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

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

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

Development of a Prioritization Strategy to Efficiently Select Natural Extracts from Large Biodiverse Datasets with a High Structural Novelty Potential

Efficient prioritization of samples in natural extract (NEs) libraries has become a critical aspect in discovering new specialized metabolites in Natural Products (NP) research. However, prioritizing NEs from given collections is still challenging due to the absence of workflows that integrate multiple sources of information to facilitate comprehensive data interpretation. To achieve optimal decision-making, the results from metabolites profiling techniques and literature data must be organized, processed, and interpreted. This task becomes particularly complex when dealing with large NEs collections (consisting of hundreds to thousands of samples). It requires an intelligent selection of samples to create manageable subsets tailored to each study. This challenge constitutes the focus of this thesis project, namely the development of a prioritization strategy to efficiently select NEs from large datasets with a high structural novelty potential. Ideally, the approach should allow for faster and more rational decision-making for sample selection and speed up the discovery of novel NPs. To address this challenge, a bioinformatic tool called Inventa was developed to assist in the selection of extracts based on their potential for structural novelty. Inventa generates a combined score considering untargeted UHPLC-HRMS2 data, spectral annotations, literature reports, and chemically informed sample comparisons. The application of this tool on a set of 76 plant extracts from the Celastraceae family led to the discovery of 13 new β-agarofuran compounds, including 5 with a new base scaffold, thus providing proof of concept for Inventa's effectiveness. This workflow can be implemented in aligned and unaligned (do not require an RT-aligned feature table) UHPLC-HRMS2 data sets. The unaligned workflow allows the processing of large volume data sets (up to thousands of samples) and the addition of samples over time. This latest approach was applied to a diverse collection of 1 600 plant extracts. Several plant species were highlighted for their structural novelty potential. The isolation work done on the extracts of Entandrophragma candollei (Q5834167) and Entandrophragma utile (Q835089) resulted in the isolation of series of novel limonoids and ergostanes. With the idea of combining the results of bioactivity and structural novelty to obtain innovative bioactive NPs, the set of 1600 plant extracts was screened for a bioactivity in a Wnt triple-negative breast cancer bioassay. The bioactivity results helped to reduce the number of extracts of interest significantly. Inventa’s results were used as supplementary information to further narrow down the selection of extracts by considering only extracts spectrally dissimilar with a low annotation rate, low number of reported compounds, ensuring a high probability of discovering structurally novel compounds. The isolation efforts were focused on the leaf extract of Hymenocardia punctata (Q15514019) and a series of structurally novel bicyclo[3.3.1]non-3-ene-2,9-diones with high bioactive potential were isolated. The tool and integrative approaches developed in this thesis project could be employed to focus phytochemical investigations on a smaller number of extracts with distinct chemical spaces, resulting in the identification of novel compounds, and potentially significant bioactivities.</p

<i>Inventa</i>: 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- β -agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro- β -agarofuran base scaffold. </p

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

AbstractNMR and ECD data of 13 compound

Chiral separation of stilbene dimers generated by biotransformation for absolute configuration determination and antibacterial evaluation

AbstractExcel exported data of all the chiral-HPLC injection (separations and controls of the isolated enantiomers), NMR spectra of the 8 compounds, and Excel file with all the UV and ECD data of each compound and their respective enantiomers, as well as calculated ECD spectr