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.

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.

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.

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.

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.