A powerful two-dimensional chromatography method for the non-target analysis of depolymerised lignin

BACKGROUND

Lignin is an abundant natural polymer obtained as a by-product from the fractionation of lignocellulosic biomass. In the name of a circular economy, lignin should be valorised into valuable chemicals or biomaterials and utilised instead of petrochemicals. For the development of efficient valorisation processes, the structural characterisation of lignin can be highly beneficial. However, this is an arduous task, as the isolated (and sometimes processed) lignin mainly consists of various neutral monomers but also oligomers. In addition, the material contains isomers, which can be especially problematic to separate and identify.

RESULTS

We present a powerful off-line comprehensive two-dimensional (2D) chromatography method combining liquid chromatography (LC), supercritical fluid chromatography (SFC), and high-resolution mass spectrometry for the non-target analysis of depolymerised lignin. The implementation of a 1-aminoanthracene column in the second dimension enabled a class separation of potential lignin monomers, dimers, trimers, and tetramers with an additional separation based on the number of hydroxyl groups and steric effects. The pentafluorophenyl column in the first dimension additionally improved the separation based on hydrophobicity. The comparison of off-line 2D LC × SFC to 1D SFC showed that besides the overall improved performance, the first method is also superior for the separation of isomers. Advanced data analysis methods (MS-DIAL, SIRIUS, and Feature-Based Molecular Network) were integrated into the non-target workflow to rapidly visualise and study the detected compounds, which proved to be especially beneficial for the characterisation of the separated isomers.

SIGNIFICANCE

The method yielded the first 2D LC plot demonstrating a classification of lignin compounds, which can be applied to compare various lignin sources and processing methods. In addition, the technique demonstrated improved separation of compounds, including isomers, which was especially beneficial as 77 % of the detected compounds had at least one isomer in the same lignin sample.

Molecular Networking and Bioassay-Guided Preparation and Separation of Active Extract and Constituents from Vicia tenuifolia Roth

Molecular networking drove the selection of material from V. tenuifolia organs that targeted active flavonoid glycosides. To optimize the extraction process, the flowers of V. tenuifolia were used to produce an anti-inflammatory extract. The effects of variables-organic solvent ratio; extraction time; and temperature-were investigated by the response of anti-inflammatory activity. Bioactivities-guided experiments helped identify fractions with high total phenolic and flavonoid content as well as antioxidant potential. Furthermore, one new compound (1), 19 first isolated together, and two known compounds were obtained and identified from the active fraction of this plant. Among them, compounds (15 and 22) were first reported for nuclear magnetic resonance (NMR) data from this study. All the isolates were evaluated for their anti-inflammatory capacity throughout, modulating nitric oxide (NO), interleukin (IL)-1β, and IL-8 production. Active compounds were further investigated for their regulation and binding affinity to the inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) proteins by Western blot and in silico approaches, respectively. The findings of this study suggested that the developed extract method, active fraction, and pure components should be further investigated as promising candidates for treating inflammation and oxidation.

New Phenolic Lipids from the Leaves of Clausena harmandiana Inhibit SARS-CoV-2 Entry into Host Cells

Induced by the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the COVID-19 pandemic underlined the clear need for antivirals against coronaviruses. In an effort to identify new inhibitors of SARS-CoV-2, a screening of 824 extracts prepared from various parts of 400 plant species belonging to the Rutaceae and Annonaceae families was conducted using a cell-based HCoV-229E inhibition assay. Due to its significant activity, the ethyl acetate extract of the leaves of Clausena harmandiana was selected for further chemical and biological investigations. Mass spectrometry-guided fractionation afforded three undescribed phenolic lipids (1-3), whose structures were determined via spectroscopic analysis. The absolute configurations of 1 and 2 were determined by analyzing Mosher ester derivatives. The antiviral activity against SARS-CoV-2 was subsequently shown, with IC₅₀ values of 0.20 and 0.05 µM for 2 and 3, respectively. The mechanism of action was further assessed, showing that both 2 and 3 are inhibitors of coronavirus entry by acting directly on the viral particle. Phenolic lipids from Clausena harmandiana might be a source of new antiviral agents against human coronaviruses.

Naturally Occurring Organohalogen Compounds-A Comprehensive Review

The present volume is the third in a trilogy that documents naturally occurring organohalogen compounds, bringing the total number-from fewer than 25 in 1968-to approximately 8000 compounds to date. Nearly all of these natural products contain chlorine or bromine, with a few containing iodine and, fewer still, fluorine. Produced by ubiquitous marine (algae, sponges, corals, bryozoa, nudibranchs, fungi, bacteria) and terrestrial organisms (plants, fungi, bacteria, insects, higher animals) and universal abiotic processes (volcanos, forest fires, geothermal events), organohalogens pervade the global ecosystem. Newly identified extraterrestrial sources are also documented. In addition to chemical structures, biological activity, biohalogenation, biodegradation, natural function, and future outlook are presented.

Synthesis and Anti-Leishmanial Properties of Quinolones Derived from Zanthosimuline

Quinoline derivatives and especially quinolones are considered as privileged structures in medicinal chemistry and are often associated with various biological properties. We recently isolated a series of original monoterpenyl quinolones from the bark of Codiaeum peltatum. As this extract was found to have a significant inhibitory activity against a Leishmania species, we decided to study the anti-leishmanial potential of this type of compound. Leishmaniasis is a serious health problem affecting more than 12 million people in the world. Available drugs cause harmful side effects and resistance for some of them. With the aim of finding anti-leishmanial compounds, we developed a synthetic strategy to access natural quinolones and analogues derived from zanthosimuline. We showed the versatility of this natural compound toward cyclization conditions, leading to various polycyclic quinolone-derived structures. The natural and synthetic compounds were evaluated against amastigote forms of Leishmania infantum. The results obtained confirmed the interest of this family of natural compounds but also revealed promising activities for some intermediates deriving from zanthosimuline. Following the same synthetic strategy, we then prepared 14 new analogues. In this work, we identified two promising molecules with good activities against intramacrophage L. infantum amastigotes without any cytotoxicity. We also showed that slight changes in amide functional groups affect drastically their anti-parasitic activity.

Computational Metabolomics Tools Reveal Subarmigerides, Unprecedented Linear Peptides from the Marine Sponge Holobiont Callyspongia subarmigera

A detailed examination of a unique molecular family, restricted to the Callyspongia genus, in a molecular network obtained from an in-house Haplosclerida marine sponge collection (including Haliclona, Callyspongia, Xestospongia, and Petrosia species) led to the discovery of subarmigerides, a series of rare linear peptides from Callyspongia subarmigera, a genus mainly known for polyacetylenes and lipids. The structure of the sole isolated peptide, subarmigeride A (1) was elucidated through extensive 1D and 2D NMR spectroscopy, HRMS/MS, and Marfey's method to assign its absolute configuration. The putative structures of seven additional linear peptides were proposed by an analysis of their respective MS/MS spectra and a comparison of their fragmentation patterns with the heptapeptide 1. Surprisingly, several structurally related analogues of subarmigeride A (1) occurred in one distinct cluster from the molecular network of the cyanobacteria strains of the Guadeloupe mangroves, suggesting that the true producer of this peptide family might be the microbial sponge-associated community, i.e., the sponge-associated cyanobacteria.

Intramolecular Diels-Alder Cycloaddition of Furan-Derived β-Enamino Diketones: An Entry to Diastereoselective Synthesis of Polycyclic Pyrano[3,2-c]quinolin-5-one Derivatives

A series of 1,3-cyclodiketone- and tetrahydroepoxyisoindole-fused β-enamino dicarbonyl heterocycles were synthesized via a 1,4-diazabicyclo[2.2.2]octane-catalyzed, CH₃NO₂-mediated three-component reaction of 1,3-cyclodiketone, furfural, and allylamine in toluene. The target compounds were generated via the formation of β-enamino diketone as a key intermediate, followed by intramolecular Diels-Alder cycloaddition. The prepared molecules bearing a quinoline-2,4-dione moiety could be further brominated with N-bromosuccinimide and diastereoselectively reduced by NaBH₄ to afford pyrano[3,2-c]quinolin-5-one-derived heterocycles with six vicinal stereogenic centers.

Hyperconjugative Interactions of the Carbon-Halogen Bond that Influence the Geometry of Cyclic α-Haloacetals

The analysis of the structures of low-energy conformers of different α-haloacetals reveals changes in bond lengths and geometries that correspond to stabilizing orbital interactions that contribute to the ground-state structures of these systems. Several factors, including the electron-donating and electron-accepting abilities of the substituents on the ring, affect the degree of the electronic interactions in these carbohydrate-like systems. The presence of an α-halogen atom that can participate in hyperconjugation has been shown to contribute to the structural characteristics of the low-energy conformer. The experimental evidence is supported by natural bond order (NBO) analysis to identify the types of interactions and to assess their relative importance.

Combination of GC-MS Molecular Networking and Larvicidal Effect against Aedes aegypti for the Discovery of Bioactive Substances in Commercial Essential Oils

Dengue is a neglected disease, present mainly in tropical countries, with more than 5.2 million cases reported in 2019. Vector control remains the most effective protective measure against dengue and other arboviruses. Synthetic insecticides based on organophosphates, pyrethroids, carbamates, neonicotinoids and oxadiazines are unattractive due to their high degree of toxicity to humans, animals and the environment. Conversely, natural-product-based larvicides/insecticides, such as essential oils, present high efficiency, low environmental toxicity and can be easily scaled up for industrial processes. However, essential oils are highly complex and require modern analytical and computational approaches to streamline the identification of bioactive substances. This study combined the GC-MS spectral similarity network approach with larvicidal assays as a new strategy for the discovery of potential bioactive substances in complex biological samples, enabling the systematic and simultaneous annotation of substances in 20 essential oils through LC50 larvicidal assays. This strategy allowed rapid intuitive discovery of distribution patterns between families and metabolic classes in clusters, and the prediction of larvicidal properties of acyclic monoterpene derivatives, including citral, neral, citronellal and citronellol, and their acetate forms (LC50 < 50 µg/mL).

Advancements in capturing and mining mass spectrometry data are transforming natural products research

Covering: 2016 up to 2021Mass spectrometry (MS) is an essential technology in natural products research with MS fragmentation (MS/MS) approaches becoming a key tool. Recent advancements in MS yield dense metabolomics datasets which have been, conventionally, used by individual labs for individual projects; however, a shift is brewing. The movement towards open MS data (and other structural characterization data) and accessible data mining tools is emerging in natural products research. Over the past 5 years, this movement has rapidly expanded and evolved with no slowdown in sight; the capabilities of today vastly exceed those of 5 years ago. Herein, we address the analysis of individual datasets, a situation we are calling the '2021 status quo', and the emergent framework to systematically capture sample information (metadata) and perform repository-scale analyses. We evaluate public data deposition, discuss the challenges of working in the repository scale, highlight the challenges of metadata capture and provide illustrative examples of the power of utilizing repository data and the tools that enable it. We conclude that the advancements in MS data collection must be met with advancements in how we utilize data; therefore, we argue that open data and data mining is the next evolution in obtaining the maximum potential in natural products research.

Navigating through chemical space and evolutionary time across the Australian continent in plant genus Eremophila

Eremophila is the largest genus in the plant tribe Myoporeae (Scrophulariaceae) and exhibits incredible morphological diversity across the Australian continent. The Australian Aboriginal Peoples recognize many Eremophila species as important sources of traditional medicine, the most frequently used plant parts being the leaves. Recent phylogenetic studies have revealed complex evolutionary relationships between Eremophila and related genera in the tribe. Unique and structurally diverse metabolites, particularly diterpenoids, are also a feature of plants in this group. To assess the full dimension of the chemical space of the tribe Myoporeae, we investigated the metabolite diversity in a chemo-evolutionary framework applying a combination of molecular phylogenetic and state-of-the-art computational metabolomics tools to build a dataset involving leaf samples from a total of 291 specimens of Eremophila and allied genera. The chemo-evolutionary relationships are expounded into a systematic context by integration of information about leaf morphology (resin and hairiness), environmental factors (pollination and geographical distribution), and medicinal properties (traditional medicinal uses and antibacterial studies), augmenting our understanding of complex interactions in biological systems.

Insight into chemical basis of traditional Chinese medicine based on the state-of-the-art techniques of liquid chromatography-mass spectrometry

Traditional Chinese medicine (TCM) has been an indispensable source of drugs for curing various human diseases. However, the inherent chemical diversity and complexity of TCM restricted the safety and efficacy of its usage. Over the past few decades, the combination of liquid chromatography with mass spectrometry has contributed greatly to the TCM qualitative analysis. And novel approaches have been continuously introduced to improve the analytical performance, including both the data acquisition methods to generate a large and informative dataset, and the data post-processing tools to extract the structure-related MS information. Furthermore, the fast-developing computer techniques and big data analytics have markedly enriched the data processing tools, bringing benefits of high efficiency and accuracy. To provide an up-to-date review of the latest techniques on the TCM qualitative analysis, multiple data-independent acquisition methods and data-dependent acquisition methods (precursor ion list, dynamic exclusion, mass tag, precursor ion scan, neutral loss scan, and multiple reaction monitoring) and post-processing techniques (mass defect filtering, diagnostic ion filtering, neutral loss filtering, mass spectral trees similarity filter, molecular networking, statistical analysis, database matching, etc.) were summarized and categorized. Applications of each technique and integrated analytical strategies were highlighted, discussion and future perspectives were proposed as well.

Ethnobotany and the Role of Plant Natural Products in Antibiotic Drug Discovery

The crisis of antibiotic resistance necessitates creative and innovative approaches, from chemical identification and analysis to the assessment of bioactivity. Plant natural products (NPs) represent a promising source of antibacterial lead compounds that could help fill the drug discovery pipeline in response to the growing antibiotic resistance crisis. The major strength of plant NPs lies in their rich and unique chemodiversity, their worldwide distribution and ease of access, their various antibacterial modes of action, and the proven clinical effectiveness of plant extracts from which they are isolated. While many studies have tried to summarize NPs with antibacterial activities, a comprehensive review with rigorous selection criteria has never been performed. In this work, the literature from 2012 to 2019 was systematically reviewed to highlight plant-derived compounds with antibacterial activity by focusing on their growth inhibitory activity. A total of 459 compounds are included in this Review, of which 50.8% are phenolic derivatives, 26.6% are terpenoids, 5.7% are alkaloids, and 17% are classified as other metabolites. A selection of 183 compounds is further discussed regarding their antibacterial activity, biosynthesis, structure-activity relationship, mechanism of action, and potential as antibiotics. Emerging trends in the field of antibacterial drug discovery from plants are also discussed. This Review brings to the forefront key findings on the antibacterial potential of plant NPs for consideration in future antibiotic discovery and development efforts.

Isolation of Picrotoxanes from Austrobuxus carunculatus Using Taxonomy-Based Molecular Networking

A unique collection of 292 extracts from 107 New Caledonian Euphorbiaceae species sensu lato was profiled by LC-MS² and the metabolite content organized by molecular networking. Based on the assumption that taxon-specific molecules are more likely to be structurally novel, taxonomic data were mapped on spectral networks to detect genus-specific clusters. Using this approach, a group of compounds unique to the genus Austrobuxus was highlighted. The subsequent MS-guided purification of the fruit EtOAc extract of Austrobuxus carunculatus led to the isolation of 13 new monolactone and "norditerpene" picrotoxanes (2-14), along with the known tutin (1). The structures of the new compounds were elucidated by HRESIMS and NMR spectroscopic data analysis, and the absolute configurations of compounds 1, 3, 7, 11, 12, and 14 were determined by single-crystal X-ray diffraction analysis. The relative and absolute configurations of compounds 4 and 5 were ascertained by chemical transformation of compound 3. The absolute configurations of other members of the series have been proposed on the basis of biogenetic considerations and specific rotation values of similar sign and magnitude. Compounds 1-14 were evaluated for their antiproliferative activities against HCT116 colon, U87-MG glioblastoma, and A549 lung human cancer cell lines. Compounds bearing an acyl chain at C-2 (i.e., 2, 4, and 13) showed IC₅₀ values in the micromolar range for the three cell lines used.

Innovative omics-based approaches for prioritisation and targeted isolation of natural products - new strategies for drug discovery

Covering: 2013 to 2019 The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed.

Computational methods for NMR and MS for structure elucidation II: database resources and advanced methods

Technological advances have contributed to the evolution of the natural product chemistry and drug discovery programs. Recently, computational methods for nuclear magnetic resonance (NMR) and mass spectrometry (MS) have speeded up and facilitated the process of structural elucidation even in high complex biological samples. In this chapter, the current computational tools related to NMR and MS databases and spectral similarity networks, as well as their applications on dereplication and determination of biological biomarkers, are addressed.

Molecular Networking Reveals the Chemical Diversity of Selaginellin Derivatives, Natural Phosphodiesterase-4 Inhibitors from Selaginella tamariscina

Selaginellins are unique pigments found in the genus Selaginella, the largest genus of Lycopodiophyta. Recent studies reported that some selaginellin analogues have potent phosphodiesterase-4 (PDE4) inhibitory activity. In this study, the chemical diversity of natural selaginellin derivatives was revealed by an MS/MS molecular networking-based dereplication of the Selaginella tamariscina extract. It led to the prioritization of chromatographic peaks predicted as previously unknown selaginellin derivatives. Targeted isolation of these compounds afforded two unusual selaginellin analogues with a 1H,3H-dibenzo[de,h]isochromene skeleton, namely, selariscins A (1) and B (2), along with eight new diarylfluorene derivatives, selaginpulvilins M-T (3-10), and five known analogues, 11-15. The absolute configurations of 1, 2, and 8-10 were elucidated by spectroscopic data analyses including computational electronic circular dichroism data. Compounds 1 and 3-10 showed PDE4 inhibitory activity with IC₅₀ values in the range of 2.8-33.8 μM, and their binding modes are suggested by a molecular docking study.

Natural products targeting strategies involving molecular networking: different manners, one goal

Covering: up to 2019Landmark advances in bioinformatics tools have recently enhanced the field of natural products research, putting today's natural product chemists in the enviable position of being able to perform the efficient targeting/discovery of previously undescribed molecules by expediting the prioritization of the isolation workflow. Among these advances, MS/MS molecular networking has appeared as a promising approach to dereplicate complex natural product mixtures, leading to a real revolution in the "art of natural product isolation" by accelerating the pace of research of this field. This review illustrates through selected cornerstone studies the new thinking in natural product isolation by drawing a parallel between the different underlying philosophies behind the use of molecular networking in targeting natural products.

Computationally-assisted discovery and structure elucidation of natural products

Computer hardware development coupled with the development of quantum chemistry, new computational models and algorithms, and user-friendly interfaces have lowered the barriers to the use of computation in the discovery and structure elucidation of natural products. Consequently, the use of computational chemistry software as a tool to discover and determine the structure of natural products has become more common in recent years. In this review, we provide several examples of recent studies that used computer technology to facilitate the discovery and structure determination of various natural products.

Antiviral Compounds from Codiaeum peltatum Targeted by a Multi-informative Molecular Networks Approach

From a set of 292 Euphorbiaceae extracts, the use of a molecular networking (MN)-based prioritization approach highlighted three clusters (MN1-3) depicting ions from the bark extract of Codiaeum peltatum. Based on their putative antiviral potential and structural novelty, the MS-guided purification of compounds present in MN1 and MN2 afforded two new daphnane-type diterpenoid orthoesters (DDO), codiapeltines A (1) and B (2), the new actephilols B (3) and C (4), and four known 1,4-dioxane-fused phenanthrene dimers (5-8). The structures of the new compounds were elucidated by NMR spectroscopic data analysis, and the absolute configurations of compounds 1 and 2 were deduced by comparison of experimental and calculated ECD spectra. Codiapeltine B (2) is the first daphnane bearing a 9,11,13-orthoester moiety, establishing a new major structural class of DDO. Compounds 1-8 and four recently reported monoterpenyl quinolones (9-12) detected in MN3 were investigated for their selective activities against chikungunya virus replication and their antipolymerase activities against the NS5 proteins of dengue and zika viruses. Compounds 3-8 exhibited strong inhibitory activities on both dengue and zika NS5 in primary assays, but extensive biological analyses indicated that only actephilol B (3) displayed a specific interaction with the NS5 targets.

MetGem Software for the Generation of Molecular Networks Based on the t-SNE Algorithm

Molecular networking (MN) is becoming a standard bioinformatics tool in the metabolomic community. Its paradigm is based on the observation that compounds with a high degree of chemical similarity share comparable MS² fragmentation pathways. To afford a clear separation between MS² spectral clusters, only the most relevant similarity scores are selected using dedicated filtering steps requiring time-consuming parameter optimization. Depending on the filtering values selected, some scores are arbitrarily deleted and a part of the information is ignored. The problem of creating a reliable representation of MS² spectra data sets can be solved using algorithms developed for dimensionality reduction and pattern recognition purposes, such as t-distributed stochastic neighbor embedding (t-SNE). This multivariate embedding method pays particular attention to local details by using nonlinear outputs to represent the entire data space. To overcome the limitations inherent to the GNPS workflow and the networking architecture, we developed MetGem. Our software allows the parallel investigation of two complementary representations of the raw data set, one based on a classic GNPS-style MN and another based on the t-SNE algorithm. The t-SNE graph preserves the interactions between related groups of spectra, while the MN output allows an unambiguous separation of clusters. Additionally, almost all parameters can be tuned in real time, and new networks can be generated within a few seconds for small data sets. With the development of this unified interface ( https://metgem.github.io ), we fulfilled the need for a dedicated, user-friendly, local software for MS² comparison and spectral network generation.