Natural deep eutectic solvents (NADES) made mainly with abundant primary metabolites are being increasingly applied in green chemistry. The advantages of NADES as green solvents have led to their use in novel green products for the food, cosmetics and pharma markets. However, one of the main difficulties encountered in the development of novel products and their quality control arises from their low vapour pressure and high viscosity. These features create the need for the development of new analytical methods suited to this type of sample. In this study, such a method was developed and applied to analyse the efficiency of a diverse set of NADES for the extraction of compounds of interest from two model plants, Ginkgo biloba and Panax ginseng. The method uses high-performance thin-layer chromatography (HPTLC) coupled with multivariate data analysis (MVDA). It was successfully applied to the comparative quali- and quantitative analysis of very chemically diverse metabolites (e.g., phenolics, terpenoids, phenolic acids and saponins) that are present in the extracts obtained from the plants using six different NADES. The composition of each NADES was a combination of two or three compounds mixed in defined molar ratios; malic acid-choline chloride (1:1), malic acid-glucose (1:1), choline chloride-glucose (5:2), malic acid-proline (1:1), glucose-fructose-sucrose (1:1:1) and glycerol-proline-sucrose (9:4:1). Of these mixtures, malic acid-choline chloride (1:1) and glycerol-proline-sucrose (1:1:1) for G. biloba leaves, and malic acid-choline chloride (1:1) and malic acid-glucose (1:1) for P. ginseng leaves and stems showed the highest yields of the target compounds. Interestingly, none of the NADES extracted ginkgolic acids as much as the conventional organic solvents. As these compounds are considered to be toxic, the fact that these NADES produce virtually ginkgolic acid-free extracts is extremely useful. The effect of adding different volumes of water to the most efficient NADES was also evaluated and the results revealed that there is a great influence exerted by the water content, with maximum yields of ginkgolides, phenolics and ginsenosides being obtained with approximately 20% water (w/w).

Metabolomics has become an important tool in the search for bioactive compounds from natural sources, with the recent inclusion of marine organisms. Of the several steps performed in metabolomics studies, the extraction process is a crucial step—one which has been overlooked for a long time. In the presented study, a pressurized liquid extraction system was used to investigate the effect of extraction parameters such as pressure, temperature, number of cycles, and solvent polarity on the chemical diversity of the extract obtained from the marine sponge, Xestospongia. For this, a full factorial design (24) was performed using a chemical diversity index, which was found to be a suitable tool to determine the efficiency of the extraction process, as the response variable. This index was calculated using a logarithmic transformation of 1H NMR signals. Three factors (number of cycles, temperature, and solvent polarity) and two interactions were found to affect the chemical diversity of the obtained extracts significantly. Two individual factors (temperature and solvent polarity) were selected for further study on their influence on sponge metabolites using orthogonal partial least square (OPLS) modeling. Based on the results, the groups of compounds that were most influenced by these parameters were determined, and it was concluded that ethanol as the extraction solvent together with low temperatures were the conditions that provided a higher chemical diversity in the extract.

Background Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the “omics” technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms. Aim of review This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms. Key scientific concepts of review The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

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Despite their high therapeutic potential, only a limited number of approved drugs originate from marine natural products. A possible reason for this is their broad metabolic variability related to the environment, which can cause reproducibility issues. Consequently, a further understanding of environmental factors influencing the production of metabolites is required. Giant barrel sponges, Xestospongia spp., are a source of many new compounds and are found in a broad geographical range. In this study, the relationship between the metabolome and the geographical location of sponges within the genus Xestospongia spp. was investigated. One hundred and thirty-nine specimens of giant barrel sponges (Xestospongia spp.) collected in four locations, Martinique, Curaçao, Taiwan, and Tanzania, were studied using a multiplatform metabolomics methodology (nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry). A clear grouping of the collected samples according to their location was shown. Metabolomics analysis revealed that sterols and various fatty acids, including polyoxygenated and brominated derivatives, were related to the differences in locations. To explore the relationship between observed metabolic changes and their bioactivity, antibacterial activity was assessed against Escherichia coli and Staphylococcus aureus. The activity was found to correlate with brominated fatty acids. These were isolated and identified as (9E,17E)-18-bromooctadeca-9,17-dien-5,7,15-triynoic acid (1), xestospongic acid (2), (7E,13E,15Z)-14,16-dibromohexadeca-7,13,15-trien-5-ynoic acid (3), and two previously unreported compounds.

Small molecules can selectively modulate biological processes and thus generate phenotypic variation. Biological samples are complex matrices, and liquid chromatography tandem mass spectrometry often detects hundreds of molecules, of which only a fraction may be associated with this variation. The challenge therefore lies in the prioritization of the most relevant molecules for further investigation. Tools are needed to effectively contextualize mass spectrometric data with phenotypical and environmental (meta)data. To accelerate this task, we developed FERMO, a dashboard application combining mass spectrometry data with qualitative and quantitative biological observations. FERMO's centralized interface enables users to rapidly inspect data, formulate hypotheses, and prioritize molecules of interest. We demonstrate the applicability of FERMO in a case study on antibiotic activity of bacterial extracts, where we successfully prioritized the bioactive molecule siomycin out of 143 molecular features. We expect that besides natural product discovery, FERMO will find application in a wide range of omics-driven fields.

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