Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

Looking for Actives in the Haystack: Merging HRMS2-Based Molecular Networking, Chemometrics, and 13C NMR-Based Dereplication Approaches

The identification of bioactive natural products (NPs) in complex mixtures has become an important subject of contemporary NP research. In an attempt to address this challenge, the present work proposes an integrated strategy that combines tandem mass spectrometry (MS2)-based molecular networking (MN), a partial least-squares (PLS) chemometric model, as well as 13C NMR-based dereplication using MixONat software. In addition, an advanced glycation end product (AGEs) assay was used for activity evaluation. The approach was implemented on a Garcinia parvifolia bark extract that comprised a high content of prenylated xanthones and had previously shown a notable inhibitory effect on AGE formation. As a main result, the proposed strategy permitted the identification of potentially active metabolites within complex mixtures and their annotation with a higher level of confidence by NMR data. Overall, this comprehensive approach provides a powerful and efficient solution for the targeting and annotating of active compounds in complex NP mixtures.

13C NMR-based dereplication using MixONat software to decipher potent anti-cholinesterase compounds in Mesua lepidota bark

A bio-assay guided fractionation strategy based on cholinesterase assay combined with 13C NMR-based dereplication was used to identify active metabolites from the bark of Mesua lepidota. Eight compounds were identified with the aid of the 13C NMR-based dereplication software, MixONat, i.e., sitosterol (1), stigmasterol (2), α-amyrin (3), friedelin (6), 3β-friedelinol (7), betulinic acid (9), lepidotol A (10) and lepidotol B (11). Further bio-assay guided isolation of active compounds afforded one xanthone, pyranojacareubin (12) and six coumarins; lepidotol A (10), lepidotol B (11), lepidotol E (13), lepidotin A (14), and lepidotin B (15), including a new Mammea coumarin, lepidotin C (16). All the metabolites showed strong to moderate butyrylcholinesterase (BChE) inhibition. Lepidotin B (15) exhibited the most potent inhibition towards BChE with a mix-mode inhibition profile and a Ki value of 1.03 µM. Molecular docking and molecular dynamics simulations have revealed that lepidotin B (15) forms stable interactions with key residues within five critical regions of BChE. These regions encompass residues Asp70 and Tyr332, the acyl hydrophobic pocket marked by Leu286, the catalytic triad represented by Ser198 and His438, the oxyanion hole (OH) constituted by Gly116 and Gly117, and the choline binding site featuring Trp82. To gauge the binding strength of lepidotin B (15) and to pinpoint pivotal residues at the binding interface, free energy calculations were conducted using the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) approach. This analysis not only predicted a favourable binding affinity for lepidotin B (15) but also facilitated the identification of significant residues crucial for the binding interaction.

Vasorelaxant Activity of Salvia hispanica L.: Involvement of the Nitric Oxide Pathway in Its Pharmacological Mechanism

Salvia hispanica L., commonly known as chía, and its seeds have been used since ancient times to prepare different beverages. Due to its nutritional content, it is considered a dietary ingredient and has been reported with many health benefits. Chia seed components are helpful in cardiovascular disease (CVD) by reducing blood pressure, platelet aggregation, cholesterol, and oxidation. Still, its vasodilator effects on the vascular system were not reported yet. The hexanic (HESh), dichloromethanic (DESh), and methanolic (MESh) extracts obtained from chía seeds were evaluated on an aortic ring ex-vivo experimental model. The vasorelaxant efficacy and mechanism of action were determined. Also, phytochemical data was obtained through 13C NMR-based dereplication. The MESh extract showed the highest efficacy (Emax = 87%), and its effect was partially endothelium-dependent. The mechanism of action was determined experimentally, and the vasorelaxant curves were modified in the presence of L-NAME, ODQ, and potassium channel blockers. MESh caused a relaxing effect on KCl 80 mM-induced contraction and was less potent than nifedipine. The CaCl2-induced contraction was significantly decreased compared with the control curve. Phytochemical analysis of MESh suggests the presence of mannitol, previously reported as a vasodilator on aortic rings. Our findings suggest NO-cGMP pathway participation as a vasodilator mechanism of action of S. hispanica seeds; this effect can be attributed, in part, to the mannitol presence. S. hispanica could be used in future research focused on antihypertensive therapies.

Chemodiversity of propolis samples collected in various areas of Benin and Congo: Chromatographic profiling and chemical characterization guided by 13 C NMR dereplication

INTRODUCTION

Propolis is a resinous natural substance collected by honeybees from buds and exudates of various trees and plants; it is widely accepted that the composition of propolis depends on the phytogeographic characteristics of the site of collection.

OBJECTIVES

The aim of this study was to determine the phytochemical composition of ethanolic extracts from eight propolis batches collected in different regions of Benin (north, center, and south) and Congo, Africa.

MATERIAL AND METHODS

Characterization of propolis samples was performed by using different hyphenated chromatographic methods combined with carbon-13 nuclear magnetic resonance (¹³ C NMR) dereplication with MixONat software. Their antioxidant or anti-advanced glycation end-product (anti-AGE) activity was then evaluated by using diphenylpicrylhydrazyl and bovine serum albumin assays, respectively.

RESULTS

Chromatographic analyses combined with ¹³ C NMR dereplication showed that two samples from the center of Benin exhibited, in addition to a huge amount of pentacyclic triterpenes, methoxylated stilbenoids or phenanthrenoids, responsible for the antioxidant activity of the extract for the first one. Among them, combretastatins might be cytotoxic. For the second one, the prenylated flavanones known in Macaranga-type propolis were responsible for its significant anti-AGE activity. The sample from Congo was composed of many triterpene derivatives belonging to Mangifera indica species.

CONCLUSION

Therefore, propolis from the center of Benin seems to be of particular interest, due to its antioxidant and anti-AGE properties. Nevertheless, as standardization of propolis is difficult in tropical zones due to its great chemodiversity, a systematic phytochemical analysis is required before promoting the use of propolis in food and health products in Africa.

Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.