Integration of Biochemometrics and Molecular Networking to Identify Antimicrobials in Angelica keiskei

Development of Xanthoangelol-Derived Compounds with Membrane-Disrupting Effects against Gram-Positive Bacteria

Infections caused by multidrug-resistant pathogens have emerged as a serious threat to public health. To develop new antibacterial agents to combat such drug-resistant bacteria, a class of novel amphiphilic xanthoangelol-derived compounds were designed and synthesized by mimicking the structure and function of antimicrobial peptides (AMPs). Among them, compound 9h displayed excellent antimicrobial activity against the Gram-positive strains tested (MICs = 0.5-2 μg/mL), comparable to vancomycin, and with low hemolytic toxicity and good membrane selectivity. Additionally, compound 9h demonstrated rapid bactericidal effects, low resistance frequency, low cytotoxicity, and good plasma stability. Mechanistic studies further revealed that compound 9h had good membrane-targeting ability and was able to destroy the integrity of bacterial cell membranes, causing an increase in intracellular ROS and the leakage of DNA and proteins, thus accelerating bacterial death. These results make 9h a promising antimicrobial candidate to combat bacterial infection.

Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides

The Feature-based Molecular Networking (FBMN) is a well-known approach for mapping and identifying structures and analogues. However, in the absence of prior knowledge about the molecular class, assessing specific fragments and clusters requires time-consuming manual validation. This study demonstrates that combining FBMN and Mass Spec Query Language (MassQL) is an effective strategy for accelerating the decoding mass fragmentation pathways and identifying molecules with comparable fragmentation patterns, such as beauvericin and its analogues. To accomplish this objective, a spectral similarity network was built from ESI-MS/MS experiments of Fusarium oxysporum at various collision energies (CIDs) and paired with a MassQL search query for conserved beauvericin ions. FBMN analysis revealed that sodiated and protonated ions clustered differently, with sodiated adducts needing more collision energy and exhibiting a distinct fragmentation pattern. Based on this distinction, two sets of particular fragments were discovered for the identification of these hexadepsipeptides: ([M + H]+) m/z 134, 244, 262, and 362 and ([M + Na]+) m/z 266, 284 and 384. By using these fragments, MassQL accurately found other analogues of the same molecular class and annotated beauvericins that were not classified by FBMN alone. Furthermore, FBMN analysis of sodiated beauvericins at 70 eV revealed subclasses with distinct amino acid residues, allowing distinction between beauvericins (beauvericin and beauvericin D) and two previously unknown structural isomers with an unusual methionine sulfoxide residue. In summary, our integrated method revealed correlations between adduct types and fragmentation patterns, facilitated the detection of beauvericin clusters, including known and novel analogues, and allowed for the differentiation between structural isomers.

Angelica keiskei Impacts the Lifespan and Healthspan of Drosophila melanogaster in a Sex and Strain-Dependent Manner

Angelica keiskei is a perennial plant, belonging to the Apiaceae family and originating from Japan. This plant has been reported to act as a diuretic, analeptic, antidiabetic, hypertensive, tumor, galactagogue, and laxative. The mechanism of action of A. keiskei is not known, but previous studies have suggested that it may act as an antioxidant. In this work, we used Drosophila melanogaster to evaluate the impact of A. keiskei on lifespan and healthspan and its potential anti-aging mechanism by conducting multiple assays on three fly strains: w¹¹¹⁸, chico, and JIV. We observed that the extract extended lifespan and improved healthspan in a sex- and strain-dependent manner. A. keiskei extended lifespan and improved reproductive fitness in female flies and either had no effect or decreased survival and physical performance in males. The extract protected against the superoxide generator paraquat in both sexes. These sex-specific effects suggest that A. keiskei may act through age-specific pathways such as the insulin and insulin-like growth factor signaling (IIS) pathways. Upon examination, we found that the increased survival of A. keiskei-fed females was dependent on the presence of the insulin receptor substrate chico, supporting the role of IIS in the action of A. keiskei.

MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Interactions between Natural Products-A Review

Plant-based natural products have been used as a source for therapeutics since the dawn of civilization. According to the World Health Organization (WHO), more than 80% of the world's population relies on traditional medicine for their primary healthcare. Numerous natural extracts, widely known in Traditional Chinese Medicine, Indian Ayurveda medicine and other practices, have led to the modern discovery and development of new drugs. Plants continuously interact with their environment, producing new compounds and ever-changing combinations of existing ones. Interestingly, some of the compounds have shown lower therapeutic activity in comparison to the extract they were isolated from. These findings suggest that the higher therapeutic activity of the source extract was due to the synergistic effect of several compounds. In other words, the total therapeutic potential of the extract cannot be explained only by the sum of its parts alone. In traditional medicine, most herbal remedies are based on a mixture of plants, and it is the interaction between different constituents that amplifies their therapeutic potential. Considering the significant influence traditional medicine has on human healthcare, knowing and studying the synergistic effect of compounds is paramount in designing smart therapeutic agents.

Interactions between Medical Plant-Derived Bioactive Compounds: Focus on Antimicrobial Combination Effects

It is accepted that the medicinal use of complex mixtures of plant-derived bioactive compounds is more effective than purified bioactive compounds due to beneficial combination interactions. However, synergy and antagonism are very difficult to study in a meticulous fashion since most established methods were designed to reduce the complexity of mixtures and identify single bioactive compounds. This study represents a critical review of the current scientific literature on the combined effects of plant-derived extracts/bioactive compounds. A particular emphasis is provided on the identification of antimicrobial synergistic or antagonistic combinations using recent metabolomics methods and elucidation of approaches identifying potential mechanisms that underlie their interactions. Proven examples of synergistic/antagonistic antimicrobial activity of bioactive compounds are also discussed. The focus is also put on the current challenges, difficulties, and problems that need to be overcome and future perspectives surrounding combination effects. The utilization of bioactive compounds from medicinal plant extracts as appropriate antimicrobials is important and needs to be facilitated by means of new metabolomics technologies to discover the most effective combinations among them. Understanding the nature of the interactions between medicinal plant-derived bioactive compounds will result in the development of new combination antimicrobial therapies.

Antibacterial Screening, Biochemometric and Bioautographic Evaluation of the Non-Volatile Bioactive Components of Three Indigenous South African Salvia Species

Salvia africana-lutea L., S. lanceolata L., and S. chamelaeagnea L. are used in South Africa as traditional medicines to treat infections. This paper describes an in-depth investigation into their antibacterial activities to identify bioactive compounds. Methanol extracts from 81 samples were screened against seven bacterial pathogens, using the microdilution assay. Biochemometric models were constructed using data derived from minimum inhibitory concentration (MIC) and ultra-performance liquid chromatography-mass spectrometry data. Active molecules in selected extracts were tentatively identified using high-performance thin layer chromatography (HPTLC), combined with bioautography, and finally, by analysis of active zone eluates by mass spectrometry (MS) via a dedicated interface. Salvia chamelaeagnea displayed notable activity towards all seven pathogens, and the activity, reflected by MICs, was superior to that of the other two species, as confirmed through ANOVA. Biochemometric models highlighted potentially bioactive compounds, including rosmanol methyl ether, epiisorosmanol methyl ether and carnosic acid. Bioautography assays revealed inhibition zones against A. baumannii, an increasingly multidrug-resistant pathogen. Mass spectral data of the eluted zones correlated to those revealed through biochemometric analysis. The study demonstrates the application of a biochemometric approach, bioautography, and direct MS analysis as useful tools for the rapid identification of bioactive constituents in plant extracts.

Phytochemicals with activity against methicillin-resistant Staphylococcus aureus

BACKGROUND

The evolution of resistance to antimicrobials is a ubiquitous phenomenon. The evolution of antibiotic resistance in Staphylococcus aureus suggests that there is no remedy with sustaining effectiveness against this pathogen. The limited number of antibacterial drug classes and the common occurrence of cross-resistant bacteria reinforce the urgent need to discover new compounds targeting novel cellular functions. Natural products are a potential source of novel antibacterial agents. Anti-MRSA (methicillin-resistant S. aureus) bioactive compounds from Streptomyces and the anti-MRSA activity of a series of plant extracts have been reviewed respectively. However, there has been no detailed review of the precise bioactive components from plants.

PURPOSE

The present review aimed to summarize the phytochemicals that have been reported with anti-MRSA activities, analyze their structure-activity relationship and novel anti-MRSA mechanisms.

METHODS

Data contained in this review article are compiled from the authoritative databases PubMed, Web of Science, Google Scholar, and so on.

RESULTS

This review summarizes 100 phytochemicals (27 flavonoids, 23 alkaloids, 17 terpenes and 33 others) that have been tested for their anti-MRSA activity. Among these phytochemicals, 39 compounds showed remarkable anti-MRSA activity with MIC values less than 10 μg/ml, 14 compounds with MIC ranges including values < 10 μg/ml, 5 compounds with MIC values less than 5 μM; 11 phytochemicals show synergism anti-MRSA effects in combination with antibiotics. Phytochemicals exerted anti-MRSA activities mainly by destroying the membrane structure and inhibiting the efflux pump.

CONCLUSIONS

The 58 compounds with excellent anti-MRSA activity the 11 compounds with synergistic anti-MRSA effect, especially cannabinoids, xanthones and fatty acids should be further studied in vitro. Novel targets, such as cell membrane and efflux pump could be promising alternatives to develop antibacterial drugs in the future in order to prevent drug resistance.

Chalcones from Angelica keiskei (ashitaba) inhibit key Zika virus replication proteins

Zika virus (ZIKV) is a dangerous human pathogen and no antiviral drugs have been approved to date. The chalcones are a group of small molecules that are found in a number of different plants, including Angelica keiskei Koidzumi, also known as ashitaba. To examine chalcone anti-ZIKV activity, three chalcones, 4-hydroxyderricin (4HD), xanthoangelol (XA), and xanthoangelol-E (XA-E), were purified from a methanol-ethyl acetate extract from A. keiskei. Molecular and ensemble docking predicted that these chalcones would establish multiple interactions with residues in the catalytic and allosteric sites of ZIKV NS2B-NS3 protease, and in the allosteric site of the NS5 RNA-dependent RNA-polymerase (RdRp). Machine learning models also predicted 4HD, XA and XA-E as potential anti-ZIKV inhibitors. Enzymatic and kinetic assays confirmed chalcone inhibition of the ZIKV NS2B-NS3 protease allosteric site with IC₅₀s from 18 to 50 µM. Activity assays also revealed that XA, but not 4HD or XA-E, inhibited the allosteric site of the RdRp, with an IC₅₀ of 6.9 µM. Finally, we tested these chalcones for their anti-viral activity in vitro with Vero cells. 4HD and XA-E displayed anti-ZIKV activity with EC₅₀ values of 6.6 and 22.0 µM, respectively, while XA displayed relatively weak anti-ZIKV activity with whole cells. With their simple structures and relative ease of modification, the chalcones represent attractive candidates for hit-to-lead optimization in the search of new anti-ZIKV therapeutics.

Metabolomics and genomics in natural products research: complementary tools for targeting new chemical entities

Covering: 2010 to 2021Organisms in nature have evolved into proficient synthetic chemists, utilizing specialized enzymatic machinery to biosynthesize an inspiring diversity of secondary metabolites. Often serving to boost competitive advantage for their producers, these secondary metabolites have widespread human impacts as antibiotics, anti-inflammatories, and antifungal drugs. The natural products discovery field has begun a shift away from traditional activity-guided approaches and is beginning to take advantage of increasingly available metabolomics and genomics datasets to explore undiscovered chemical space. Major strides have been made and now enable -omics-informed prioritization of chemical structures for discovery, including the prospect of confidently linking metabolites to their biosynthetic pathways. Over the last decade, more integrated strategies now provide researchers with pipelines for simultaneous identification of expressed secondary metabolites and their biosynthetic machinery. However, continuous collaboration by the natural products community will be required to optimize strategies for effective evaluation of natural product biosynthetic gene clusters to accelerate discovery efforts. Here, we provide an evaluative guide to scientific literature as it relates to studying natural product biosynthesis using genomics, metabolomics, and their integrated datasets. Particular emphasis is placed on the unique insights that can be gained from large-scale integrated strategies, and we provide source organism-specific considerations to evaluate the gaps in our current knowledge.

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.

Scalarane-Type Sesterterpenoids from the Marine Sponge Lendenfeldia sp. Alleviate Inflammation in Human Neutrophils

Sponge-derived scalaranes are remarkable sesterterpenoids previously found to exhibit profound inhibitory effects against neutrophilic inflammation. In our current work, we constructed the metabolomic profile of marine sponge Lendenfeldia sp. for the first time using a tandem mass spectrometry (MS/MS) molecular networking approach. The results highlighted the rich chemical diversity of these scalaranes, motivating us to conduct further research to discover novel scalaranes targeting neutrophilic inflammation. MS- and NMR-assisted isolation and elucidation led to the discovery of seven new homoscalaranes, lendenfeldaranes K–Q (1–7), characterized by methylation at C-24, together with five known derivatives, lendenfeldarane B (8), 25-nor-24-methyl-12,24-dioxoscalar-16-en-22-oic acid (9), 24-methyl-12,24,25-trioxoscalar-16-en-22-oic acid (10), felixin B (11), and 23-hydroxy-20-methyldeoxoscalarin (12). Scalaranes 1–4 and 6–12 were assayed against superoxide anion generation and elastase release, which represented the neutrophilic inflammatory responses of respiratory burst and degranulation, respectively. The results indicated that 1–3 and 6–12 exhibited potential anti-inflammatory activities (IC₅₀ for superoxide anion scavenging: 0.87~6.57 μM; IC₅₀ for elastase release: 1.12~6.97 μM).

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.

Modeling Pharmacokinetic Natural Product-Drug Interactions for Decision-Making: A NaPDI Center Recommended Approach

The popularity of botanical and other purported medicinal natural products (NPs) continues to grow, especially among patients with chronic illnesses and patients managed on complex prescription drug regimens. With few exceptions, the risk of a given NP to precipitate a clinically significant pharmacokinetic NP-drug interaction (NPDI) remains understudied or unknown. Application of static or dynamic mathematical models to predict and/or simulate NPDIs can provide critical information about the potential clinical significance of these complex interactions. However, methods used to conduct such predictions or simulations are highly variable. Additionally, published reports using mathematical models to interrogate NPDIs are not always sufficiently detailed to ensure reproducibility. Consequently, guidelines are needed to inform the conduct and reporting of these modeling efforts. This recommended approach from the Center of Excellence for Natural Product Drug Interaction Research describes a systematic method for using mathematical models to interpret the interaction risk of NPs as precipitants of potential clinically significant pharmacokinetic NPDIs. A framework for developing and applying pharmacokinetic NPDI models is presented with the aim of promoting accuracy, reproducibility, and generalizability in the literature. SIGNIFICANCE STATEMENT: Many natural products (NPs) contain phytoconstituents that can increase or decrease systemic or tissue exposure to, and potentially the efficacy of, a pharmaceutical drug; however, no regulatory agency guidelines exist to assist in predicting the risk of these complex interactions. This recommended approach from a multi-institutional consortium designated by National Institutes of Health as the Center of Excellence for Natural Product Drug Interaction Research provides a framework for modeling pharmacokinetic NP-drug interactions.

A metabolomic approach to target antimalarial metabolites in the Artemisia annua fungal endophytes

Fungal endophytes are a major source of anti-infective agents and other medically relevant compounds. However, their classical blinded-chemical investigation is a challenging process due to their highly complex chemical makeup. Thus, utilizing cheminformatics tools such as metabolomics and computer-aided modelling is of great help deal with such complexity and select the most probable bioactive candidates. In the present study, we have explored the fungal endophytes associated with the well-known antimalarial medicinal plant Artemisia annua for their production of further antimalarial agents. Based on the preliminary antimalarial screening of these endophytes and using LC-HRMS-based metabolomics and multivariate analyses, we suggested different potentially active metabolites (compounds 1–8). Further in silico investigation using the neural-network-based prediction software PASS led to the selection of a group of quinone derivatives (compounds 1–5) as the most possible active hits. Subsequent in vitro validation revealed emodin (1) and physcion (2) to be potent antimalarial candidates with IC₅₀ values of 0.9 and 1.9 µM, respectively. Our approach in the present investigation therefore can be applied as a preliminary evaluation step in the natural products drug discovery, which in turn can facilitate the isolation of selected metabolites notably the biologically active ones.

Targeted and untargeted analysis of secondary metabolites to monitor growth and quorum sensing inhibition for methicillin-resistant Staphylococcus aureus (MRSA)

Drug resistant infections are an increasing problem world-wide, responsible for an estimated 700,000 annual mortalities. The use of antibiotics to treat such infections has resulted in the development of resistant bacterial pathogens such as methicillin-resistant Staphylococcus aureus (MRSA). One potential alternative strategy for treating drug resistant bacterial infections is to inhibit the production of toxins, thereby making the bacteria less harmful to the host, a so called "anti-virulence" approach. In MRSA, the agr quorum sensing system is one of the major regulators of toxin production, and quorum sensing inhibitors that target this system are a promising anti-virulence strategy. With this study, we developed a method that enables the activity of quorum sensing inhibitors to be measured using ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS). This method is an improvement over existing methods because it can be employed to distinguish antimicrobial activity from quorum sensing inhibition activity based on the UPLC-MS data. This is possible by simultaneously tracking production of metabolites regulated by the agr quorum sensing system (AIP-I and formylated δ-toxin) and a metabolite that appears not to be agr regulated under the conditions of this study (aureusimine B). The newly developed method provides more nuanced indication of how metabolite production changes over time and in response to quorum sensing or growth inhibition than is possible with commonly employed spectroscopic methods.

Exploring novel herbicidin analogues by transcriptional regulator overexpression and MS/MS molecular networking

Background

Herbicidin F has an undecose tricyclic furano-pyrano-pyran structure with post-decorations. It was detected from Streptomyces mobaraensis US-43 fermentation broth as a trace component by HPLC–MS analysis. As herbicidins exhibit herbicidal, antibacterial, antifungal and antiparasitic activities, we are attracted to explore more analogues for further development.

Results

The genome of S. mobaraensis US-43 was sequenced and a herbicidin biosynthetic gene cluster (hcd) was localized. The cluster contains structural genes, one transporter and three potential transcription regulatory genes. Overexpression of the three regulators respectively showed that only hcdR2 overexpression significantly improved the production of herbicidin F, and obviously increased the transcripts of 7 structural genes as well as the transporter gene. After performing homology searches using BLASTP in the GenBank database, 14 hcd-like clusters were found with a cluster-situated hcdR2 homologue. These HcdR2 orthologues showed overall structural similarity, especially in the C-terminal DNA binding domain. Based on bioinformatics analysis, a 21-bp consensus binding motif of HcdR2 was detected within 30 promoter regions in these genome-mined clusters. EMSA results verified that HcdR2 bound to the predicted consensus sequence. Additionally, we employed molecular networking to explore novel herbicidin analogues in hcdR2 overexpression strain. As a result, ten herbicidin analogues including six new compounds were identified based on MS/MS fragments. Herbicidin O was further purified and confirmed by ¹H NMR spectrum.

Conclusions

A herbicidin biosynthetic gene cluster (hcd) was identified in S. mobaraensis US-43. HcdR2, a member of LuxR family, was identified as the pathway-specific positive regulator, and the production of herbicidin F was dramatically increased by overexpression of hcdR2. Combined with molecular networking, ten herbicidin congeners including six novel herbicidin analogues were picked out from the secondary metabolites of hcdR2 overexpression strain. The orthologues of herbicidin F pathway-specific regulator HcdR2 were present in most of the genome-mined homologous biosynthetic gene clusters, which possessed at least one consensus binding motif with LuxR family characteristic. These results indicated that the combination of overexpression of hcdR2 orthologous regulator and molecular networking might be an effective way to exploit the “cryptic” herbicidin-related biosynthetic gene clusters for discovery of novel herbicidin analogues.

Chemometrics: a complementary tool to guide the isolation of pharmacologically active natural products

Chemometrics offers an important complementary tool to enhance the searching and isolation of bioactive natural products from natural sources.

Simplify: A Mass Spectrometry Metabolomics Approach to Identify Additives and Synergists from Complex Mixtures

In fields ranging from environmental toxicology to drug discovery, it is critical to identify how multiple chemical compounds interact to perturb biological systems. Isolation-based approaches fail to incorporate multiconstituent interactions, such as synergy. We have developed an approach called "Simplify", which identifies mixture constituents that interact to achieve biological effects. Simplify combines biological and mass spectrometric data sets and uses an "activity index" to predict mixture interactions. Using the plant Salvia miltiorrhiza as a case study, we employed Simplify to identify four individual constituents that contribute to antimicrobial activity, three additives and one synergist. Our study is the first to enable identification of unknown synergists prior to isolating them, demonstrating the ability of the Simplify workflow to predict key contributors to the biological effect of a complex mixture. While utilized for natural products discovery in this study, this approach is expected to prove useful across multiple disciplines that rely on mixture analysis.

Selection and characterization of botanical natural products for research studies: a NaPDI center recommended approach

Covering: up to the end of 2018 Dietary supplements, which include botanical (plant-based) natural products, constitute a multi-billion-dollar industry in the US. Regulation and quality control for this industry is an ongoing challenge. While there is general agreement that rigorous scientific studies are needed to evaluate the safety and efficacy of botanical natural products used by consumers, researchers conducting such studies face a unique set of challenges. Botanical natural products are inherently complex mixtures, with composition that differs depending on myriad factors including variability in genetics, cultivation conditions, and processing methods. Unfortunately, many studies of botanical natural products are carried out with poorly characterized study material, such that the results are irreproducible and difficult to interpret. This review provides recommended approaches for addressing the critical questions that researchers must address prior to in vitro or in vivo (including clinical) evaluation of botanical natural products. We describe selection and authentication of botanical material and identification of key biologically active compounds, and compare state-of-the-art methodologies such as untargeted metabolomics with more traditional targeted methods of characterization. The topics are chosen to be of maximal relevance to researchers, and are reviewed critically with commentary as to which approaches are most practical and useful and what common pitfalls should be avoided.

Synergy and antagonism in natural product extracts: when 1 + 1 does not equal 2

Covering: 2000 to 2019 According to a 2012 survey from the Centers for Disease Control and Prevention, approximately 18% of the U.S. population uses natural products (including plant-based or botanical preparations) for treatment or prevention of disease. The use of plant-based medicines is even more prevalent in developing countries, where for many they constitute the primary health care modality. Proponents of the medicinal use of natural product mixtures often claim that they are more effective than purified compounds due to beneficial "synergistic" interactions. A less-discussed phenomenon, antagonism, in which effects of active constituents are masked by other compounds in a complex mixture, also occurs in natural product mixtures. Synergy and antagonism are notoriously difficult to study in a rigorous fashion, particularly given that natural products chemistry research methodology is typically devoted to reducing complexity and identifying single active constituents for drug development. This report represents a critical review with commentary about the current state of the scientific literature as it relates to studying combination effects (including both synergy and antagonism) in natural product extracts. We provide particular emphasis on analytical and Big Data approaches for identifying synergistic or antagonistic combinations and elucidating the mechanisms that underlie their interactions. Specific case studies of botanicals in which synergistic interactions have been documented are also discussed. The topic of synergy is important given that consumer use of botanical natural products and associated safety concerns continue to garner attention by the public and the media. Guidance by the natural products community is needed to provide strategies for effective evaluation of safety and toxicity of botanical mixtures and to drive discovery in botanical natural product research.

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.

Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures

Compounds derived from natural sources represent the majority of small-molecule drugs utilized today. Plants, owing to their complex biosynthetic pathways, are poised to synthesize diverse secondary metabolites that selectively target biological macromolecules. Despite the vast chemical landscape of botanicals, drug discovery programs from these sources have diminished due to the costly and time-consuming nature of standard practices and high rates of compound rediscovery. Untargeted metabolomics approaches that integrate biological and chemical data sets potentially enable the prediction of active constituents early in the fractionation process. However, data acquisition and data processing parameters may have major impacts on the success of models produced. Using an inactive botanical mixture spiked with known antimicrobial compounds, untargeted mass spectrometry-based metabolomics data were combined with bioactivity data to produce selectivity ratio models subjected to a variety of data acquisition and data processing parameters. Selectivity ratio models were used to identify active constituents that were intentionally added to the mixture, along with an additional antimicrobial compound, randainal (5), which was masked by the presence of antagonists in the mixture. These studies found that data-processing approaches, particularly data transformation and model simplification tools using a variance cutoff, had significant impacts on the models produced, either masking or enhancing the ability to detect active constituents in samples. The current study highlights the importance of the data processing step for obtaining reliable information from metabolomics models and demonstrates the strengths and limitations of selectivity ratio analysis to comprehensively assess complex botanical mixtures.

Endoplasmic reticulum stress triggers Xanthoangelol-induced protective autophagy via activation of JNK/c-Jun Axis in hepatocellular carcinoma

Background

Xanthoangelol (XAG) was reported to exhibit antitumor properties in several cancer. However, the specific anti-tumor activity of XAG in human hepatocellular carcinoma (HCC) and the relevant mechanisms are not known.

Methods

The effects of XAG on HCC cell proliferation and apoptosis were respectively examined by CCK-8 assay and Annexin V-FITC/PI apoptosis kit. Western blotting was conducted to detect the expression of proteins. The effect of XAG on the development of acidic vesicle organelles was assessed using acridine orange staining. mRFP-GFP-LC3 adenovirus was used to transfect HCC cells and the formation of autolysosome was detected using a confocal microscope.

Results

Mechanistically, XAG promotes HCC cell death through triggering intrinsic apoptosis pathway, not extrinsic apoptotic pathway. Furthermore, XAG treatment induced autophagy in Bel 7402 and SMMC 7721 cells, as evidenced by an increase in autophagy-associated proteins, including LC3B-II, Beclin-1, and Atg5. Interestingly, inhibition of autophagy with 3-MA, Bafilomycin A1 (Baf A1), or siRNA targeting Atg5 effectively enhanced the apoptotic cell ratio in XAG-treated cells, indicating that protective effect of autophagy induced by XAG in HCC. Moreover, autophagy induced by XAG was mediated by activating endoplasmic reticulum stress (ERS), along with administration of XAG, the expression levels of ERS-associated proteins, including CHOP, GRP78, ATF6, p-eIF2α, IRE1α, and cleaved caspase-12 were significantly increased in HCC cells. Meanwhile, suppressing ERS with chemical chaperones (TUDCA) or CHOP shRNA could effectively abrogate the autophagy-inducing effect of XAG, and increase the apoptotic cell death. Further mechanistic studies showed that ERS-induced autophagy in XAG-treated cells was mediated by activation of JNK/c-jun pathway. XAG treatment resulted in the increase of p-JNK and p-c-jun, while suppressing ERS with TUDCA or CHOP shRNA could effectively reverse it. Meanwhile, SP600125, a JNK inhibitor, effectively reversed XAG-induced protective autophagy and enhanced cell apoptosis in XAG-treated HCC cells. In vivo results demonstrated that XAG exerts potent antitumor properties with low toxicity.

Conclusions

Collectively, these results suggested that XAG could be served as a promising candidate for the treatment and prevention of HCC.

Electronic supplementary material

The online version of this article (10.1186/s13046-018-1012-z) contains supplementary material, which is available to authorized users.