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.

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.

As contemporary bioinformatic and chemoinformatic capabilities are reshaping natural products research, major benefits could result from an open database of referenced structure-organism pairs. Those pairs allow the identification of distinct molecular structures found as components of heterogeneous chemical matrices originating from living organisms. Current databases with such information suffer from paywall restrictions, limited taxonomic scope, poorly standardized fields, and lack of interoperability. To ensure data quality, references to the work that describes the structure-organism relationship are mandatory. To fill this void, we collected and curated a set of structure-organism pairs from publicly available natural products databases to yield LOTUS (naturaL prOducTs occUrrences databaSe), which contains over 500,000 curated and referenced structure-organism pairs. All the programs developed for data collection, curation, and dissemination are publicly available. To provide unlimited access as well as standardized linking to other resources, LOTUS data is both hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. The diffusion of these referenced structure-organism pairs within the Wikidata framework addresses many of the limitations of currently-available databases and facilitates linkage to existing biological and chemical data resources. This resource represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.Graphical abstractFigure 1:Graphical abstract

<p><a>Pigments of mushrooms are a fertile ground of inspiration: they spread across various chemical backbones, absorption ranges, and bioactivities. While looking from a photochemical perspective, we discovered a new bioactivity, i.e., photoactivity. We revealed that singlet oxygen production is a common theme in one group of webcaps (i.e., dermocyboid Cortinarii, formerly called Dermocybe). This photoactivity was explored by bioactivity-based molecular networking and photo-activity guided isolation. As a result, three photosensitizers based on anthraquinone structures were isolated. All three were photochemically characterized and (photo)cytotoxically tested. For one of the three, i.e. (-)-7,7’-biphyscion (<b>1</b>), a promising photoyield of </a>f_D= 20 % (l_exc = 455 nm) and an excellent photocytotoxicity of approx. 64 nM against A549 lung cancer cell lines (l_exc = 468 nm, 9.3 J/cm²) was found, while no effect was observed in the dark. Several molecular biological methods proved the harmlessness of <b>1</b> in the dark while showing that apoptosis is dose-dependent induced by <b>1</b> under irradiation. Therewith, <b>1</b> is a promising candidate for photodynamic therapy, while the photoactivity theme in the subgenus hints towards a yet unthought bioactivity in fungi: photoactivated defense.</p>

25 Mass spectrometry (MS) hyphenated to liquid chromatography (LC)-MS offers unrivalled sensitivity 26 for metabolite profiling of complex biological matrices encountered in natural products (NP) research. 27 With advanced platforms LC, MS/MS spectra are acquired in an untargeted manner on most detected 28 features. This generates massive and complex sets of spectral data that provide valuable structural 29 information on most analytes. To interpret such datasets, computational methods are mandatory. To 30 this extent, computerized annotation of metabolites links spectral data to candidate structures. When 31 profiling complex extracts spectra are often organized in clusters by similarity via Molecular 32 Networking (MN). A spectral matching score is usually established between the acquired data and 33 experimental or theoretical spectral databases (DB). The process leads to various candidate structures 34 for each MS features. At this stage, obtaining high annotation confidence level remains a challenge 35 notably due to the high chemodiversity of specialized metabolomes. 36

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.

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.

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.