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.

Metabolite profiling of susceptible and resistant wheat (Triticum aestivum) cultivars responding to Puccinia striiformis f. sp. tritici infection.

BACKGROUND: Puccinia striiformis f. sp. tritici (Pst) is an economically devasting disease that is prominent in cereal crops such as wheat (Triticum aestivum). The fungal pathogen can cause approximately 30-70% losses in crop productivity and yields. Pst has become difficult to manage due to its ease of transmission through wind dispersal over long distances, and intercontinental dispersal has been previously reported. The ease of transmission has resulted in further destruction because of new and more virulent strains infecting crops previously resistant to a different strain. RESULTS: In this study, a liquid chromatography-mass spectrometry-based untargeted metabolomics approach, in combination with multivariate data analytical tools, was used to elucidate the mechanistic nature of the defence systems of a Pst-resistant and a susceptible wheat cultivar infected with P. striiformis. We also investigated the time-dependant metabolic reconfiguration of infected plants over a four-week period. The untargeted metabolomic analysis revealed a time-course metabolic reprogramming involving phenylpropanoids (majority flavonoids), amino acids, lipids, benzoic acids, TCA cycle intermediates and benzoxazinoids responding to Pst infection. Interestingly, the results do not show a linear course for the decrease and increase (up-/down-regulation) of said classes of metabolites, but rather the up- or down-regulation of specific metabolites in response to the pathogen infection. The resistant Koonap cultivar had an abundance of phenolic compounds such as rutin, isoorintin-7-O-glucoside and luteolin-6-C-hexoside-O-hexoside. These compounds showed a decrease over time in control Koonap plants compared to an increase in Pst-infected plants. These metabolites were down-regulated in the susceptible Gariep cultivar, which could serve as biomarkers for plant responses to biotic stress and resistance against Pst. CONCLUSIONS: Overall, an LC-MS-based metabolomics approach allowed for the metabolic profiling and analysis of the impact of plant-pathogen interactions on the overall plant metabolome and provided a real-time snapshot of the differential significant metabolic perturbations occurring in wheat plants responding to the Pst pathogen. The Pst-resistant Koonap cultivar showed a rapid accumulation of defence metabolites in response to pathogen infection compared to the susceptible Gariep cultivar. These findings provide insight into the mechanistic biochemical nature of plant-microbe interactions and the prospects of metabolic engineering for improved plant tolerance and resistance to biotic stresses.

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.

Providing metabolomics education and training: pedagogy and considerations.

BACKGROUND: Metabolomics is a highly multidisciplinary and non-standardised research field. Metabolomics researchers must possess and apply extensive cross-disciplinary content knowledge, subjective experience-based judgement, and the associated diverse skill sets. Accordingly, appropriate educational and training initiatives are important in developing this knowledge and skills base in the metabolomics community. For these initiatives to be successful, they must consider both pedagogical best practice and metabolomics-specific contextual challenges. AIM OF REVIEW: The aim of this review is to provide consolidated pedagogical guidance for educators and trainers in metabolomics educational and training programmes. KEY SCIENTIFIC CONCEPTS OF REVIEW: In this review, we discuss the principles of pedagogical best practice as they relate to metabolomics. We then discuss the challenges and considerations in developing and delivering education and training in metabolomics. Finally, we present examples from our own teaching practice to illustrate how pedagogical best practice can be integrated into metabolomics education and training programmes.

Metabolomic Profiling of the Host Response of Tomato (Solanum lycopersicum) Following Infection by Ralstonia solanacearum.

Tomato (Solanum lycopersicum) is an important dietary source of bioactive phytochemicals and active breeding programs constantly produce new cultivars possessing superior and desirable traits. The phytopathogenic Ralstonia solanacearum, the causal agent of bacterial wilt, is a highly destructive bacterial disease with a high economic impact on tomato production. This study followed an untargeted metabolomic approach involving four tomato cultivars and aimed at the identification of secondary metabolites involved in plant defense after infection with R. solanacearum. Liquid chromatography coupled to mass spectrometry (LC-MS) in combination with multivariate data analysis and chemometric modelling were utilized for the identification of discriminant secondary metabolites. The total of 81 statistically selected features were annotated belonging to the metabolite classes of amino acids, organic acids, fatty acids, various derivatives of cinnamic acid and benzoic acids, flavonoids and steroidal glycoalkaloids. The results indicate that the phenylpropanoid pathway, represented by flavonoids and hydroxycinnamic acids, is of prime importance in the tomato defense response. The hydroxycinnamic acids esters of quinic acid, hexoses and glucaric acids were identified as signatory biomarkers, as well as the hydroxycinnamic acid amides to polyamines and tyramine. Interestingly, the rapid and differential accumulation of putrescine, dopamine, and tyramine derivatives, along with the presence of a newly documented metabolite, feruloyl serotonin, were documented in the infected plants. Metabolite concentration variability in the different cultivar tissues point to cultivar-specific variation in the speed and manner of resource redistribution between the host tissues. These metabolic phenotypes provide insights into the differential metabolic signatures underlying the defense metabolism of the four cultivars, defining their defensive capabilities to R. solanacearum.

Mass spectrometry in untargeted liquid chromatography/mass spectrometry metabolomics: Electrospray ionisation parameters and global coverage of the metabolome.

RATIONALE: Liquid chromatography coupled to mass spectrometry (LC/MS) is a dominant analytical platform in metabolomics, because of the high sensitivity and resolution, thus enabling large-scale coverage of metabolomes. Correspondingly, electrospray ionisation (ESI) is the favoured ionisation method in untargeted LC/MS metabolomics given the ability to produce large numbers of ions. In the workflow of LC/ESI-MS metabolomics, maximising the ionisation efficiency over a wide mass range is inevitably an essential and determining step, subsequently defining the extent of coverage of the metabolome under investigation. Thus in this study, electronic factors related to the functioning of the ESI source, namely the capillary and sample cone voltages, were explored to investigate the influence on the data acquired in metabolomic investigations. METHODS: Hydromethanolic samples from an untargeted study (sorghum plants responding dynamically to fungal infection) were analysed on a high-resolution/definition LC/ESI-MS system. Here the capillary and sample cone voltages of the ZSpray™ ESI source were varied between 1.5-3.0 kV and 10.0-40.0 V, respectively. The acquired data were processed with MarkerLynx™ software and analysed using central composite design response surface methodology and chemometric approaches (principal component analysis and orthogonal projection latent structures-discriminant analysis). RESULTS: The results evidently demonstrate that both capillary and sampling cone voltages not only significantly influence the recorded MS signals with regard to the number and abundance of features, but also the overall structure of the collected data. This consequently impacts on the information extracted from the data and thus affects coverage of the metabolome. CONCLUSIONS: The observations postulate in that, untargeted LC/MS metabolomics, 'what you see is what you ionise'. Although there is convergence of collected data under different ESI conditions, the nuances observed indicate that the exploration of different ion source settings could be the best trade-off in expanding and maximising the metabolome coverage in untargeted metabolomic experiments.

Metabolomics in Plant Priming Research: The Way Forward?

A new era of plant biochemistry at the systems level is emerging, providing detailed descriptions of biochemical phenomena at the cellular and organismal level. This new era is marked by the advent of metabolomics­the qualitative and quantitative investigation of the entire metabolome (in a dynamic equilibrium) of a biological system. This field has developed as an indispensable methodological approach to study cellular biochemistry at a global level. For protection and survival in a constantly-changing environment, plants rely on a complex and multi-layered innate immune system. This involves surveillance of 'self' and 'non-self,' molecule-based systemic signalling and metabolic adaptations involving primary and secondary metabolites as well as epigenetic modulation mechanisms. Establishment of a pre-conditioned or primed state can sensitise or enhance aspects of innate immunity for faster and stronger responses. Comprehensive elucidation of the molecular and biochemical processes associated with the phenotypic defence state is vital for a better understanding of the molecular mechanisms that define the metabolism of plant⁻pathogen interactions. Such insights are essential for translational research and applications. Thus, this review highlights the prospects of metabolomics and addresses current challenges that hinder the realisation of the full potential of the field. Such limitations include partial coverage of the metabolome and maximising the value of metabolomics data (extraction of information and interpretation). Furthermore, the review points out key features that characterise both the plant innate immune system and enhancement of the latter, thus underlining insights from metabolomic studies in plant priming. Future perspectives in this inspiring area are included, with the aim of stimulating further studies leading to a better understanding of plant immunity at the metabolome level.

Comparative Metabolic Phenotyping of Tomato (Solanum lycopersicum) for the Identification of Metabolic Signatures in Cultivars Differing in Resistance to Ralstonia solanacearum.

Tomato (Solanum lycopersicum) is an important dietary source which contains numerous bioactive phytochemicals. Active breeding programs constantly produce new cultivars possessing superior and desirable traits. However, the underlying molecular signatures that functionally describe these traits are yet to be elucidated. Thus, in this study we used an untargeted metabolomic approach to describe differential metabolic profiles of four cultivars described as having high to intermediate resistance to Ralstonia solanacearum. Metabolites were methanol-extracted from leaves, stems and root tissues and analyzed by liquid chromatography coupled with high definition mass spectrometry. Multivariate data analysis revealed cultivar-related differential metabolic phenotypes. A total of 41 metabolites were statistically selected and annotated, consisting of amino acids, organic acids, lipids, derivatives of cinnamic acid and benzoic acids, flavonoids and steroidal glycoalkaloids which were especially prominent in the two highly resistant cultivars. Interestingly, the less resistant cultivars had various fatty acid derivatives in root extracts that contributed to the differentiated metabolic signatures. Moreover, the metabolic phenotype of the STAR9008 (8SC) cultivar with intermediate resistance, was characterized by derivatives of cinnamic acids and flavonoids but at lower levels compared to the resistant cultivars. The 8SC cultivar also exhibited a lack of hydroxybenzoic acid biomarkers, which may be attributed to its lower resistance. These metabolic phenotypes provide insights into the differential metabolic signatures underlying the metabolism of these four cultivars, defining their respective phenotypic traits such as their resistance, tolerance or susceptibility to Ralstonia solanacearum.

Data-Driven Characterization of Metabolome Reprogramming during Early Development of Sorghum Seedlings.

Specialized metabolites are produced via discrete metabolic pathways. These small molecules play significant roles in plant growth and development, as well as defense against environmental stresses. These include damping off or seedling blight at a post-emergence stage. Targeted metabolomics was followed to gain insights into metabolome changes characteristic of different developmental stages of sorghum seedlings. Metabolites were extracted from leaves at seven time points post-germination and analyzed using ultra-high performance liquid chromatography coupled to mass spectrometry. Multivariate statistical analysis combined with chemometric tools, such as principal component analysis, hierarchical clustering analysis, and orthogonal partial least squares-discriminant analysis, were applied for data exploration and to reduce data dimensionality as well as for the selection of potential discriminant biomarkers. Changes in metabolome patterns of the seedlings were analyzed in the early, middle, and late stages of growth (7, 14, and 29 days post-germination). The metabolite classes were amino acids, organic acids, lipids, cyanogenic glycosides, hormones, hydroxycinnamic acid derivatives, and flavonoids, with the latter representing the largest class of metabolites. In general, the metabolite content showed an increase with the progression of the plant growth stages. Most of the differential metabolites were derived from tryptophan and phenylalanine, which contribute to innate immune defenses as well as growth. Quantitative analysis identified a correlation of apigenin flavone derivatives with growth stage. Data-driven investigations of these metabolomes provided new insights into the developmental dynamics that occur in seedlings to limit post-germination mortality.

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.

microbeMASST: a taxonomically informed mass spectrometry search tool for microbial metabolomics data.

microbeMASST, a taxonomically informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbe-derived metabolites and relative producers without a priori knowledge will vastly enhance the understanding of microorganisms' role in ecology and human health.

Metabolomic analysis of methyl jasmonate-induced triterpenoid production in the medicinal herb Centella asiatica (L.) urban.

Centella asiatica is an important source of biologically active pentacyclic triterpenoids. The enhancement of the biosynthesis of the centellosides by manipulation of associated metabolic pathways is receiving much attention. Jasmonates play critical roles in plant metabolism by up-regulating the expression of genes related to secondary metabolites. Here, we investigated the effect of methyl jasmonate (MeJa) in C. asiatica through targeted metabolomic profiling of asiaticoside and madecassoside as well as their aglycones, asiatic acid and madecassic acid. Cell suspensions were treated with 0.2 mM MeJa for 2, 4 and 6 days. Liquid chromatography coupled to mass spectrometry (LC-MS) was used to explore induced changes in metabolite profiles, both qualitatively and quantitatively. Principal component analysis (PCA)-derived scores plots revealed clusters of sample replicates for control and treated samples at 2, 4 and 6 days while loading plots aided in identifying signatory biomarkers (asiatic acid and madecassic acid, as well as asiaticoside and madecassoside) that clearly demonstrate the variability between samples. In addition to increased biosynthesis of the targeted centelloids, other differential changes in the intracellular metabolite profiles reflected the response of the C. asiatica cells to the MeJa-treatment as a reprogramming of the metabolome.

Computational Metabolomics to Elucidate Molecular Signaling and Regulatory Mechanisms Associated with Biostimulant-Mediated Growth Promotion and Abiotic Stress Tolerance in Crop Plants.

Biostimulants show potentials as sustainable strategies for improved crop development and stress resilience. However, the cellular and molecular mechanisms, in particular the signaling and regulatory events, governing the agronomically observed positive effects of biostimulants on plants remain enigmatic, thus hampering novel formulation and exploration of biostimulants. Metabolomics offers opportunities to elucidate metabolic and regulatory processes that define biostimulant-induced changes in the plant's biochemistry and physiology, thus contributing to decode the modes of action of biostimulants. Here, we describe an application of metabolomics to elucidate biostimulant effects on crop plants. Using the case study of a humic substance (HS)-based biostimulant applied on maize plants, under normal and nutrient-starved stress conditions, this chapter proposes key methodological guidance and considerations of computational metabolomics approach to investigate metabolic and regulatory reconfiguration and networks underlying biostimulant-induced physiological changes in plants. Computational metabolome mining tools, in the Global Natural Products Social Molecular Networking (GNPS) ecosystem, are highlighted as well as metabolic pathway and network analysis for biological interpretation of the data.

Metabolic Reprogramming of Barley in Response to Foliar Application of Dichlorinated Functional Analogues of Salicylic Acid as Priming Agents and Inducers of Plant Defence.

Designing innovative biological crop protection strategies to stimulate natural plant immunity is motivated by the growing need for eco-friendly alternatives to conventional biocidal agrochemicals. Salicylic acid (SA) and analogues are known chemical inducers of priming plant immunity against environmental stresses. The aim of the study was to study the metabolic reprogramming in barley plants following an application of three proposed dichlorinated inducers of acquired resistance. 3,5-Dichloroanthranilic acid, 2,6-dichloropyridine-4-carboxylic acid, and 3,5-dichlorosalicylic acid were applied to barley at the third leaf stage of development and harvested at 12, 24, and 36 h post-treatment. Metabolites were extracted using methanol for untargeted metabolomics analyses. Samples were analysed by ultra-high performance liquid chromatography coupled to high-definition mass spectrometry (UHPLC-HDMS). Chemometric methods and bioinformatics tools were used to mine and interpret the generated data. Alterations in the levels of both primary and secondary metabolites were observed. The accumulation of barley-specific metabolites, hordatines, and precursors was observed from 24 h post-treatment. The phenylpropanoid pathway, a marker of induced resistance, was identified among the key mechanisms activated by the treatment with the three inducers. No salicylic acid or SA derivatives were annotated as signatory biomarkers; instead, jasmonic acid precursors and derivatives were found as discriminatory metabolites across treatments. The study highlights differences and similarities in the metabolomes of barley after treatment with the three inducers and points to the triggering chemical changes associated with defence and resistance. This report is the first of its kind, and the knowledge acquired provides deeper insight into the role of dichlorinated small molecules as inducers of plant immunity and can be used in metabolomics-guided plant improvement programmes.

An Integrated Molecular Networking and Docking Approach to Characterize the Metabolome of Helichrysum splendidum and Its Pharmaceutical Potentials.

South Africa is rich in diverse medicinal plants, and it is reported to have over 35% of the global Helichrysum species, many of which are utilized in traditional medicine. Various phytochemical studies have offered valuable insights into the chemistry of Helichrysum plants, hinting at bioactive components that define the medicinal properties of the plant. However, there are still knowledge gaps regarding the size and diversity of the Helichrysum chemical space. As such, continuous efforts are needed to comprehensively characterize the phytochemistry of Helichrysum, which will subsequently contribute to the discovery and exploration of Helichrysum-derived natural products for drug discovery. Thus, reported herein is a computational metabolomics work to comprehensively characterize the metabolic landscape of the medicinal herb Helichrysum splendidum, which is less studied. Metabolites were methanol-extracted and analyzed on a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. Spectral data were mined using molecular networking (MN) strategies. The results revealed that the metabolic map of H. splendidum is chemically diverse, with chemical superclasses that include organic polymers, benzenoids, lipid and lipid-like molecules, alkaloids, and derivatives, phenylpropanoids and polyketides. These results point to a vastly rich chemistry with potential bioactivities, and the latter was demonstrated through computationally assessing the binding of selected metabolites with CDK-2 and CCNB1 anti-cancer targets. Molecular docking results showed that flavonoids (luteolin, dihydroquercetin, and isorhamnetin) and terpenoids (tiliroside and silybin) interact strongly with the CDK-2 and CCNB1 targets. Thus, this work suggests that these flavonoid and terpenoid compounds from H. splendidum are potentially anti-cancer agents through their ability to interact with these proteins involved in cancer pathways and progression. As such, these actionable insights are a necessary step for further exploration and translational studies for H. splendidum-derived compounds for drug discovery.

Metabolomic Reconfiguration in Primed Barley (Hordeum vulgare) Plants in Response to Pyrenophora teres f. teres Infection.

Necrotrophic fungi affect a wide range of plants and cause significant crop losses. For the activation of multi-layered innate immune defences, plants can be primed or pre-conditioned to rapidly and more efficiently counteract this pathogen. Untargeted and targeted metabolomics analyses were applied to elucidate the biochemical processes involved in the response of 3,5-dichloroanthranilic acid (3,5-DCAA) primed barley plants to Pyrenophora teres f. teres (Ptt). A susceptible barley cultivar ('Hessekwa') at the third leaf growth stage was treated with 3,5-DCAA 24 h prior to infection using a Ptt conidia suspension. The infection was monitored over 2, 4, and 6 days post-inoculation. For untargeted studies, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-MS) was used to analyse methanolic plant extracts. Acquired data were processed to generate the data matrices utilised in chemometric modelling and multi-dimensional data mining. For targeted studies, selected metabolites from the amino acids, phenolic acids, and alkaloids classes were quantified using multiple reaction monitoring (MRM) mass spectrometry. 3,5-DCAA was effective as a priming agent in delaying the onset and intensity of symptoms but could not prevent the progression of the disease. Unsupervised learning methods revealed clear differences between the sample extracts from the control plants and the infected plants. Both orthogonal projection to latent structure-discriminant analysis (OPLS-DA) and 'shared and unique structures' (SUS) plots allowed for the extraction of potential markers of the primed and naïve plant responses to Ptt. These include classes of organic acids, fatty acids, amino acids, phenolic acids, and derivatives and flavonoids. Among these, 5-oxo-proline and citric acid were notable as priming response-related metabolites. Metabolites from the tricarboxylic acid pathway were only discriminant in the primed plant infected with Ptt. Furthermore, the quantification of targeted metabolites revealed that hydroxycinnamic acids were significantly more prominent in the primed infected plants, especially at 2 d.p.i. Our research advances efforts to better understand regulated and reprogrammed metabolic responses that constitute defence priming in barley against Ptt.

Metabolomic evaluation of PGPR defence priming in wheat (Triticum aestivum L.) cultivars infected with Puccinia striiformis f. sp. tritici (stripe rust).

Plant-microbe interactions are a phenomenal display of symbiotic/parasitic relationships between living organisms. Plant growth-promoting rhizobacteria (PGPR) are some of the most widely investigated plant-beneficial microbes due to their capabilities in stimulating plant growth and development and conferring protection to plants against biotic and abiotic stresses. As such, PGPR-mediated plant priming/induced systemic resistance (ISR) has become a hot topic among researchers, particularly with prospects of applications in sustainable agriculture. The current study applies untargeted ultra-high performance liquid chromatography-high-definition mass spectrometry (UHPLC-HDMS) to investigate PGPR-based metabolic reconfigurations in the metabolome of primed wheat plants against Puccinia striiformis f. sp. tricti (Pst). A seed bio-priming approach was adopted, where seeds were coated with two PGPR strains namely Bacillus subtilis and Paenibacillus alvei (T22) and grown under controlled conditions in a glasshouse. The plants were infected with Pst one-week post-germination, followed by weekly harvesting of leaf material. Subsequent metabolite extraction was carried out for analysis on a UHPLC-HDMS system for data acquisition. The data was chemometrically processed to reveal the underlying trends and data structures as well as potential signatory biomarkers for priming against Pst. Results showed notable metabolic reprogramming in primary and secondary metabolism, where the amino acid and organic acid content of primed-control, primed-challenged and non-primed-challenged plants were differentially reprogrammed. Similar trends were observed from the secondary metabolism, in which primed plants (particularly primed-challenged) showed an up-regulation of phenolic compounds (flavonoids, hydroxycinnamic acids-HCAs- and HCA amides) compared to the non-primed plants. The metabolomics-based semi-quantitative and qualitative assessment of the plant metabolomes revealed a time-dependent metabolic reprogramming in primed-challenged and primed-unchallenged plants, indicating the metabolic adaptations of the plants to stripe rust infection over time.

A Taxonomically-informed Mass Spectrometry Search Tool for Microbial Metabolomics Data.

MicrobeMASST, a taxonomically-informed mass spectrometry (MS) search tool, tackles limited microbial metabolite annotation in untargeted metabolomics experiments. Leveraging a curated database of >60,000 microbial monocultures, users can search known and unknown MS/MS spectra and link them to their respective microbial producers via MS/MS fragmentation patterns. Identification of microbial-derived metabolites and relative producers, without a priori knowledge, will vastly enhance the understanding of microorganisms' role in ecology and human health.

Ergosterol-induced sesquiterpenoid synthesis in tobacco cells.

Plants have the ability to continuously respond to microbial signals in their environment. One of these stimuli is a steroid from fungal membranes, ergosterol, which does not occur in plants, but acts as a pathogen-associated molecular pattern molecule to trigger defence mechanisms. Here we investigated the effect of ergosterol on the secondary metabolites in tobacco (Nicotiana tabacum) cells by profiling the induced sesquiterpenoids. Suspensions of tobacco cells were treated with different concentrations (0-1,000 nM) of ergosterol and incubated for different time periods (0-24 h). Metabolites were extracted with a selective dispersive liquid-liquid micro-extraction method. Thin layer chromatography was used as a screening method for identification of sesquiterpenoids in tobacco extracts. Liquid chromatography coupled to mass spectrometry was used for quantitative and qualitative analyses. The results showed that ergosterol triggered differential changes in the metabolome of tobacco cells, leading to variation in the biosynthesis of secondary metabolites. Metabolomic analysis through principal component analysis-scores plots revealed clusters of sample replicates for ergosterol treatments of 0, 50, 150, 300 and 1,000 nM and time-dependent variation at 0, 6, 12, 18 and 24 h. Five bicyclic sesquiterpenoid phytoalexins, capsidiol, lubimin, rishitin, solavetivone and phytuberin, were identified as being ergosterol-induced, contributing to the altered metabolome.

Metabolic profiling of HIV infected individuals on an AZT-based antiretroviral treatment regimen reveals persistent oxidative stress.

Human immunodeficiency viral (HIV) infection and long-term use of combination antiretroviral therapy (cART) have both been associated with the development of metabolic and immunological complications. Despite having many markers for HIV disease progression, the reliability of these markers remains debatable and most of these cannot be used as valid markers for treatment response. As such, it remains important to discover and develop biological markers, which will aid in monitoring disease progression, treatment response and the diagnoses of HIV-related metabolic disorders. Previous HIV-metabonomics studies unravelled the ability to detect and measure potential biological markers of HIV disease progression and treatment response. Several significantly differing metabolites were identified, however, only a small number of studies have investigated the link of specific metabolic disorders to an exact antiretroviral regimen. Here, an NMR-based untargeted metabonomic approach was used to profile metabolic changes in the sera of 24 HIV+ cART+ individuals receiving Zidovudine-based combination antiretroviral therapy compared to their 15 HIV+ ART- and 38 HIV- counterparts. Chemometric analysis identified significant differences in metabolic features related to glutamine, glutamate, glutathione, glucose and arginine. Pathway analysis also revealed the glutamine and glutamate metabolism pathway as the most significantly altered pathway between the HIV+ cART+ and HIV+ cART- group. Findings from this study further confirm the reliability of NMR-based metabonomics in HIV biomarker discovery. In addition, this study contributes to our understandings of the metabolic effect of antiretroviral therapy.