Secure and Sustainable Sourcing of Plant Tissues for the Exhaustive Exploration of Their Chemodiversity.

The main challenge of plant chemical diversity exploration is how to develop tools to study exhaustively plant tissues. Their sustainable sourcing is a limitation as bioguided strategies and dereplication need quite large amounts of plant material. We examine if alternative solutions could overcome these difficulties by obtaining a secure, sustainable, and scalable source of tissues able to biosynthesize an array of metabolites. As this approach would be as independent of the botanical origin as possible, we chose eight plant species from different families. We applied a four steps culture establishment procedure, monitoring targeted compounds through mass spectrometry-based analytical methods. We also characterized the capacities of leaf explants in culture to produce diverse secondary metabolites. In vitro cultures were successfully established for six species with leaf explants still producing a diversity of compounds after the culture establishment procedure. Furthermore, explants from leaves of axenic plantlets were also analyzed. The detection of marker compounds was confirmed after six days in culture for all tested species. Our results show that the first stage of this approach aiming at easing exploration of plant chemodiversity was completed, and leaf tissues could offer an interesting alternative providing a constant source of natural compounds.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

(-)-5-Demethoxygrandisin B a New Lignan from Virola surinamensis (Rol.) Warb. Leaves: Evaluation of the Leishmanicidal Activity by In Vitro and In Silico Approaches.

Leishmaniasis is a complex disease caused by infection with different Leishmania parasites. The number of medications used for its treatment is still limited and the discovery of new drugs is a valuable approach. In this context, here we describe the in vitro leishmanicidal activity and the in silico interaction between trypanothione reductase (TryR) and (-)-5-demethoxygrandisin B from the leaves of Virola surinamensis (Rol.) Warb. The compound (-)-5-demethoxygrandisin B was isolated from V. surinamensis leaves, a plant found in the Brazilian Amazon, and it was characterized as (7R,8S,7'R,8'S)-3,4,5,3',4'-pentamethoxy-7,7'-epoxylignan. In vitro antileishmanial activity was examined against Leishmania amazonensis, covering both promastigote and intracellular amastigote phases. Cytotoxicity and nitrite production were gauged using BALB/c peritoneal macrophages. Moreover, transmission electron microscopy was applied to probe ultrastructural alterations, and flow cytometry assessed the shifts in the mitochondrial membrane potential. In silico methods such as molecular docking and molecular dynamics assessed the interaction between the most stable configuration of (-)-5-demethoxygrandisin B and TryR from L. infantum (PDB ID 2JK6). As a result, the (-)-5-demethoxygrandisin B was active against promastigote (IC50 7.0 µM) and intracellular amastigote (IC50 26.04 µM) forms of L. amazonensis, with acceptable selectivity indexes. (-)-5-demethoxygrandisin B caused ultrastructural changes in promastigotes, including mitochondrial swelling, altered kDNA patterns, vacuoles, vesicular structures, autophagosomes, and enlarged flagellar pockets. It reduced the mitochondria membrane potential and formed bonds with important residues in the TryR enzyme. The molecular dynamics simulations showed stability and favorable interaction with TryR. The compound targets L. amazonensis mitochondria via TryR enzyme inhibition.