Assessing Specialized Metabolite Diversity in the Cosmopolitan Plant Genus Euphorbia L

Extraction, Annotation, and Purification of Resin Glycosides from the Morning Glory Family (Convolvulaceae)

The metabolic diversity of plants, comprising over a million different metabolites across the plant kingdom, harbors enormous potential for pharmaceutical and biotechnological applications. Resin glycoside (RG) acylsugars from the Convolvulaceae are of interest due to their medicinal and agricultural potential. However, understanding the biological relevance of RGs is challenging as they exhibit a high lineage-specific structural diversity. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with computational peak annotation can provide insights into this diversity. Here, we present a comprehensive protocol for the characterization of RG diversity using a sensitive LC-MS/MS instrument, a knowledge-based computational pipeline, and a web tool for peak annotation. The described experimental approach provides a step-by-step guide for RG sampling, extraction, purification for downstream analyses such as bioassays, and structural annotation using LC-MS/MS and computational metabolomics. The protocol focuses on qualitative analysis for putative annotation (Annotation Level 2 as defined by the Metabolomics Standards Initiative) of RGs and can serve as a valuable template for researchers exploring plant metabolic diversity beyond RGs and acylsugars.

Mapping of specialized metabolite terms onto a plant phylogeny using text mining and large language models

Plants produce a staggering array of chemicals that are the basis for organismal function and important human nutrients and medicines. However, it is poorly defined how these compounds evolved and are distributed across the plant kingdom, hindering a systematic view and understanding of plant chemical diversity. Recent advances in plant genome/transcriptome sequencing have provided a well-defined molecular phylogeny of plants, on which the presence of diverse natural products can be mapped to systematically determine their phylogenetic distribution. Here, we built a proof-of-concept workflow where previously reported diverse tyrosine-derived plant natural products were mapped onto the plant tree of life. Plant chemical-species associations were mined from literature, filtered, evaluated through manual inspection of over 2500 scientific articles, and mapped onto the plant phylogeny. The resulting "phylochemical" map confirmed several highly lineage-specific compound class distributions, such as betalain pigments and Amaryllidaceae alkaloids. The map also highlighted several lineages enriched in dopamine-derived compounds, including the orders Caryophyllales, Liliales, and Fabales. Additionally, the application of large language models, using our manually curated data as a ground truth set, showed that post-mining processing can largely be automated with a low false-positive rate, critical for generating a reliable phylochemical map. Although a high false-negative rate remains a challenge, our study demonstrates that combining text mining with language model-based processing can generate broader phylochemical maps, which will serve as a valuable community resource to uncover key evolutionary events that underlie plant chemical diversity and enable system-level views of nature's millions of years of chemical experimentation.

Accessing the specialized metabolome of actinobacteria from the bulk soil of Paullinia cupana Mart. on the Brazilian Amazon: a promising source of bioactive compounds against soybean phytopathogens

The Amazon rainforest, an incredibly biodiverse ecosystem, has been increasingly vulnerable to deforestation. Despite its undeniable importance and potential, the Amazonian microbiome has historically received limited study, particularly in relation to its unique arsenal of specialized metabolites. Therefore, in this study our aim was to assess the metabolic diversity and the antifungal activity of actinobacterial strains isolated from the bulk soil of Paullinia cupana, a native crop, in the Brazilian Amazon Rainforest. Extracts from 24 strains were subjected to UPLC-MS/MS analysis using an integrative approach that relied on the Chemical Structural and Compositional Similarity (CSCS) metric, GNPS molecular networking, and in silico dereplication tools. This procedure allowed the comprehensive understanding of the chemical space encompassed by these actinobacteria, which consists of features belonging to known bioactive metabolite classes and several unannotated molecular families. Among the evaluated strains, five isolates exhibited bioactivity against a panel of soybean fungal phytopathogens (Rhizoctonia solani, Macrophomina phaseolina, and Sclerotinia sclerotiorum). A focused inspection led to the annotation of pepstatins, oligomycins, hydroxamate siderophores and dorrigocins as metabolites produced by these bioactive strains, with potentially unknown compounds also comprising their metabolomes. This study introduces a pragmatic protocol grounded in established and readily available tools for the annotation of metabolites and the prioritization of strains to optimize further isolation of specialized metabolites. Conclusively, we demonstrate the relevance of the Amazonian actinobacteria as sources for bioactive metabolites useful for agriculture. We also emphasize the importance of preserving this biome and conducting more in-depth studies on its microbiota.

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

Chromosome-level Genome Assembly of Euphorbia peplus, a Model System for Plant Latex, Reveals that Relative Lack of Ty3 Transposons Contributed to Its Small Genome Size

Euphorbia peplus (petty spurge) is a small, fast-growing plant that is native to Eurasia and has become a naturalized weed in North America and Australia. Euphorbia peplus is not only medicinally valuable, serving as a source for the skin cancer drug ingenol mebutate, but also has great potential as a model for latex production owing to its small size, ease of manipulation in the laboratory, and rapid reproductive cycle. To help establish E. peplus as a new model, we generated a 267.2-Mb Hi-C-anchored PacBio HiFi nuclear genome assembly with a BUSCO score of 98.5%, a genome annotation based on RNA-seq data from six organs, and publicly accessible tools including a genome browser and an interactive organ-specific expression atlas. Chromosome number is highly variable across Euphorbia species. Using a comparative analysis of our newly sequenced E. peplus genome with other Euphorbiaceae genomes, we show that variation in Euphorbia chromosome number between E. peplus and Euphorbia lathyris is likely due to fragmentation and rearrangement rather than chromosomal duplication followed by diploidization of the duplicated sequence. Moreover, we found that the E. peplus genome is relatively compact compared with related members of the genus in part due to restricted expansion of the Ty3 transposon family. Finally, we identify a large gene cluster that contains many previously identified enzymes in the putative ingenol mebutate biosynthesis pathway, along with additional gene candidates for this biosynthetic pathway. The genomic resources we have created for E. peplus will help advance research on latex production and ingenol mebutate biosynthesis in the commercially important Euphorbiaceae family.

Improving annotation propagation on molecular networks through random walks: introducing ChemWalker

MOTIVATION

Annotation of the mass signals is still the biggest bottleneck for the untargeted mass spectrometry analysis of complex mixtures. Molecular networks are being increasingly adopted by the mass spectrometry community as a tool to annotate large-scale experiments. We have previously shown that the process of propagating annotations from spectral library matches on molecular networks can be automated using Network Annotation Propagation (NAP). One of the limitations of NAP is that the information for the spectral matches is only propagated locally, to the first neighbor of a spectral match. Here, we show that annotation propagation can be expanded to nodes not directly connected to spectral matches using random walks on graphs, introducing the ChemWalker python library.

RESULTS

Similarly to NAP, ChemWalker relies on combinatorial in silico fragmentation results, performed by MetFrag, searching biologically relevant databases. Departing from the combination of a spectral network and the structural similarity among candidate structures, we have used MetFusion Scoring function to create a weight function, producing a weighted graph. This graph was subsequently used by the random walk to calculate the probability of 'walking' through a set of candidates, departing from seed nodes (represented by spectral library matches). This approach allowed the information propagation to nodes not directly connected to the spectral library match. Compared with NAP, ChemWalker has a series of improvements, on running time, scalability and maintainability and is available as a standalone python package.

AVAILABILITY AND IMPLEMENTATION

ChemWalker is freely available at https://github.com/computational-chemical-biology/ChemWalker.

CONTACT

ridasilva@usp.br.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Using interdisciplinary, phylogeny-guided approaches to understand the evolution of plant metabolism

To cope with relentless environmental pressures, plants produce an arsenal of structurally diverse chemicals, often called specialized metabolites. These lineage-specific compounds are derived from the simple building blocks made by ubiquitous core metabolic pathways. Although the structures of many specialized metabolites are known, the underlying metabolic pathways and the evolutionary events that have shaped the plant chemical diversity landscape are only beginning to be understood. However, with the advent of multi-omics data sets and the relative ease of studying pathways in previously intractable non-model species, plant specialized metabolic pathways are now being systematically identified. These large datasets also provide a foundation for comparative, phylogeny-guided studies of plant metabolism. Comparisons of metabolic traits and features like chemical abundances, enzyme activities, or gene sequences from phylogenetically diverse plants provide insights into how metabolic pathways evolved. This review highlights the power of studying evolution through the lens of comparative biochemistry, particularly how placing metabolism into a phylogenetic context can help a researcher identify the metabolic innovations enabling the evolution of structurally diverse plant metabolites.

Gene discovery and virus-induced gene silencing reveal branched pathways to major classes of bioactive diterpenoids in Euphorbia peplus

SignificanceEuphorbia peplus, a member of the Euphorbia genus, is rich in jatrophane and ingenane diterpenoids. Using a metabolomics-guided transcriptomic approach to gene candidate identification, we have discovered a short-chain dehydrogenase gene involved in the production of the lathyrane jolkinol E. We have developed a virus-induced gene-silencing method in E. peplus that has allowed us to demonstrate the direct relationship between casbene and polycyclic diterpenoids and that jolkinol C acts as a key branch point intermediate in the production of ingenanes and jatrophanes. This work contributes both knowledge and tools for engineering production of bioactive diterpenoids in heterologous host systems, thus enabling their further evaluation and development.

Untargeted Metabolomics Sheds Light on the Diversity of Major Classes of Secondary Metabolites in the Malpighiaceae Botanical Family

Natural products produced by plants are one of the most investigated natural sources, which substantially contributed to the development of the natural products field. Even though these compounds are widely explored, the literature still lacks comprehensive investigations aiming to explore the evolution of secondary metabolites produced by plants, especially if classical methodologies are employed. The development of sensitive hyphenated techniques and computational tools for data processing has enabled the study of large datasets, being valuable assets for chemosystematic studies. Here, we describe a strategy for chemotaxonomic investigations using the Malpighiaceae botanical family as a model. Our workflow was based on MS/MS untargeted metabolomics, spectral searches, and recently described in silico classification tools, which were mapped into the latest molecular phylogeny accepted for this family. The metabolomic analysis revealed that different ionization modes and extraction protocols significantly impacted the chemical profiles, influencing the chemotaxonomic results. Spectral searches within public databases revealed several clades or genera-specific molecular families, being potential chemical markers for these taxa, while the in silico classification tools were able to expand the Malpighiaceae chemical space. The classes putatively annotated were used for ancestral character reconstructions, which recovered several classes of metabolites as homoplasies (i.e., non-exclusive) or synapomorphies (i.e., exclusive) for all sampled clades and genera. Our workflow combines several approaches to perform a comprehensive evolutionary chemical study. We expect it to be used on further chemotaxonomic investigations to expand chemical knowledge and reveal biological insights for compounds classes in different biological groups.

Euphocactoside, a New Megastigmane Glycoside from Euphorbia cactus Growing in Saudi Arabia

A phytochemical investigation of the aerial parts of Euphorbia cactus Ehrenb. ex Boiss. revealed a new megastigmane, euphocactoside (5), along with eleven known metabolites. Euphocactoside (5) is the 3-O-glucoside derivative of a polyhydroxylated megastigmane showing unprecedented structural features. The structure of euphocactoside, including stereochemical details, was elucidated by extensive spectroscopic analysis based on 1D and 2D nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HR-ESIMS). The isolated compounds were evaluated for their cytotoxic activity against three different human cancer cell lines, namely, A549 (lung), LoVo (colon), and MCF-7 (breast), using MTT assay, and moderate to marginal activities were observed for compounds 1–3, 8 and 9 against all three cell lines.

Unprecedented Insights on Chemical and Biological Significance of Euphorbia cactus Growing in Saudi Arabia

Euphorbia cactus Ehrenb ex Boiss. is a plant species reported from central Africa and the southern Arabian Peninsula, belonging to the family of Euphorbiaceae. The plant has ethnobotanical values and is well-known for its milky latex, which has been turned into medicine to treat various ailments. To the best of our knowledge, there have been no literature reports available on phytochemical constituents and antiproliferative mechanism of E. cactus. In the current study, the phytochemical investigation of E. cactus methanolic extract (ECME) resulted in the isolation and characterization of four secondary metabolites, which are reported for the first time from this plant species. In addition, the results of 1,1-diphenyl-2-picrylhydrazyl (DPPH•) and ferrous ion chelating (FIC) assays expressed maximum antioxidant activity by ECME and the isolated phytochemicals. Furthermore, ECME exerted a promising antiproliferative effect against different cancer cell lines, and the A549 lung cancer cells were the most sensitive with an IC₅₀ value of 20 µg/mL. The antiproliferative action of ECME in A549 cells was associated with cell accumulation in the G2/M phase and an increase in early and late apoptosis. In addition, RT-PCR and western blot analysis revealed that ECME decreased the anti-apoptotic (Bcl-2) expression, while the expression of pro-apoptotic (Bax) and caspase-3 were increased. This study provides the first insight into the phytochemical constituents and the antiproliferative mechanism of ECME, implying that it could be exploited as a promising natural source for developing new cancer therapies. Further preclinical research is warranted to support the current results.

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.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

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.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Next-Generation Mass Spectrometry Metabolomics Revives the Functional Analysis of Plant Metabolic Diversity

The remarkable diversity of specialized metabolites produced by plants has inspired several decades of research and nucleated a long list of theories to guide empirical ecological studies. However, analytical constraints and the lack of untargeted processing workflows have long precluded comprehensive metabolite profiling and, consequently, the collection of the critical currencies to test theory predictions for the ecological functions of plant metabolic diversity. Developments in mass spectrometry (MS) metabolomics have revolutionized the large-scale inventory and annotation of chemicals from biospecimens. Hence, the next generation of MS metabolomics propelled by new bioinformatics developments provides a long-awaited framework to revisit metabolism-centered ecological questions, much like the advances in next-generation sequencing of the last two decades impacted all research horizons in genomics. Here, we review advances in plant (computational) metabolomics to foster hypothesis formulation from complex metabolome data. Additionally, we reflect on how next-generation metabolomics could reinvigorate the testing of long-standing theories on plant metabolic diversity.

Effect of the Andean Geography and Climate on the Specialized Metabolism of Its Vegetation: The Subtribe Espeletiinae (Asteraceae) as a Case Example

The Andean mountains are 'center stage' to some of the most spectacular examples of plant diversifications, where geographic isolation and past climatic fluctuations have played a major role. However, the influence of Andean geography and climate as drivers of metabolic variation in Andean plants is poorly elucidated. Here, we studied the influence of those factors on the metabolome of the subtribe Espeletiinae (Asteraceae) using liquid chromatography coupled to high-resolution mass spectrometry data of over two hundred samples from multiple locations. Our results demonstrate that metabolic profiles can discriminate Espeletiinae taxa at different geographic scales, revealing inter- and intraspecific metabolic variations: at the country level, segregation between Colombian and Venezuelan taxa was observed; regionally, between páramo massifs; and locally, between páramo complexes. Metabolic differences in Espeletiinae were mainly explained by geographic isolation, although differences in taxonomic genera, temperature, and elevation, were also important. Furthermore, we found that different species inhabiting the same páramo complex showed stronger chemical similarities than the same species at different locations, corroborating that geographic isolation represents the main driver of metabolic change in Espeletiinae. The current study serves as a starting point to fill in the gaps in how Andean geography and climate have shaped the metabolism of its vegetation and reveal the potential of untargeted metabolomics to study the environmental physiology of plants.

The evolutionary fate of rpl32 and rps16 losses in the Euphorbia schimperi (Euphorbiaceae) plastome

Gene transfers from mitochondria and plastids to the nucleus are an important process in the evolution of the eukaryotic cell. Plastid (pt) gene losses have been documented in multiple angiosperm lineages and are often associated with functional transfers to the nucleus or substitutions by duplicated nuclear genes targeted to both the plastid and mitochondrion. The plastid genome sequence of Euphorbia schimperi was assembled and three major genomic changes were detected, the complete loss of rpl32 and pseudogenization of rps16 and infA. The nuclear transcriptome of E. schimperi was sequenced to investigate the transfer/substitution of the rpl32 and rps16 genes to the nucleus. Transfer of plastid-encoded rpl32 to the nucleus was identified previously in three families of Malpighiales, Rhizophoraceae, Salicaceae and Passifloraceae. An E. schimperi transcript of pt SOD-1-RPL32 confirmed that the transfer in Euphorbiaceae is similar to other Malpighiales indicating that it occurred early in the divergence of the order. Ribosomal protein S16 (rps16) is encoded in the plastome in most angiosperms but not in Salicaceae and Passifloraceae. Substitution of the E. schimperi pt rps16 was likely due to a duplication of nuclear-encoded mitochondrial-targeted rps16 resulting in copies dually targeted to the mitochondrion and plastid. Sequences of RPS16-1 and RPS16-2 in the three families of Malpighiales (Salicaceae, Passifloraceae and Euphorbiaceae) have high sequence identity suggesting that the substitution event dates to the early divergence within Malpighiales.

Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.

In Vitro Modulation of P-Glycoprotein Activity by Euphorbia intisy Essential Oil on Acute Myeloid Leukemia Cell Line HL-60R

Euphorbia species have a large spectrum of traditional medicinal uses. We tested the biological activities of the essential oil (EO) of Euphorbia intisy Drake in an acquired multidrug resistance leukemia model to assess whether the EO obtained by hydrodistillation of stems was able to reverse the resistant phenotype. HL-60R cell lines are characterized by the overexpression of P-glycoprotein (P-gp), inhibitors of apoptosis proteins (IAPs) and constitutive expression of NF-κB. EO chemical composition was determined by GC/MS analysis; cytotoxic activity of EO by MTS assay alone or in combination with doxorubicin; pro-apoptotic effect and doxorubicin accumulation were analyzed by flow cytometry; P-gp ATPase activity was measured by P-gp-Glo™ assay systems kit. The ability to inhibit NF-κB and its target genes was also assessed. E. intisy EO exhibited a comparable cytotoxic effect and ability to block P-gp in both the HL-60 and its MDR variant HL-60R. In addition, EO suppressed P-gp protein expression and significantly downregulated MDR1 mRNA level, as well as some IAPs proteins, probably through the inhibition of NF-κB. Our results suggest that E. intisy EO could reverse P-gp-mediated drug resistance in tumor cells acting as a chemosensitizing agent.

Plastome Evolution in the Hyperdiverse Genus Euphorbia (Euphorbiaceae) Using Phylogenomic and Comparative Analyses: Large-Scale Expansion and Contraction of the Inverted Repeat Region

With c. 2,000 species, Euphorbia is one of the largest angiosperm genera, yet a lack of chloroplast genome (plastome) resources impedes a better understanding of its evolution. In this study, we assembled and annotated 28 plastomes from Euphorbiaceae, of which 15 were newly sequenced. Phylogenomic and comparative analyses of 22 plastome sequences from all four recognized subgenera within Euphorbia revealed that plastome length in Euphorbia is labile, presenting a range of variation c. 42 kb. Large-scale expansions of the inverted repeat (IR) region were identified, and at the extreme opposite, the near-complete loss of the IR region (with only 355 bp left) was detected for the first time in Euphorbiaceae. Other structural variations, including gene inversion and duplication, and gene loss/pseudogenization, were also observed. We screened the most promising molecular markers from both intergenic and coding regions for phylogeny-based utilities, and estimated maximum likelihood and Bayesian phylogenies from four datasets including whole plastome sequences. The monophyly of Euphorbia is supported, and its four subgenera are recovered in a successive sister relationship. Our study constitutes the first comprehensive investigation on the plastome structural variation in Euphorbia and it provides resources for phylogenetic research in the genus, facilitating further studies on its taxonomy, evolution, and conservation.

Assessing the genetic and chemical diversity of Taraxacum species in the Korean Peninsula

The genetic relationship between Taraxacum species, also known as the dandelion, is complicated because of asexual and mixed sexual apomictic reproduction. The usage of Taraxacum species in traditional medicines make their specialized metabolism important, but interspecific chemical difference has rarely been reported for the genus. In this study, we assembled the chloroplast genome and 45S rDNA of six Taraxacum species that occur in Korea (T. campylodes, T. coreanum, T. erythrospermum, T. mongolicum, T. platycarpum, and T. ussuriense), and performed a comparative analysis, which revealed their phylogenetic relationships and possible natural hybridity. We also performed a liquid chromatography-mass spectrometry-based phytochemical analysis to reveal interspecific chemical diversity. The comparative metabolomics analysis revealed that Taraxacum species could be separated into three chemotypes according to their major defensive specialized metabolites, which were the sesquiterpene lactones, the phenolic inositols, and chlorogenic acid derivatives. The CP DNA- and 45S rDNA-based phylogenetic trees showed a tangled relationship, which supports the notion of ongoing hybridization of wild Taraxacum species. The untargeted LC-MS analysis revealed that each Taraxacum plant exhibits species-specific defensive specialized metabolism. Moreover, 45S rDNA-based phylogenetic tree correlated with the hierarchical cluster relied on metabolite compositions. Given the coincidence between these analyses, we represented that 45S rDNA could well reflect overall nuclear genome variation in Taraxacum species.

Bioactive diterpenoid metabolism and cytotoxic activities of genetically transformed Euphorbia lathyris roots

Plants in the genus Euphorbia produce a wide variety of pharmacologically active diterpenoids with anticancer, multidrug resistance reversal, and antiviral properties. Some are the primary industrial source of ingenol mebutate, which is approved for treatment of the precancerous skin condition actinic keratosis. Similar to other high value phytochemicals, Euphorbia diterpenoids accumulate at low concentrations in planta and chemical synthesis produces similarly low yields. We established genetically transformed root cultures of Euphorbia lathryis as a strategy to gain greater access to diterpenoids from this genus. Transformed roots produced via stem explant infection with Agrobacterium rhizogenes strain 15834 recapitulated the metabolite profiles of field-grown plant roots and aerial tissues. Several putative diterpenoids were present in transformed roots, including ingenol and closely related structures, indicating that root cultures are a promising approach to Euphorbia-specific diterpenoid production. Treatment with methyl jasmonate led to a significant, albeit transient increase in mRNA levels of early diterpenoid biosynthetic enzymes (farnesyl pyrophosphate synthase, geranylgeranyl pyrophosphate synthase, and casbene synthase), suggesting that elicitation could prove useful in future pathway characterization and metabolic engineering efforts. We also show the potential of transformed E. lathyris root cultures for natural product drug discovery applications by measuring their cytotoxic activities using a panel of human carcinoma cell lines derived from prostate, cervix, breast, and lung.

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule-focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user's underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking-one of the main analysis tools used within the GNPS platform-creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.

Assessing specialized metabolite diversity of Alnus species by a digitized LC-MS/MS data analysis workflow

Alnus spp. (Betulaceae) have been used for treatments of hemorrhage, burn injuries, antipyretic fever, diarrhea, and alcoholism in traditional medicines. In this study, a digitized LC-MS/MS data analysis workflow was applied to provide an overview on chemical diversity of 15 Alnus extracts prepared from bark, twigs, leaves, and fruits of A. japonica, A. firma, A. hirsuta, and A. hirsuta var. sibirica. Most of the MS/MS spectra could be putatively annotated based on library matching, in silico fragmentation, and substructural topic modeling. The putative annotation allowed us to discriminate the extracts into three chemotypes based on dominant chemical scaffolds: diarylheptanoids, flavonoids or tannins. This high-throughput chemical annotation was correlated with α-glucosidase inhibition data of extracts, and it allowed us to identify gallic acid as the major active compound of A. firma.

Comparative Chloroplast Genomics of Endangered Euphorbia Species: Insights into Hotspot Divergence, Repetitive Sequence Variation, and Phylogeny

Euphorbia is one of the largest genera in the Euphorbiaceae family, comprising 2000 species possessing commercial, medicinal, and ornamental importance. However, there are very little data available on their molecular phylogeny and genomics, and uncertainties still exist at a taxonomic level. Herein, we sequence the complete chloroplast (cp) genomes of two species, E. larica and E. smithii, of the genus Euphorbia through next-generation sequencing and perform a comparative analysis with nine related genomes in the family. The results revealed that the cp genomes had similar quadripartite structure, gene content, and genome organization with previously reported genomes from the same family. The size of cp genomes ranged from 162,172 to 162,358 bp with 132 and 133 genes, 8 rRNAs, 39 tRNA in E. smithii and E. larica, respectively. The numbers of protein-coding genes were 85 and 86, with each containing 19 introns. The four-junction regions were studied and results reveal that rps19 was present at J_LB (large single copy region and inverted repeat b junction) in E. larica where its complete presence was located in the IRb (inverted repeat b) region in E. smithii. The sequence comparison revealed that highly divergent regions in rpoC1, rpocB, ycf3, clpP, petD, ycf1, and ndhF of the cp genomes might provide better understanding of phylogenetic inferences in the Euphorbiaceae and order Malpighiales. Phylogenetic analyses of this study illustrate sister clades of E. smithii with E. tricullii and these species form a monophyletic clade with E. larica. The current study might help us to understand the genome architecture, genetic diversity among populations, and evolutionary depiction in the genera.

MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation

Mass spectrometry (MS) offers unrivalled sensitivity for the metabolite profiling of complex biological matrices encountered in natural products (NP) research. The massive and complex sets of spectral data generated by such platforms require computational approaches for their interpretation. Within such approaches, computational metabolite annotation automatically links spectral data to candidate structures via a score, which is usually established between the acquired data and experimental or theoretical spectral databases (DB). This process leads to various candidate structures for each MS features. However, at this stage, obtaining high annotation confidence level remains a challenge notably due to the extensive chemodiversity of specialized metabolomes. The design of a metascore is a way to capture complementary experimental attributes and improve the annotation process. Here, we show that integrating the taxonomic position of the biological source of the analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and complement the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5- to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata, particularly biological sources, is timely and critical for the NP research community.