC) was identified to significantly reduce the energy levels in cancer remaining inactive in non-cancerous cells. The ODC consisted of Rapta-C, erlotinib, metformin and parthenolide and low doses. Molecular and functional analysis of ODC revealed a loss of adhesiveness and induction of apoptosis. Gene-expression network analysis displayed significant alterations in the cellular metabolism, confirmed by LC-MS based metabolomic analysis, highlighting significant changes in the lipid classes. We used heterotypic in vitro 3D co-cultures and ex vivo organoids to validate the activity of the ODC, maintaining an efficacy of over 70%. Our results show that repurposed drugs can be combined to target cancer cells selectively with prominent activity. The strong impact on cell adherence and metabolism indicates a favorable mechanism of action of the ODC to treat ccRCC.

Insights on the Structural and Metabolic Resistance of Potato (Solanum tuberosum) Cultivars to Tuber Black Dot (Colletotrichum coccodes).

Black dot is a blemish disease of potato tubers caused by the phytopathogenic fungus Colletotrichum coccodes. Qualitative resistance (monogenic) that leads to the hypersensitive response has not been reported against black dot, but commercial potato cultivars show different susceptibility levels to the disease, indicating that quantitative resistance (polygenic) mechanisms against this pathogen exist. Cytological studies are essential to decipher pathogen colonization of the plant tissue, and untargeted metabolomics has been shown effective in highlighting resistance-related metabolites in quantitative resistance. In this study, we used five commercial potato cultivars with different susceptibility levels to black dot, and studied the structural and biochemical aspects that correlate with resistance to black dot using cytological and untargeted metabolomics methods. The cytological approach using semithin sections of potato tuber periderm revealed that C. coccodes colonizes the tuber periderm, but does not penetrate in cortical cells. Furthermore, skin thickness did not correlate with disease susceptibility, indicating that other factors influence quantitative resistance to black dot. Furthermore, suberin amounts did not correlate with black dot severity, and suberin composition was similar between the five potato cultivars studied. On the other hand, the untargeted metabolomics approach allowed highlighting biomarkers of infection, as well as constitutive and induced resistance-related metabolites. Hydroxycinnamic acids, hydroxycinnamic acid amides and steroidal saponins were found to be biomarkers of resistance under control conditions, while hydroxycoumarins were found to be specifically induced in the resistant cultivars. Notably, some of these biomarkers showed antifungal activity in vitro against C. coccodes. Altogether, our results show that quantitative resistance of potatoes to black dot involves structural and biochemical mechanisms, including the production of specialized metabolites with antifungal properties.

Antiseizure potential of the ancient Greek medicinal plant Helleborus odorus subsp. cyclophyllus and identification of its main active principles.

ETHNOPHARMACOLOGICAL RELEVANCE: Ethnopharmacological data and ancient texts support the use of black hellebore (Helleborus odorus subsp. cyclophyllus, Ranunculaceae) for the management and treatment of epilepsy in ancient Greece. AIM OF THE STUDY: A pharmacological investigation of the root methanolic extract (RME) was conducted using the zebrafish epilepsy model to isolate and identify the compounds responsible for a potential antiseizure activity and to provide evidence of its historical use. In addition, a comprehensive metabolite profiling of this studied species was proposed. MATERIALS AND METHODS: The roots were extracted by solvents of increasing polarity and root decoction (RDE) was also prepared. The extracts were evaluated for antiseizure activity using a larval zebrafish epilepsy model with pentylenetetrazole (PTZ)-induced seizures. The RME exhibited the highest antiseizure activity and was therefore selected for bioactivity-guided fractionation. Isolated compounds were fully characterized by NMR and high-resolution tandem mass spectrometry (HRMS/MS). The UHPLC-HRMS/MS analyses of the RME and RDE were used for dereplication and metabolite profiling. RESULTS: The RME showed 80% inhibition of PTZ-induced locomotor activity (300 µg/ml). This extract was fractionated and resulted in the isolation of a new glucopyranosyl-deoxyribonolactone (1) and a new furostanol saponin derivative (2), as well as of 20-hydroxyecdysone (3), hellebrin (4), a spirostanol glycoside derivative (5) and deglucohellebrin (6). The antiseizure activity of RME was found to be mainly due to the new furostanol saponin (2) and hellebrin (4), which reduced 45% and 60% of PTZ-induced seizures (135 µM, respectively). Besides, the aglycone of hellebrin, hellebrigenin (S34), was also active (45% at 7 µM). To further characterize the chemical composition of both RME and RDE, 30 compounds (A7-33, A35-37) were annotated based on UHPLC-HRMS/MS metabolite profiling. This revealed the presence of additional bufadienolides, furostanols, and evidenced alkaloids. CONCLUSIONS: This study is the first to identify the molecular basis of the ethnopharmacological use of black hellebore for the treatment of epilepsy. This was achieved using a microscale zebrafish epilepsy model to rapidly quantify in vivo antiseizure activity. The UHPLC-HRMS/MS profiling revealed the chemical diversity of the extracts and the presence of numerous bufadienolides, furostanols and ecdysteroids, also present in the decoction.

Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation.

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.