Metabolomics and Molecular Networking to Characterize the Chemical Space of Four Momordica Plant Species

Exploring associations between metabolites and gene transcripts of common bean (Phaseolus vulgaris L.) in response to rust (Uromyces appendiculatus) infection

Common bean (Phaseolus vulgaris L.) faces escalating challenges resulting from the increasing prevalence of fungal pathogens such as rust caused by Uromyces appendiculatus, threatening yields and quality of the crop. Understanding P. vulgaris' disease response mechanisms is pivotal for the crop's resilience and food security. Current scientific understanding of underlying molecular mechanisms of P. vulgaris to U. appendiculatus is limited, particularly with respect to specialised molecular data, including metabolite profiles and gene expression. There is a significant knowledge gap in explicating precise metabolomic and transcriptional changes that occur in P. vulgaris upon interaction with U. appendiculatus, which limits strategies aimed at enhancing pathogen resistance. In this study, biological stress response strategies of common bean to the rust pathogen were elucidated through a combined metabolomic and transcriptomic profiling approach. Our findings revealed that U. appendiculatus triggered diverse levels of 30 known metabolites, primarily flavonoids, lipids, nucleosides, and phenylpropanoids among others. Transcriptome sequencing detected over 3000 differentially expressed genes, including multiple transcription factor families such as heat shock proteins (HSPs), cytochrome P450 monooxygenases (CYP), terpene synthases and WRKY transcription factors (TFs) among others. Integrative metabolome and transcriptome analysis showed that rust infection enriched metabolomic pathways, biosynthesis of secondary metabolites, protein processing in the endoplasmic reticulum, and purine metabolism among others. The metabolome and transcriptome integration approach employed in this study provides insights on molecular mechanisms underlying U. appendiculatus response in P. vulgaris' key developmental stages.

Non-Targeted Metabolomics Reveal Apomorphine's Therapeutic Effects and Lysophospholipid Alterations in Steatohepatitis

Metabolic dysfunction-associated steatohepatitis (MASH), characterized by progressive inflammation and fibrosis, evolves from metabolic dysfunction-associated steatotic liver disease and significantly heightens the risk of cirrhosis and hepatocellular carcinoma. Understanding metabolic pathways that influence MASH progression is crucial for developing targeted therapies. Non-targeted metabolomics offer a comprehensive view of metabolic alterations, enabling identification of novel biomarkers and pathways without preconceived ideas. Conversely, targeted metabolomics deliver precise and reproducible measurements, focusing on predefined metabolites to accurately quantify established pathways. This study utilized hepatocyte-specific PTEN knockout mice as a model to explore metabolic shifts associated with MASH. By integrating non-targeted metabolomics and targeted metabolomics, we analyzed liver samples from three groups: normal, pathological (MASH-affected), and MASH-affected, but treated with apomorphine, an antioxidant and recently reported ferroptosis inhibitor with potential therapeutic effects. Metabolic profiling identified lysophospholipids (LPLs) as significantly altered metabolites, with elevated levels in the MASH model and a notable reduction after apomorphine treatment. This suggests that LPLs are central to the etiology of MASH and may serve as targets for therapeutic intervention. Our findings underscore the effectiveness of apomorphine in modulating disease-specific metabolic disruptions, offering insights into its potential as a treatment for human MASH.

Bioprospecting of soil-borne microorganisms and chemical dereplication of their anti-microbial constituents with the aid of UPLC-QTOF-MS and molecular networking approach

Due to the emergence of drug-resistant microorganisms, the search for broad-spectrum antimicrobial compounds has become extremely crucial. Natural sources like plants and soils have been explored for diverse metabolites with antimicrobial properties. This study aimed to identify microorganisms from agricultural soils exhibiting antimicrobial effects against known human pathogens, and to highlight the chemical space of the responsible compounds through the computational metabolomics-based bioprospecting approach. Herein, bacteria were extracted from soil samples and their antimicrobial potential was measured via the agar well diffusion method. Methanolic extracts from the active bacteria were analyzed using the liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) technique, and the subsequent data was further analyzed through molecular networking approach which aided in identification of potential anti-microbial compounds. Furthermore, 16S rRNA gene sequencing enabled identification of the active bacterial isolates, where isolate 1 and 2 were identified as strains of Bacillus pumilus, whilst isolate 3 was found to be Bacillus subtilis. Interestingly, isolate 3 (Bacillus subtilis) displayed wide-ranging antimicrobial activity against the tested human pathogens. Molecular networking revealed the presence of Diketopiperazine compounds such as cyclo (D-Pro-D-Leu), cyclo (L-Tyr-L-Pro), cyclo (L-Pro-D-Phe), and cyclo (L-Pro-L-Val), alongside Surfactin C, Surfactin B, Pumilacidin E, and Isarrin D in the Bacillus strains as the main anti-microbial compounds. The application of the molecular networking approach represents an innovation in the field of bio-guided bioprospection of microorganisms and has proved to be an effective and feasible towards unearthing potent antimicrobial compounds. Additionally, the (computational metabolomics-based) approach accelerates the discovery of bioactive compounds and isolation of strains which offer a promising avenue for discovering new clinical antimicrobials. Finally, soil microbial flora could serve an alternative source of anti-microbial compounds which can assist in the fight against emergence of multi-drug resistance bacterial pathogens.

Charting the Cannabis plant chemical space with computational metabolomics

INTRODUCTION

The chemical classification of Cannabis is typically confined to the cannabinoid content, whilst Cannabis encompasses diverse chemical classes that vary in abundance among all its varieties. Hence, neglecting other chemical classes within Cannabis strains results in a restricted and biased comprehension of elements that may contribute to chemical intricacy and the resultant medicinal qualities of the plant.

OBJECTIVES

Thus, herein, we report a computational metabolomics study to elucidate the Cannabis metabolic map beyond the cannabinoids.

METHODS

Mass spectrometry-based computational tools were used to mine and evaluate the methanolic leaf and flower extracts of two Cannabis cultivars: Amnesia haze (AMNH) and Royal dutch cheese (RDC).

RESULTS

The results revealed the presence of different chemical compound classes including cannabinoids, but extending it to flavonoids and phospholipids at varying distributions across the cultivar plant tissues, where the phenylpropnoid superclass was more abundant in the leaves than in the flowers. Therefore, the two cultivars were differentiated based on the overall chemical content of their plant tissues where AMNH was observed to be more dominant in the flavonoid content while RDC was more dominant in the lipid-like molecules. Additionally, in silico molecular docking studies in combination with biological assay studies indicated the potentially differing anti-cancer properties of the two cultivars resulting from the elucidated chemical profiles.

CONCLUSION

These findings highlight distinctive chemical profiles beyond cannabinoids in Cannabis strains. This novel mapping of the metabolomic landscape of Cannabis provides actionable insights into plant biochemistry and justifies selecting certain varieties for medicinal use.

Next generation sequencing-aided screening, isolation, molecular identification, and antimicrobial potential for bacterial endophytes from the medicinal plant, Elephantorrhiza elephantina

Elephantorrhiza elephantina, a wild plant in southern Africa, is utilized in traditional medicine for various ailments, leading to its endangerment and listing on the Red List of South African Plants. To date, there have been no reports on bacterial endophytes from this plant, their classes of secondary metabolites, and potential medicinal properties. This study presents (i) taxonomic characterization of bacterial endophytes in leaf and root tissues using 16S rRNA, (ii) bacterial isolation, morphological, and phylogenetic characterization, (iii) bacterial growth, metabolite extraction, and LC-MS-based metabolite fingerprinting, and (iv) antimicrobial testing of bacterial crude extracts. Next-generation sequencing yielded 693 and 2,459 DNA read counts for the rhizomes and leaves, respectively, detecting phyla including Proteobacteria, Bacteroidota, Gemmatimonadota, Actinobacteriota, Verrucomicrobiota, Dependentiae, Firmicutes, and Armatimonodata. At the genus level, Novosphingobium, Mesorhizobium, Methylobacterium, and Ralstonia were the most dominant in both leaves and rhizomes. From root tissues, four bacterial isolates were selected, and 16S rRNA-based phylogenetic characterization identified two closely related Pseudomonas sp. (strain BNWU4 and 5), Microbacterium oxydans BNWU2, and Stenotrophomonas maltophilia BNWU1. The ethyl acetate:chloroform (1:1 v/v) organic extract from each isolate exhibited antimicrobial activity against all selected bacterial pathogens. Strain BNWU5 displayed the highest activity, with minimum inhibitory concentrations ranging from 62.5 μg/mL to 250 μg/mL against diarrhoeagenic Escherichia coli, Escherichia coli O157:H7, Salmonella enterica, antibiotic-resistant Vibrio cholerae, Staphylococcus aureus, Bacillus cereus, and Enterococcus durans. LC-MS analysis of the crude extract revealed common antimicrobial metabolites produced by all isolates, including Phenoxomethylpenicilloyl (penicilloyl V), cis-11-Eicosenamide, 3-Hydroxy-3-phenacyloxindole, and 9-Octadecenamide.

Integration of cloud-based molecular networking and docking for enhanced umami peptide screening from Pixian douban

This study presents an innovative cloud-based approach, using Pixian Douban, a well-known Chinese fermented seasoning, as a case study, to improve the identification of umami peptides and explore their interactions with the T1R1/T1R3 receptor. A feature-based molecular networking method was utilized to rapidly identify a total of eighteen peptides, including seven previously unrecorded ones. Notably, the umami threshold of QIVK in an aqueous solution was determined to be 0.3215 mmol/L, surpassing the majority of peptides reported in the past three years. Molecular docking analysis further revealed the strong binding of QIVK to T1R3 receptor residues through hydrogen bonds, as well as interactions via salt bridges and electrostatic attractions. As a result, this research significantly contributes to the efficient screening of umami peptides and the elucidation of the molecular basis of umami sensory perception in complex food systems.

Bioprospecting for Rhizobacteria with the Ability to Enhance Drought Tolerance in Lessertia frutescens

Lessertia frutescens is a multipurpose medicinal plant indigenous to South Africa that is used for the management of cancer, stomach ulcers, wounds, etc. The use and demand for the raw materials from this plant have been increasing steadily over the years, putting strain on the dwindling wild populations. Although cultivation may provide relief to the strained supply, the persistent drought climate poses a threat to the plant's growth and productivity. This study explored three plant-growth-promoting rhizobacteria isolates, TUTLFNC33, TUTLFNC37 and TUTLFWC74, obtained from the root nodules of Lessertia frutescens as potential bioinoculants that can improve yield, biological activities and the production of secondary metabolites in the host plant. Isolate TUTLFNC37 was identified as the most promising isolate for inoculation of Lessertia frutescens under drought conditions as it induced drought tolerance through enhanced root proliferation, osmolyte proline accumulation and stomatal closure. Superior biomass yield, phenolics, triterpenes and antioxidant activity were evident in the extracts of Lessertia frutescens inoculated with TUTLFNC37 and under different levels of drought. Furthermore, the metabolomics of the plant extracts demonstrated the ability of the isolate to withstand drastic changes in the composition of unique metabolites, sutherlandiosides A-D and sutherlandins A-D. Molecular families which were never reported in the plant (peptides and glycerolipids) were detected and annotated in the molecular networks. Although drought had deleterious effects on Lessertia frutescens, isolate TUTLFNC37 alleviated the impact of the stress. Isolate TUTLFNC37 is therefore the most promising, environmentally friendly alternative to harmful chemicals such as nitrate-based fertilizers. The isolate should be studied to establish its field performance, cross infectivity with other medicinal plants and competition with inherent soil microbes.

Anti-cervical cancer mechanism of bioactive compounds from Alangium platanifolium based on the 'compound-target-disease' network

In this study, we analyzed the chemical compositions of Alangium platanifolium (Sieb. et Zucc.) Harms (AP) using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) non-targeted plant metabolomics integration MolNetEnhancer strategy. A total of 75 compounds, including flavonoids, alkaloids, terpenes, C21 steroids, among others, were identified by comparing accurate mass-to-charge ratios, MS2 cleavage fragments, retention times, and MolNetenhancer-integrated analytical data, and the cleavage rules of the characteristic compounds were analyzed. A total of 125 potential cervical cancer (CC) therapeutic targets were obtained through Gene Expression Omnibus (GEO) data mining, differential analysis, and database screening. Hub targets were obtained by constructing protein-protein interaction (PPI) networks and CytoNCA topology analysis, including SRC, STAT3, TP53, PIK3R1, MAPK3, and PIK3CA. According to Gene ontology (GO) analysis, AP was primarily against CC by influencing gland development, oxidative stress processes, serine/threonine kinase, and tyrosine kinase activity. Enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the PI3K/AKT and MAPK signaling pathways play a crucial role in AP treatment for CC. The compound-target-pathway (C-T-P) network revealed that quercetin, methylprednisolone, and caudatin may play key roles in the treatment of CC. The results of molecular docking revealed that the core compound could bind significantly to the core target. In this study, the compounds in AP were systematically analyzed qualitatively, and the core components, core targets, and mechanisms of action of AP in the treatment of CC were screened through a combination of network pharmacology tools. Providing a scientific reference for the therapeutic material basis and quality control of AP.

A Relook into the Flavonoid Chemical Space of Moringa oleifera Lam. Leaves through a Combination of LC-MS and Molecular Networking

Moringa oleifera Lam. is a functional tree that is known to produce a variety of metabolites with purported pharmacological activities. It is frequently called the "miracle tree" due to its utilization in numerous nutraceutical and pharmacological contexts. This study was aimed at studying the chemical space of M. oleifera leaf extracts through molecular networking (MN), a tool that identifies metabolites by classifying them based on their MS-based fragmentation pattern similarities and signals. In this case, a special emphasis was placed on the flavonoid composition. The MN unraveled different molecular families such as flavonoids, carboxylic acids and derivatives, lignin glycosides, fatty acyls, and macrolactams that are found within the plant. In silico annotation tools such as network annotation propagation (NAP) and DEREPLICATOR, an unsupervised substructure identification tool (MS2LDA), and MolNet enhancer were also explored to further compliment the classic molecular networking output within the Global Natural Product Social (GNPS) site. In this study, common flavonoids found within Moringa oleifera were further annotated using MS2LDA. Utilizing computational tools allowed for the discovery of a wide range of structurally diverse flavonoid molecules within M. oleifera leaf extracts. The expansion of the flavonoid chemical repertoire in this plant arises from intricate glycosylation modifications, leading to the creation of structural isomers that manifest as isobaric ions during mass spectrometry (MS) analyses.

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform

Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

Rationalising the retro-Diels-Alder fragmentation pattern of viscutins using electrospray interface-tandem mass spectrometry coupled to theoretical modelling

RATIONALE

Although mass spectrometry (MS) is a powerful tool in structural elucidation of unknown flavonoids based on their unique fragmentation patterns, proposing the correct fragmentation mechanism is still a challenge from tandem mass spectrometry data only. In recent years, computational tools such as molecular networking and MS2LDA have played a major role in the identification of structurally related compounds through an in-depth survey of their fragmentation patterns.

METHODS

Therefore, in this study, three viscutin molecules in Viscum combreticola Engl. crude extracts were characterised using ultra-high-performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry and MS2LDA, a computational tool. Ion-trap mass spectrometry and density functional theoretical modelling were used as confirmatory tools to rationalise the unique fragmentation patterns observed for these molecules.

RESULTS

Here, MS2LDA revealed the presence of a unique Mass2Motif in all the three viscutin molecules at m/z 137, which was confirmed to be a ^1,3 A^- RDA (retro-Diels-Alder) fragmentation product using liquid chromatography-ion-trap mass spectrometry and density functional theoretical modelling. Moreover, MS2LDA proved to be useful in differentiating this spectral feature that was specific to viscutin molecules in the presence of other isobaric ions at m/z 137 occurring in compounds in other molecular families.

CONCLUSION

Therefore, the results of the current study showed that computational tools such as MS2LDA are essential in uncovering some gas-phase fragmentation reactions of molecules in MS and that theoretical modelling is a powerful tool in rationalising these reactions in metabolite identification.

Defence-related metabolic changes in wheat (Triticum aestivum L.) seedlings in response to infection by Puccinia graminis f. sp. tritici

Stem rust caused by the pathogen Puccinia graminis f. sp. tritici is a destructive fungal disease-causing major grain yield losses in wheat. Therefore, understanding the plant defence regulation and function in response to the pathogen attack is required. As such, an untargeted LC-MS-based metabolomics approach was employed as a tool to dissect and understand the biochemical responses of Koonap (resistant) and Morocco (susceptible) wheat varieties infected with two different races of P. graminis (2SA88 [TTKSF] and 2SA107 [PTKST]). Data was generated from the infected and non-infected control plants harvested at 14- and 21- days post-inoculation (dpi), with 3 biological replicates per sample under a controlled environment. Chemo-metric tools such as principal component analysis (PCA), orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were used to highlight the metabolic changes using LC-MS data of the methanolic extracts generated from the two wheat varieties. Molecular networking in Global Natural Product Social (GNPS) was further used to analyse biological networks between the perturbed metabolites. PCA and OPLS-DA analysis showed cluster separations between the varieties, infection races and the time-points. Distinct biochemical changes were also observed between the races and time-points. Metabolites were identified and classified using base peak intensities (BPI) and single ion extracted chromatograms from samples, and the most affected metabolites included flavonoids, carboxylic acids and alkaloids. Network analysis also showed high expression of metabolites from thiamine and glyoxylate, such as flavonoid glycosides, suggesting multi-faceted defence response strategy by understudied wheat varieties towards P. graminis pathogen infection. Overall, the study provided the insights of the biochemical changes in the expression of wheat metabolites in response to stem rust infection.

Metabolome profile variations in common bean (Phaseolus vulgaris L.) resistant and susceptible genotypes incited by rust (Uromyces appendiculatus)

The causal agent of rust, Uromyces appendiculatus is a major constraint for common bean (Phaseolus vulgaris) production. This pathogen causes substantial yield losses in many common bean production areas worldwide. U. appendiculatus is widely distributed and although there have been numerous breakthroughs in breeding for resistance, its ability to mutate and evolve still poses a major threat to common bean production. An understanding of plant phytochemical properties can aid in accelerating breeding for rust resistance. In this study, metabolome profiles of two common bean genotypes Teebus-RR-1 (resistant) and Golden Gate Wax (susceptible) were investigated for their response to U. appendiculatus races (1 and 3) at 14- and 21-days post-infection (dpi) using liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (LC-qTOF-MS). Non-targeted data analysis revealed 71 known metabolites that were putatively annotated, and a total of 33 were statistically significant. Key metabolites including flavonoids, terpenoids, alkaloids and lipids were found to be incited by rust infections in both genotypes. Resistant genotype as compared to the susceptible genotype differentially enriched metabolites including aconifine, D-sucrose, galangin, rutarin and others as a defence mechanism against the rust pathogen. The results suggest that timely response to pathogen attack by signalling the production of specific metabolites can be used as a strategy to understand plant defence. This is the first study to illustrate the utilization of metabolomics to understand the interaction of common bean with rust.

Mass spectrometry DDA parameters and global coverage of the metabolome: Spectral molecular networks of momordica cardiospermoides plants

INTRODUCTION

Molecular networking (MN) has emerged as a key strategy to organize and annotate untargeted tandem mass spectrometry (MS/MS) data generated using either data independent- or dependent acquisition (DIA or DDA). The latter presents a time-efficient approach where full scan (MS¹) and MS² spectra are obtained with shorter cycle times. However, there are limitations related to DDA parameters, some of which are (i) intensity threshold and (ii) collision energy. The former determines ion prioritization for fragmentation, and the latter defines the fragmentation of selected ions. These DDA parameters inevitably determine the coverage and quality of spectral data, which would affect the outputs of MN methods.

OBJECTIVES

This study assessed the extent to which the quality of the tandem spectral data relates to MN topology and subsequent implications in the annotation of metabolites and chemical classification relative to the different DDA parameters employed.

METHODS

Herein, characterising the metabolome of Momordica cardiospermoides plants, we employ classical MN performance indicators to investigate the effects of collision energies and intensity thresholds on the topology of generated MN and propagated annotations.

RESULTS

We demonstrated that the lowest predefined intensity thresholds and collision energies result in comprehensive molecular networks. Comparatively, higher intensity thresholds and collision energies resulted in fewer MS² spectra acquisition, subsequently fewer nodes, and a limited exploration of the metabolome through MN.

CONCLUSION

Contributing to ongoing efforts and conversations on improving DDA strategies, this study proposes a framework in which multiple DDA parameters are utilized to increase the coverage of ions acquired and improve the global coverage of MN, propagated annotations, and the chemical classification performed.

Eco-physiological response of secondary metabolites of teas: Review of quality attributes of herbal tea

Herbal tea is a rich source of secondary metabolites which are reputed to have medicinal and nutritional efficacy. These secondary metabolites are influenced by the abiotic and biotic stresses that improve the production of herbal teas in terms of biomass production, accumulation and partitioning of assimilates of compounds. In this study, various examples of herbal teas have been shown to respond differently to secondary metabolites affected by environmental factors. Thus, the meta-analysis of this study confirms that different herbal teas' response to environmental factors depends on the type of species, cultivar, and the degree of shade that the plant is exposed. It is also evident that the metabolic processes are also known to optimize the production of secondary metabolites which can thus be achieved by manipulating agronomic practices on herbal teas. The different phenolic compound in herbal teas possesses the antioxidant, antimicrobial, antiatherosclerosis, anti-inflammatory, antimutagenic, antitumor, antidiabetic and antiviral activities that are important in managing chronic diseases associated with lifestyle. It can be precluded that more studies should be conducted to establish interactive responses of biotic and abiotic environmental factors on quality attributes of herbal teas.

Integrative strategy for quality control of Radix Bupleuri based on non-targeted metabolomic profiling and molecular networking

Quality control of Radix Bupleuri (RB) can be challenging due to the complexity of origin, the similar morphological characteristics, and the diversity of the multiple components. In this study, an integrated strategy for extensive identification of metabolites in plants based on multiple data processing methods was proposed to distinguish four commercially available RB species. First, the pre-processed mass spectrometry data was uploaded to Global Natural Products Social Molecular Networking (GNPS) for spectral library search and molecular network analysis, which can effectively differentiate isomers and reduce molecular redundancy. Second, the possible cleavage mode was summarized from the characteristic MS/MS fragment ions of saikoside standard, and then the possible structure of saikoside in the sample was deduced according to the cleavage patterns. Third, collected all kinds of RB components reported in the literature and matched the information in the samples to obtain more comprehensive information about metabolites. Finally, chemical markers were found employing chemometrics. This strategy not only increases the variety and number of identified components, but also improves the accuracy of the data. Based on this strategy, a total of 132 components were identified from different species of RB, and 14 chemical constituents were considered to be potential chemical markers to distinguish four kinds of RB. Among them, saikogenin a, hydroxy-saikosaponin a, hydroxy-saikosaponin d, and rutinum were of great significance for identification. The method proposed in this study not only successfully identified and distinguished four species of RB, but also laid a good theoretical foundation for regulating the RB market. This strategy provides promising perspectives in the accurate analysis of the ingredients of traditional Chinese medicine.

Discovery of metabolic markers for the discrimination of Helwingia species based on bioactivity evaluation, plant metabolomics, and network pharmacology

RATIONALE

Helwingia japonica (HJ), a traditional medicinal plant, is commonly used for the treatment of dysentery, blood in the stool, and scald burns. Three major HJ species, Helwingia japonica (Thunb.) Dietr. (QJY), Helwingia himalaica Hook. f. et Thoms. ex C. B. Clarke, and Helwingia chinensis Batal., share great similarities in both morphology and chemical constituents. The discrimination of medicinal plants directly affects their pharmacological and clinical effects. Here, we solved the taxonomy uncertainty of these three HJ species and explored the discrimination and study of other traditional medicines (TMs).

METHODS

First, the anti-inflammatory effects of the three HJ species were compared using lipopolysaccharide (LPS)-induced inflammatory responses in mouse leukemia cells of monocyte macrophage (RAW) 264.7 cells. Then, plant metabolomics were performed in 48 batches of samples to discover chemical markers for discriminating different HJ species. Finally, network pharmacology was applied to explore the linkages among constituents, targets, and signaling pathways.

RESULTS

In vitro experiments showed that the QJY exhibited the most potential anti-inflammatory activities. Meanwhile, 172 compounds were tentatively identified and eight metabolites with higher relative content in QJY were designated as chemical markers to distinguish QJY and the other two species. According to the property of absorbed in vivo, threonic acid, arginine, and tyrosine were selected to construct a component-target-pathway network. The network pharmacology analysis confirmed that the chemotaxonomy differentiation was consistent with the bioactive assessment.

CONCLUSIONS

The present study demonstrates that bioactivity evaluation integrated with plant metabolomics and network pharmacology could be used as an effective approach to discriminate different TMs and discover the active compounds.

Margaritaria nobilis L.F. (Phyllanthaceae): Ethnopharmacology and Application of Computational Tools in the Annotation of Bioactive Molecules

Margaritaria nobilis is a shrubby species widely distributed in Brazil from the Amazon to the Atlantic Rainforest. Its bark and fruit are used in the Peruvian Amazon for disinfecting abscesses and as a tonic in pregnancy, respectively, and its leaves are used to treat cancer symptoms. From analyses via UHPLC-MS/MS, we sought to determine the chemical profile of the ethanolic extract of M. nobilis leaves by means of putative analyses supported by computational tools and spectral libraries. Thus, it was possible to annotate 44 compounds, of which 12 are phenolic acid derivatives, 16 are O-glycosylated flavonoids and 16 hydrolysable tannins. Among the flavonoids, although they are known, except for kaempferol, which has already been isolated from this species, the other flavonoids (10, 14, 15, 21, 24–26, 28–30, 33–35, 40 and 41) are being reported for the first time in the genus. Among the hydrolysable tannins, six ellagitannins present the HHDP group (6, 19, 22, 31, 38 and 43), one presents the DHHDP group (5), and four contain oxidatively modified congeners (12, 20, 37 and 39). Through the annotation of these compounds, we hope to contribute to the improved chemosystematics knowledge of the genus. Furthermore, supported by a metric review of the literature, we observed that many of the compounds reported here are congeners of authentically bioactive compounds. Thus, we believe that this work may help in understanding future pharmacological activities.

Analysis of botanicals and botanical supplements by LC-MS/MS-based molecular networking: Approaches for annotating plant metabolites and authentication

Prior to the advent of modern medicine, humans have used botanicals extensively for their therapeutic potential. With the majority of newly approved drugs having their origins in natural products, plants remain at the forefront of drug discovery. Continued research and discovery necessitate the use of high-throughput analytical methods to screen and identify bioactive components and potential therapeutic molecules from plants. Utilizing a pre-generated plant extract library, we subjected botanicals to LC-MS/MS-based molecular networking to determine their chemical composition and relatively quantify already known metabolites. The LC-MS/MS-based molecular networking approach was also used to authenticate the composition of dietary supplements against their corresponding plant specimens. The networking procedures provided concise visual representations of the chemical space and highly informative assessments of the botanicals. The procedures also proved to define the composition of the botanical supplements quickly and efficiently. This offered an innovative approach to metabolite profiling and authentication practices and additionally allowed for the identification of new, putatively unknown metabolites for future isolation and biological evaluation.