A global approach to analysis and interpretation of metabolic data for plant natural product discovery

Comprehensive review of the repositioning of non-oncologic drugs for cancer immunotherapy

Drug repositioning or repurposing has gained worldwide attention as a plausible way to search for novel molecules for the treatment of particular diseases or disorders. Drug repurposing essentially refers to uncovering approved or failed compounds for use in various diseases. Cancer is a deadly disease and leading cause of mortality. The search for approved non-oncologic drugs for cancer treatment involved in silico modeling, databases, and literature searches. In this review, we provide a concise account of the existing non-oncologic drug molecules and their therapeutic potential in chemotherapy. The mechanisms and modes of action of the repurposed drugs using computational techniques are also highlighted. Furthermore, we discuss potential targets, critical pathways, and highlight in detail the different challenges pertaining to drug repositioning for cancer immunotherapy.

Integrating Targeted Metabolomics and Targeted Proteomics to Study the Responses of Wheat Plants to Engineered Nanomaterials

This manuscript presents a multiomics investigation into the metabolic and proteomic responses of wheat to molybdenum (Mo)- and copper (Cu)-based engineered nanomaterials (ENMs) exposure via root and leaf application methods. Wheat plants underwent a four-week growth period with a 16 h photoperiod (light intensity set at 150 μmol·m-2·s-1), at 22 °C and 60% humidity. Six distinct treatments were applied, including control conditions alongside exposure to Mo- and Cu-based ENMs through both root and leaf routes. The exposure dosage amounted to 6.25 mg of the respective element per plant. An additional treatment with a lower dose (0.6 mg Mo/plant) of Mo ENM exclusively through the root system was introduced upon the detection of phytotoxicity. Utilizing LC-MS/MS analysis, 82 metabolites across various classes and 24 proteins were assessed in different plant tissues (roots, stems, leaves) under diverse treatments. The investigation identified 58 responsive metabolites and 19 responsive proteins for Cu treatments, 71 responsive metabolites, and 24 responsive proteins for Mo treatments, mostly through leaf exposure for Cu and root exposure for Mo. Distinct tissue-specific preferences for metabolite accumulation were revealed, highlighting the prevalence of organic acids and fatty acids in stem or root tissues, while sugars and amino acids were abundant in leaves, mirroring their roles in energy storage and photosynthesis. Joint-pathway analysis was conducted and unveiled 23 perturbed pathways across treatments. Among these, Mo exposure via roots impacted all identified pathways, whereas exposure via leaf affected 15 pathways, underscoring the reliance on exposure route of metabolic and proteomic responses. The coordinated response observed in protein and metabolite concentrations, particularly in amino acids, highlighted a dynamic and interconnected proteomic-to-metabolic-to-proteomic relationship. Furthermore, the contrasting expression patterns observed in glutamate dehydrogenase (upregulation at 1.38 ≤ FC ≤ 1.63 with high Mo dose, and downregulation at 0.13 ≤ FC ≤ 0.54 with low Mo dose) and its consequential impact on glutamine expression (7.67 ≤ FC ≤ 39.60 with high Mo dose and 1.50 ≤ FC ≤ 1.95 with low Mo dose) following Mo root exposure highlighted dose-dependent regulatory trends influencing proteins and metabolites. These findings offer a multidimensional understanding of plant responses to ENMs exposure, guiding agricultural practices and environmental safety protocols while advancing knowledge on nanomaterial impacts on plant biology.

Integrative transcriptomics reveals association of abscisic acid and lignin pathways with cassava whitefly resistance

BACKGROUND

Whiteflies are a global threat to crop yields, including the African subsistence crop cassava (Manihot esculenta). Outbreaks of superabundant whitefly populations throughout Eastern and Central Africa in recent years have dramatically increased the pressures of whitefly feeding and virus transmission on cassava. Whitefly-transmitted viral diseases threaten the food security of hundreds of millions of African farmers, highlighting the need for developing and deploying whitefly-resistant cassava. However, plant resistance to whiteflies remains largely poorly characterized at the genetic and molecular levels. Knowledge of cassava-defense programs also remains incomplete, limiting characterization of whitefly-resistance mechanisms. To better understand the genetic basis of whitefly resistance in cassava, we define the defense hormone- and Aleurotrachelus socialis (whitefly)-responsive transcriptome of whitefly-susceptible (COL2246) and whitefly-resistant (ECU72) cassava using RNA-seq. For broader comparison, hormone-responsive transcriptomes of Arabidopsis thaliana were also generated.

RESULTS

Whitefly infestation, salicylic acid (SA), jasmonic acid (JA), ethylene (ET), and abscisic acid (ABA) transcriptome responses of ECU72 and COL2246 were defined and analyzed. Strikingly, SA responses were largely reciprocal between the two cassava genotypes and we suggest candidate regulators. While susceptibility was associated with SA in COL2246, resistance to whitefly in ECU72 was associated with ABA, with SA-ABA antagonism observed. This was evidenced by expression of genes within the SA and ABA pathways and hormone levels during A. socialis infestation. Gene-enrichment analyses of whitefly- and hormone-responsive genes suggest the importance of fast-acting cell wall defenses (e.g., elicitor recognition, lignin biosynthesis) during early infestation stages in whitefly-resistant ECU72. A surge of ineffective immune and SA responses characterized the whitefly-susceptible COL2246's response to late-stage nymphs. Lastly, in comparison with the model plant Arabidopsis, cassava's hormone-responsive genes showed striking divergence in expression.

CONCLUSIONS

This study provides the first characterization of cassava's global transcriptome responses to whitefly infestation and defense hormone treatment. Our analyses of ECU72 and COL2246 uncovered possible whitefly resistance/susceptibility mechanisms in cassava. Comparative analysis of cassava and Arabidopsis demonstrated that defense programs in Arabidopsis may not always mirror those in crop species. More broadly, our hormone-responsive transcriptomes will also provide a baseline for the cassava community to better understand global responses to other yield-limiting pests/pathogens.

Primary Metabolite Screening Shows Significant Differences between Embryogenic and Non-Embryogenic Callus of Tamarillo (Solanum betaceum Cav.)

Tamarillo is a solanaceous tree that has been extensively studied in terms of in vitro clonal propagation, namely somatic embryogenesis. In this work, a protocol of indirect somatic embryogenesis was applied to obtain embryogenic and non-embryogenic callus from leaf segments. Nuclear magnetic resonance spectroscopy was used to analyze the primary metabolome of these distinct calli to elucidate possible differentiation mechanisms from the common genetic background callus. Standard multivariate analysis methods were then applied, and were complemented by univariate statistical methods to identify differentially expressed primary metabolites and related metabolic pathways. The results showed carbohydrate and lipid metabolism to be the most relevant in all the calli assayed, with most discriminant metabolites being fructose, glucose and to a lesser extent choline. The glycolytic rate was higher in embryogenic calli, which shows, overall, a higher rate of sugar catabolism and a different profile of phospholipids with a choline/ethanolamine analysis. In general, our results show that a distinct primary metabolome between embryogenic and non-embryogenic calli occurs and that intracellular levels of fructose and sucrose and the glucose to sucrose ratio seem to be good candidates as biochemical biomarkers of embryogenic competence.

NMR-Based Metabolomics to Analyze the Effects of a Series of Monoamine Oxidases-B Inhibitors on U251 Cells

Alzheimer’s disease (AD) is a typical progressive neurodegenerative disorder, and with multiple possible pathogenesis. Among them, coumarin derivatives could be used as potential drugs as monoamine oxidase-B (MAO-B) inhibitors. Our lab has designed and synthesized coumarin derivatives based on MAO-B. In this study, we used nuclear magnetic resonance (NMR)-based metabolomics to accelerate the pharmacodynamic evaluation of candidate drugs for coumarin derivative research and development. We detailed alterations in the metabolic profiles of nerve cells with various coumarin derivatives. In total, we identified 58 metabolites and calculated their relative concentrations in U251 cells. In the meantime, the outcomes of multivariate statistical analysis showed that when twelve coumarin compounds were treated with U251cells, the metabolic phenotypes were distinct. In the treatment of different coumarin derivatives, there several metabolic pathways changed, including aminoacyl-tRNA biosynthesis, D-glutamine and D-glutamate metabolism, glycine, serine and threonine metabolism, taurine and hypotaurine metabolism, arginine biosynthesis, alanine, aspartate and glutamate metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, glutathione metabolism and valine, leucine and isoleucine biosynthesis. Our work documented how our coumarin derivatives affected the metabolic phenotype of nerve cells in vitro. We believe that these NMR-based metabolomics might accelerate the process of drug research in vitro and in vivo.

Identification of key genes involved in secondary metabolite biosynthesis in Digitalis purpurea

The medicinal plant Digitalis purpurea produces cardiac glycosides that are useful in the pharmaceutical industry. These bioactive compounds are in high demand due to ethnobotany's application to therapeutic procedures. Recent studies have investigated the role of integrative analysis of multi-omics data in understanding cellular metabolic status through systems metabolic engineering approach, as well as its application to genetically engineering metabolic pathways. In spite of numerous omics experiments, most molecular mechanisms involved in metabolic pathways biosynthesis in D. purpurea remain unclear. Using R Package Weighted Gene Co-expression Network Analysis, co-expression analysis was performed on the transcriptome and metabolome data. As a result of our study, we identified transcription factors, transcriptional regulators, protein kinases, transporters, non-coding RNAs, and hub genes that are involved in the production of secondary metabolites. Since jasmonates are involved in the biosynthesis of cardiac glycosides, the candidate genes for Scarecrow-Like Protein 14 (SCL14), Delta24-sterol reductase (DWF1), HYDRA1 (HYD1), and Jasmonate-ZIM domain3 (JAZ3) were validated under methyl jasmonate treatment (MeJA, 100 μM). Despite early induction of JAZ3, which affected downstream genes, it was dramatically suppressed after 48 hours. SCL14, which targets DWF1, and HYD1, which induces cholesterol and cardiac glycoside biosynthesis, were both promoted. The correlation between key genes and main metabolites and validation of expression patterns provide a unique insight into the biosynthesis mechanisms of cardiac glycosides in D. purpurea.

Metabolic profiling on the analysis of different parts of Schisandra chinensis based on UPLC-QTOF-MS with comparative bioactivity assays

The Schisandra chinensis is an important edible plant, and previous phytochemical research focused on the S. chinensis fruit (SF) due to its long history as traditional Chinese medicine. Schisandra chinensis fruit was used as an astringent tonic to astringe the lungs and the kidneys, replenish energy, promote the production of body fluids, tonify the kidney, and induce sedation. The components of S. chinensis, such as its stems (SS), leaves (SL), and roots (SR), have drawn little attention regarding their metabolites and bioactivities. In this study, a strategy of combining a chemical database with the Progenesis QI informatics platform was applied to characterize the metabolites. A total of 332 compounds were tentatively identified, including lignans, triterpenoids, flavonoids, tannins, and other compound classes. Heatmap and principal component analysis (PCA) showed remarkable differences in different parts of the plants. By multiple orthogonal partial least-squares discriminant analyses (OPLS-DA), 76 compounds were identified as potential marker compounds that differentiate these different plant parts. Based on the variable influence on the projection score from OPLS-DA, the active substances including gomisin D, schisandrol B, schisantherin C, kadsuranin, and kadlongilactone F supported the fact that the biological activity of the roots was higher than that of the fruit. These substances can be used as marker compounds in the plant roots, which likely contribute to their antioxidant and anti-inflammatory activities. The plant roots could be a new medicinal source that exhibits better activity than that of traditional medicinal parts, which makes them worth exploring.

Identification of Unknown Substances in Ambient Air (PM10), Profiles and Differences between Rural, Urban and Industrial Areas

A fast and automated strategy has been developed for identifying unknown substances in the atmosphere (concretely, in the particulate matter, PM₁₀) using LC-HRMS (MS³). A total of 15 samples were collected in three different areas (rural, urban and industrial). A sampling flow rate of 30 m³ h⁻¹ was applied for 24 h, sampling a total volume of around 720 m³. A total of 49 compounds were tentatively identified using very restrictive criteria regarding exact mass, retention time, isotopic profile and both MS² and MS³ spectra. Pesticides, pharmaceutical active compounds, drugs, plasticizers and metabolites were the most identified compounds. To verify whether the developed methodology was suitable, 11 substances were checked with their analytical standards and all of them were confirmed. Different profiles for industrial, rural and urban areas were examined. The Principal Component Analysis (PCA) model allowed us to separate the obtained data of the three assessed area. When the profiles obtained in the three evaluated areas were compared using a Volcano plot (the rural area was taken as reference), 11 compounds were confirmed as being discriminant: three of them (3-hydroxy-2-methylpyridine, 3-methyladenine and nicotine) were more likely to be found in industrial sites; ten compounds (3-hydroxy-2-methylpyridine, 3-methyladenine, azoxystrobin, cocaine, cotinine, ethoprophos, imidacloprid, metalaxyl-M, nicotine and pyrimethanil) were more probable in the case of urban sites; finally, triisopropanolamine was more likely to be detected in rural locations.

HBM4EU Chromates Study: Urinary Metabolomics Study of Workers Exposed to Hexavalent Chromium

Exposure to hexavalent chromium Cr(VI) may occur in several occupational activities, placing workers in many industries at risk for potential related health outcomes. Untargeted metabolomics was applied to investigate changes in metabolic pathways in response to Cr(VI) exposure. We obtained our data from a study population of 220 male workers with exposure to Cr(VI) and 102 male controls from Belgium, Finland, Poland, Portugal and the Netherlands within the HBM4EU Chromates Study. Urinary metabolite profiles were determined using liquid chromatography mass spectrometry, and differences between post-shift exposed workers and controls were analyzed using principal component analysis. Based on the first two principal components, we observed clustering by industrial chromate application, such as welding, chrome plating, and surface treatment, distinct from controls and not explained by smoking status or alcohol use. The changes in the abundancy of excreted metabolites observed in workers reflect fatty acid and monoamine neurotransmitter metabolism, oxidative modifications of amino acid residues, the excessive formation of abnormal amino acid metabolites and changes in steroid and thyrotropin-releasing hormones. The observed responses could also have resulted from work-related factors other than Cr(VI). Further targeted metabolomics studies are needed to better understand the observed modifications and further explore the suitability of urinary metabolites as early indicators of adverse effects associated with exposure to Cr(VI).

Leveraging a graft collection to develop metabolome-based trait prediction for the selection of tomato rootstocks with enhanced salt tolerance

Grafting has been demonstrated to significantly enhance the salt tolerance of crops. However, breeding efforts to develop enhanced graft combinations are hindered by knowledge-gaps as to how rootstocks mediate scion-response to salt stress. We grafted the scion of cultivated M82 onto rootstocks of 254 tomato accessions and explored the morphological and metabolic responses of grafts under saline conditions (EC = 20 dS m^-1) as compared to self-grafted M82 (SG-M82). Correlation analysis and Least Absolute Shrinkage and Selection Operator were performed to address the association between morphological diversification and metabolic perturbation. We demonstrate that grafting the same variety onto different rootstocks resulted in scion phenotypic heterogeneity and emphasized the productivity efficiency of M82 irrespective of the rootstock. Spectrophotometric analysis to test lipid oxidation showed largest variability of malondialdehyde (MDA) equivalents across the population, while the least responsive trait was the ratio of fruit fresh weight to total fresh weight (FFW/TFW). Generally, grafts showed greater values for the traits measured than SG-M82, except for branch number and wild race-originated rootstocks; the latter were associated with smaller scion growth parameters. Highly responsive and correlated metabolites were identified across the graft collection including malate, citrate, and aspartate, and their variance was partly related to rootstock origin. A group of six metabolites that consistently characterized exceptional graft response was observed, consisting of sorbose, galactose, sucrose, fructose, myo-inositol, and proline. The correlation analysis and predictive modelling, integrating phenotype- and leaf metabolite data, suggest a potential predictive relation between a set of leaf metabolites and yield-related traits.

Comoclathrin, a novel potent skin-whitening agent produced by endophytic Comoclathris strains associated with Andalusia desert plants

As part of our screening program for the discovery of molecules of microbial origin with skin-whitening activity, 142 diverse fungal endophytes from a wide variety of Andalusia arid plants were screened, applying the OSMAC approach. The fungal strains CF-090361 and CF-090766, isolated from xerophytic plants, were selected as the most promising, while phylogenetic analysis revealed that both strains could represent a new species within the genus Comoclathris. The effect of different fermentation conditions on the production of tyrosinase inhibitory activity was examined, in order to identify the optimum cultivation conditions. LCMS based metabolomics was applied to determine significant differences between the strains and fermentation conditions, and to identify potential bioactive secondary metabolites. Bioassay-guided purification of the main active components led to the isolation of three new compounds (1-3), along with the known compounds graphostrin B (4) and brevianamide M (5). Compound 1 (Comoclathrin) demonstrated the strongest anti-tyrosinase activity (IC50 0.16 μΜ), which was 90-times higher than kojic acid (IC50 14.07 μΜ) used as positive control. Additionally, comoclathrin showed no significant cytotoxicity against a panel of cancer cell lines (HepG2, A2058, A549, MCF-7 and MIA PaCa-2) and normal BJ fibroblasts. These properties render comoclathrin an excellent development candidate as whitening agent.

Discovering and harnessing oxidative enzymes for chemoenzymatic synthesis and diversification of anticancer camptothecin analogues

Semi-synthetic derivatives of camptothecin, a quinoline alkaloid found in the Camptotheca acuminata tree, are potent anticancer agents. Here we discovered two C. acuminata cytochrome P450 monooxygenases that catalyze regio-specific 10- and 11-oxidations of camptothecin, and demonstrated combinatorial chemoenzymatic C-H functionalizations of the camptothecin scaffold using the new enzymes to produce a suite of anticancer drugs, including topotecan (Hycamtin®) and irinotecan (Camptosar®). This work sheds new light into camptothecin metabolism, and represents greener approaches for accessing clinically relevant camptothecin derivatives.

Non-targeted LC-MS and CE-MS for biomarker discovery in bioreactors: Influence of separation, mass spectrometry and data processing tools

Liquid separation coupled to mass spectrometry is often used for non-targeted analyses in various fields, such as metabolomics. However, the combination of non-standardized methods, various mass spectrometers (MS) and processing tools for data evaluation affect biomarker discovery potentially. Here, we present a comprehensive study of these factors based on non-targeted liquid chromatography coupled to time-of-flight (TOF) and Orbitrap MS and capillary zone electrophoresis to Orbitrap analyses of the same bioreactor samples, describing the correlation of its gas yield with changing feature signal intensity. The three datasets were processed with MZmine 2 and XCMS online and subsequential Partial Least Square Regression (PLSR) with Variable Importance in Projection (VIP) ranking for feature prioritization. The six feature tables were compared to evaluate their overlap of shared features and the influence of the processing software and MS instrument on the VIP values and fold changes. The overlaps, defined as a fraction of one feature table found in the comparative table, were from 27% to 57% for the comparison of MZmine and XCMS and from 15% to 50% between Orbitrap and TOF data sets, respectively. Considering the most relevant features only (VIP >1.5), the overlaps were increased significantly in all cases from 26% to 95%. For the same data set, both VIP values and fold changes were well correlated, however, varied significantly between TOF and Orbitrap. CE-MS showed higher total feature numbers compared to LC-MS, most likely due to its more appropriate selectivity, different sample preparation, and/or the sensitive nano-ESI interface. Since only less than 10% of MS/MS data overlapped, CE-MS provided complementary information to LC-MS. Overall, our systematic study proves the benefits of using different separation techniques and processing tools but also indicates a significant influence of mass spectrometry on comprehensive biomarker discovery.

NMR-Based Metabolomics Analysis Predicts Response to Neoadjuvant Chemotherapy for Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most fatal type of breast cancer (BC). Due to the lack of relevant targeted drug therapy, in addition to surgery, chemotherapy is still the most common treatment option for TNBC. TNBC is heterogeneous, and different patients have an unusual sensitivity to chemotherapy. Only part of the patients will benefit from chemotherapy, so neoadjuvant chemotherapy (NAC) is controversial in the treatment of TNBC. Here, we performed an NMR spectroscopy–based metabolomics study to analyze the relationship between the patients’ metabolic phenotypes and chemotherapy sensitivity in the serum samples. Metabolic phenotypes from patients with pathological partial response, pathological complete response, and pathological stable disease (pPR, pCR, and pSD) could be distinguished. Furthermore, we conducted metabolic pathway analysis based on identified significant metabolites and revealed significantly disturbed metabolic pathways closely associated with three groups of TNBC patients. We evaluated the discriminative ability of metabolites related to significantly disturbed metabolic pathways by using the multi-receiver–operating characteristic (ROC) curve analysis. Three significantly disturbed metabolic pathways of glycine, serine, and threonine metabolism, valine, leucine, and isoleucine biosynthesis, and alanine, aspartate, and glutamate metabolism could be used as potential predictive models to distinguish three types of TNBC patients. These results indicate that a metabolic phenotype could be used to predict whether a patient is suitable for NAC. Metabolomics research could provide data in support of metabolic phenotypes for personalized treatment of TNBC.

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.

Systematic investigation of stereochemistry, stereoselective bioactivity, and antifungal mechanism of chiral triazole fungicide metconazole

In this study, the stereochemistry, stereoselective fungicidal bioactivity, and antifungal mechanism of chiral triazole fungicide metconazole were investigated. The configurations of metconazole stereoisomers were determined to be (1R, 5R)-metconazole, (1R, 5S)-metconazole, (1S, 5S)-metconazole, and (1S, 5R)-metconazole through using electronic circular dichroism spectroscopy. The bioactivities of four stereoisomers and their stereoisomer mixture toward Fusarium graminearum Schw and Alternaria triticina were found to be in the following order: (1S, 5R)-metconazole > the stereoisomer mixture > (1S, 5S)-metconazole > (1R, 5R)-metconazole > (1R, 5S)-metconazole. In addition, the fungicidal activities of (1S, 5R)-metconazole against two tested pathogens was 13.9-23.4 times higher than those of (1R, 5S)-metconazole. Molecular docking methodology was applied to characterize the docking energy and distances between Cytochrome P450 CYP51B and the metconazole stereoisomers, and (1S, 5R)-metconazole showed the strongest binding energy and the shortest distance binding to CYP51B than the other three stereoisomers. Moreover, enantioselective metabolisms of (1S, 5R)-metconazole and (1R, 5S)-metconazole by Fusarium graminearum Schw were investigated through NMR-based metabolomics. The amounts of alanine, arginine, acetate, ethanol, and dimethylamine produced in the presence of (1R, 5S)-metconazole were significantly higher than corresponding amounts in the presence of (1S, 5R)-metconazole, whereas the amounts of glucose, glycerol, glutamate, methionine, and trimethylamine formed in the presence of (1R, 5S)-metconazole were much less than those in the presence of (1S, 5R)-metconazole. This systematic investigation of metconazole stereoisomers would provide a new perception of metconazole in stereoisomeric level, including bioactivities, metabolic behaviors and antifungal mechanism.

LC-MS/MS-based profiling of bioactive metabolites of endophytic bacteria from Cannabis sativa and their anti-Phytophthora activity

Protection of crop plants from phytopathogens through endophytic bacteria is a newly emerged area of biocontrol. In this study, endophytic bacteria were isolated from the rhizosphere of Cannabis sativa. Based on initial antimicrobial screening, three (03) bacteria Serratia marcescens MOSEL-w2, Enterobacter cloacae MOSEL-w7, and Paenibacillus MOSEL-w13 were selected. Antimicrobial assays of these selected bacteria against Phytophthora parasitica revealed that E. cloacae MOSEL-w7 and Paenibacillus sp. MOSEL-w13 possessed strong activity against P. parasitica. All these bacterial extracts showed strong inhibition against P. parasitica at different concentrations (4-400 µg mL^-1). P. parasitica hyphae treated with ethyl acetate extract of E. cloacae MOSEL-w7 resulted in severe growth abnormalities compared to control. The extracts were further evaluated for in vivo detached-leaf assay against P. parasitica on the wild type tobacco. Application of 1% ethyl acetate bacterial extract of S. marcescens MOSEL-w2, E. cloacae MOSEL-w7, and Paenibacillus sp. MOSEL-w13 reduced P. parasitica induced lesion sizes and lesion frequencies by 60-80%. HPLC based fractions of each extract also showed bioactivity against P. parasitica. A total of 24 compounds were found in the S. marcescens MOSEL-w2, 15 compounds in E. cloacae MOSEL-w7 and 20 compounds found in Paenibacillus sp. MOSEL-w13. LC-MS/MS analyses showed different bioactive compounds in the bacterial extracts such as Cotinine (alkylpyrrolidine), L-tryptophan, L-lysine, L-Dopa, and L-ornithine. These results suggest that S. marcescens MOSEL-w2, E. cloacae MOSEL-w7, and Paenibacillus MOSEL-w13 are a source of bioactive metabolites and could be used in combination with other biocontrol agents, with other modes of action for controlling diseases caused by Phytophthora in crops. They could be a clue for the broad-spectrum biopesticides for agriculturally significant crops.

Identification of key genes associated with secondary metabolites biosynthesis by system network analysis in Valeriana officinalis

Valeriana officinalis is a medicinal plant, a source of bioactive chemical compounds and secondary metabolites which are applied in pharmaceutical industries. The advent of ethnomedicine has provided alternatives for disease treatment and has increased demands for natural products and bioactive compounds. A set of preliminary steps to answers for such demands can include integrative omics for systems metabolic engineering, as an approach that contributes to the understanding of cellular metabolic status. There is a growing trend of this approach for genetically engineering metabolic pathways in plant systems, by which natural and synthetic compounds can be produced. As in the case of most medicinal plants, there are no sufficient information about molecular mechanisms involved in the regulation of metabolic pathways in V. officinalis. In this research, systems biology was performed on the RNA-seq transcriptome and metabolome data to find key genes that contribute to the synthesis of major secondary metabolites in V. officinalis. The R Package Weighted Gene Co-Expression Network Analysis (WGCNA) was employed to analyze the data. Based on the results, some major modules and hub genes were identified to be associated with the valuable secondary metabolites. In addition, some TF-encoding genes, including AP2/ERF-ERF, WRKY and NAC TF families, as well as some regulatory factors including protein kinases and transporters were identified. The results showed that several novel hub genes, such as PCMP-H24, RPS24B, ANX1 and PXL1, may play crucial roles in metabolic pathways. The current findings provide an overall insight into the metabolic pathways of V. officinalis and can expand the potential for engineering genome-scale pathways and systems metabolic engineering to increase the production of bioactive compounds by plants.

CMAUP: a database of collective molecular activities of useful plants

The beneficial effects of functionally useful plants (e.g. medicinal and food plants) arise from the multi-target activities of multiple ingredients of these plants. The knowledge of the collective molecular activities of these plants facilitates mechanistic studies and expanded applications. A number of databases provide information about the effects and targets of various plants and ingredients. More comprehensive information is needed for broader classes of plants and for the landscapes of individual plant’s multiple targets, collective activities and regulated biological pathways, processes and diseases. We therefore developed a new database, Collective Molecular Activities of Useful Plants (CMAUP), to provide the collective landscapes of multiple targets (ChEMBL target classes) and activity levels (in 2D target-ingredient heatmap), and regulated gene ontologies (GO categories), biological pathways (KEGG categories) and diseases (ICD blocks) for 5645 plants (2567 medicinal, 170 food, 1567 edible, 3 agricultural and 119 garden plants) collected from or traditionally used in 153 countries and regions. These landscapes were derived from 47 645 plant ingredients active against 646 targets in 234 KEGG pathways associated with 2473 gene ontologies and 656 diseases. CMAUP (http://bidd2.nus.edu.sg/CMAUP/) is freely accessible and searchable by keywords, plant usage classes, species families, targets, KEGG pathways, gene ontologies, diseases (ICD code) and geographical locations.

MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets

The diverse and growing omics data in public domains provide researchers with tremendous opportunity to extract hidden, yet undiscovered, knowledge. However, the vast majority of archived data remain unused. Here, we present MetaOmGraph (MOG), a free, open-source, standalone software for exploratory analysis of massive datasets. Researchers, without coding, can interactively visualize and evaluate data in the context of its metadata, honing-in on groups of samples or genes based on attributes such as expression values, statistical associations, metadata terms and ontology annotations. Interaction with data is easy via interactive visualizations such as line charts, box plots, scatter plots, histograms and volcano plots. Statistical analyses include co-expression analysis, differential expression analysis and differential correlation analysis, with significance tests. Researchers can send data subsets to R for additional analyses. Multithreading and indexing enable efficient big data analysis. A researcher can create new MOG projects from any numerical data; or explore an existing MOG project. MOG projects, with history of explorations, can be saved and shared. We illustrate MOG by case studies of large curated datasets from human cancer RNA-Seq, where we identify novel putative biomarker genes in different tumors, and microarray and metabolomics data from Arabidopsis thaliana. MOG executable and code: http://metnetweb.gdcb.iastate.edu/ and https://github.com/urmi-21/MetaOmGraph/.

BioHackathon 2015: Semantics of data for life sciences and reproducible research

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Genomic Survey, Transcriptome, and Metabolome Analysis of Apocynum venetum and Apocynum hendersonii to Reveal Major Flavonoid Biosynthesis Pathways

Apocynum plants, especially A. venetum and A. hendersonii, are rich in flavonoids. In the present study, a whole genome survey of the two species was initially carried out to optimize the flavonoid biosynthesis-correlated gene mining. Then, the metabolome and transcriptome analyses were combined to elucidate the flavonoid biosynthesis pathways. Both species have small genome sizes of 232.80 Mb (A. venetum) and 233.74 Mb (A. hendersonii) and showed similar metabolite profiles with flavonols being the main differentiated flavonoids between the two specie. Positive correlation of gene expression levels (flavonone-3 hydroxylase, anthocyanidin reductase, and flavonoid 3-O-glucosyltransferase) and total flavonoid content were observed. The contents of quercitrin, hyperoside, and total anthocyanin in A. venetum were found to be much higher than in A. hendersonii, and such was thought to be the reason for the morphological difference in color of A. venetum and A. hendersonii. This study provides valuable genomic and metabolome information for understanding of A. venetum and A. hendersonii, and lays a foundation for elucidating Apocynum genus plant flavonoid biosynthesis.

Computational chemoproteomics to understand the role of selected psychoactives in treating mental health indications

We have developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform to infer homology of drug behaviour at a proteomic level by constructing and analysing structural compound-proteome interaction signatures of 3,733 compounds with 48,278 proteins in a shotgun manner. We applied the CANDO platform to predict putative therapeutic properties of 428 psychoactive compounds that belong to the phenylethylamine, tryptamine, and cannabinoid chemical classes for treating mental health indications. Our findings indicate that these 428 psychoactives are among the top-ranked predictions for a significant fraction of mental health indications, demonstrating a significant preference for treating such indications over non-mental health indications, relative to randomized controls. Also, we analysed the use of specific tryptamines for the treatment of sleeping disorders, bupropion for substance abuse disorders, and cannabinoids for epilepsy. Our innovative use of the CANDO platform may guide the identification and development of novel therapies for mental health indications and provide an understanding of their causal basis on a detailed mechanistic level. These predictions can be used to provide new leads for preclinical drug development for mental health and other neurological disorders.

Deciphering the metabolic perturbation in hepatic alveolar echinococcosis: a 1H NMR-based metabolomics study

BACKGROUND

Hepatic alveolar echinococcosis (HAE) is caused by the growth of Echinococcus multilocularis larvae in the liver. It is a chronic and potentially lethal parasitic disease. Early stage diagnosis for this disease is currently not available due to its long asymptomatic incubation period. In this study, a proton nuclear magnetic resonance (1H NMR)-based metabolomics approach was applied in conjunction with multivariate statistical analysis to investigate the altered metabolic profiles in blood serum and urine samples obtained from HAE patients. The aim of the study was to identify the metabolic signatures associated with HAE.

RESULTS

A total of 21 distinct metabolic differences between HAE patients and healthy individuals were identified, and they are associated with perturbations in amino acid metabolism, energy metabolism, glyoxylate and dicarboxylate metabolism. Furthermore, the present results showed that the Fischer ratio, which is the molar ratio of branched-chain amino acids to aromatic amino acids, was significantly lower (P < 0.001) in the blood serum obtained from the HAE patients than it was in the healthy patient group.

CONCLUSIONS

The altered Fischer ratio, together with perturbations in metabolic pathways identified in the present study, may provide new insights into the mechanistic understanding of HAE pathogenesis and potential therapeutic interventions.

Comparison of the Plasma Metabolome Profiles Between the Internal Thoracic Artery and Ascending Aorta in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Gas Chromatography Time-of-Flight Mass Spectrometry

BACKGROUND

The left internal thoracic artery (LITA) has been used as the first conduit of choice in coronary artery bypass grafting (CABG) because of excellent long-term patency and outcomes. However, no studies have examined substances other than nitric oxide that could be beneficial for the bypass conduit, native coronary artery or ischemic myocardium. This study was conducted to evaluate differences in metabolic profiles between the LITA and ascending aorta using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS).

METHODS

Twenty patients who underwent CABG using the LITA were prospectively enrolled. Plasma samples were collected simultaneously from the LITA and ascending aorta. GC-TOF-MS based untargeted metabolomic analyses were performed and a 2-step volcano plot analysis was used to identify distinguishable markers from two plasma metabolome profiles. Semi-quantitative and quantitative analyses were performed using GC-TOF-MS and enzyme-linked immunosorbent assay, respectively, after selecting target metabolites based on the metabolite set enrichment analysis.

RESULTS

Initial volcano plot analysis demonstrated 5 possible markers among 851 peaks detected. The final analysis demonstrated that the L-cysteine peak was significantly higher in the LITA than in the ascending aorta (fold change = 1.86). The concentrations of intermediate metabolites such as L-cysteine, L-methionine and L-cystine in the 'cysteine and methionine metabolism pathway' were significantly higher in the LITA than in the ascending aorta (2.0-, 1.4- and 1.2-fold, respectively). Quantitative analysis showed that the concentration of hydrogen sulfide (H₂S) was significantly higher in the LITA.

CONCLUSION

The plasma metabolome profiles of the LITA and ascending aorta were different, particularly higher plasma concentrations of L-cysteine and H₂S in the LITA.

LC-HRMS-Database Screening Metrics for Rapid Prioritization of Samples to Accelerate the Discovery of Structurally New Natural Products

In order to accelerate the isolation and characterization of structurally new or novel secondary metabolites, it is crucial to develop efficient strategies that prioritize samples with greatest promise early in the workflow so that resources can be utilized in a more efficient and cost-effective manner. We have developed a metrics-based prioritization approach using exact LC-HRMS, which uses data for 24 618 marine natural products held in the PharmaSea database. Each sample was evaluated and allocated a metric score by a software algorithm based on the ratio of new masses over the total (sample novelty), ratio of known masses over the total (chemical novelty), number of peaks above a defined peak area threshold (sample complexity), and peak area (sample diversity). Samples were then ranked and prioritized based on these metric scores. To validate the approach, eight marine sponges and six tunicate samples collected from the Fiji Islands were analyzed, metric scores calculated, and samples targeted for isolation and characterization of new compounds. Structures of new compounds were elucidated by spectroscopic techniques, including 1D and 2D NMR, MS, and MS/MS. Structures were confirmed by computer-assisted structure elucidation methods (CASE) using the ACD/Structure Elucidator Suite.

Detection of Volatile Organic Compounds (VOCs) in Urine via Gas Chromatography-Mass Spectrometry QTOF to Differentiate Between Localized and Metastatic Models of Breast Cancer

Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).

PgUGT95B2 preferentially metabolizes flavones/flavonols and has evolved independently from flavone/flavonol UGTs identified in Arabidopsis thaliana

UDP-dependent glycosyltransferases (UGTs) convert aglycones into more stable, bioactive, and structurally diverse glycosylated derivatives. Pomegranate (Punica granatum L.) produces various glycosylated phenolic metabolites, e.g. hydrolyzable tannins (HTs), anthocyanins, and flavonoids, and constitutes an excellent system for investigating the corresponding UGT activities. Here we report the cloning and functional characterization of a pomegranate UGT, PgUGT95B2, which is highly active towards flavones and flavonols and can glycosylate at more than one position in the substrate molecule. Particularly, PgUGT95B2 has the strongest activity towards tricetin (flavone with a tri-hydroxylated B-ring) and can act at the 4'-O position of its B-ring. In addition, PgUGT95B2 was able to glycosylate flavones present in pomegranate metabolite extracts. Conversely, PgUGT95B2 did not produce a galloylglucose ester (precursor for HT biosynthesis) or anthocyanins in enzyme assays. Our phylogenetic analysis suggested an independent evolution of PgUGT95B2 and flavone/flavonol UGTs identified in the model plant Arabidopsis thaliana through convergent evolution or gene loss.

Arabidopsis bioinformatics resources: The current state, challenges, and priorities for the future

Effective research, education, and outreach efforts by the Arabidopsis thaliana community, as well as other scientific communities that depend on Arabidopsis resources, depend vitally on easily available and publicly-shared resources. These resources include reference genome sequence data and an ever-increasing number of diverse data sets and data types. TAIR (The Arabidopsis Information Resource) and Araport (originally named the Arabidopsis Information Portal) are community informatics resources that provide tools, data, and applications to the more than 30,000 researchers worldwide that use in their work either Arabidopsis as a primary system of study or data derived from Arabidopsis. Four years after Araport's establishment, the IAIC held another workshop to evaluate the current status of Arabidopsis Informatics and chart a course for future research and development. The workshop focused on several challenges, including the need for reliable and current annotation, community-defined common standards for data and metadata, and accessible and user-friendly repositories/tools/methods for data integration and visualization. Solutions envisioned included (a) a centralized annotation authority to coalesce annotation from new groups, establish a consistent naming scheme, distribute this format regularly and frequently, and encourage and enforce its adoption. (b) Standards for data and metadata formats, which are essential, but challenging when comparing across diverse genotypes and in areas with less-established standards (e.g., phenomics, metabolomics). Community-established guidelines need to be developed. (c) A searchable, central repository for analysis and visualization tools. Improved versioning and user access would make tools more accessible. Workshop participants proposed a "one-stop shop" website, an Arabidopsis "Super-Portal" to link tools, data resources, programmatic standards, and best practice descriptions for each data type. This must have community buy-in and participation in its establishment and development to encourage adoption.

Systems biology of seeds: decoding the secret of biochemical seed factories for nutritional security

Seeds serve as biochemical factories of nutrition, processing, bio-energy and storage related important bio-molecules and act as a delivery system to transmit the genetic information to the next generation. The research pertaining towards delineating the complex system of regulation of genes and pathways related to seed biology and nutrient partitioning is still under infancy. To understand these, it is important to know the genes and pathway(s) involved in the homeostasis of bio-molecules. In recent past with the advent and advancement of modern tools of genomics and genetic engineering, multi-layered 'omics' approaches and high-throughput platforms are being used to discern the genes and proteins involved in various metabolic, and signaling pathways and their regulations for understanding the molecular genetics of biosynthesis and homeostasis of bio-molecules. This can be possible by exploring systems biology approaches via the integration of omics data for understanding the intricacy of seed development and nutrient partitioning. These information can be exploited for the improvement of biologically important chemicals for large-scale production of nutrients and nutraceuticals through pathway engineering and biotechnology. This review article thus describes different omics tools and other branches that are merged to build the most attractive area of research towards establishing the seeds as biochemical factories for human health and nutrition.

Light-Dependent Changes in the Spatial Localization of Metabolites in Solenostemon scutellarioides (Coleus Henna) Visualized by Matrix-Free Atmospheric Pressure Electrospray Laser Desorption Ionization Mass Spectrometry Imaging

The visualization of foliage color in plants provides immediate insight into some of the compounds that exist in the leaf. However, many non-colored compounds are also present; their cellular distributions are not readily identifiable optically. In this study we evaluate the applicability of mass spectrometry imaging (MSI) via electrospray laser desorption ionization (ELDI) to reveal the spatial distribution of metabolites. ELDI-MSI is a matrix free, atmospheric pressure ionization method that utilizes a UV laser coupled with supplemental ionization by electrospray. We specifically applied ELDI-MSI to determine the spatial distribution of metabolites in Coleus Henna half leaves that were grown with half-sections either fully illuminated or shaded. We monitored dynamic changes in the spatial distribution of metabolites in response to the change of illumination every 7 days for a 28 day period. A novel source-sink relationship was observed between the 2 halves of the experimental leaf. Furthermore, Coleus Henna leaves present visually recognizable sectors associated with the differential accumulation of flavonoids. Thus, we correlated the effect of differential illumination and presence or absence of flavonoids with metabolic changes revealed by the accumulation of carbohydrates, amino acids, and organic acids. The results show the potential of ELDI-MSI to provide spatial information for a variety of plant metabolites with little sample preparation.

HEAT INDUCIBLE LIPASE1 Remodels Chloroplastic Monogalactosyldiacylglycerol by Liberating α-Linolenic Acid in Arabidopsis Leaves under Heat Stress

Under heat stress, polyunsaturated acyl groups, such as α-linolenate (18:3) and hexadecatrienoate (16:3), are removed from chloroplastic glycerolipids in various plant species. Here, we showed that a lipase designated HEAT INDUCIBLE LIPASE1 (HIL1) induces the catabolism of monogalactosyldiacylglycerol (MGDG) under heat stress in Arabidopsis thaliana leaves. Using thermotolerance tests, a T-DNA insertion mutant with disrupted HIL1 was shown to have a heat stress-sensitive phenotype. Lipidomic analysis indicated that the decrease of 34:6-MGDG under heat stress was partially impaired in the hil1 mutant. Concomitantly, the heat-induced increment of 54:9-triacylglycerol in the hil1 mutant was 18% lower than that in the wild-type plants. Recombinant HIL1 protein digested MGDG to produce 18:3-free fatty acid (18:3-FFA), but not 18:0- and 16:0-FFAs. A transient assay using fluorescent fusion proteins confirmed chloroplastic localization of HIL1. Transcriptome coexpression network analysis using public databases demonstrated that the HIL1 homolog expression levels in various terrestrial plants are tightly associated with chloroplastic heat stress responses. Thus, HIL1 encodes a chloroplastic MGDG lipase that releases 18:3-FFA in the first committed step of 34:6 (18:3/16:3)-containing galactolipid turnover, suggesting that HIL1 has an important role in the lipid remodeling process induced by heat stress in plants.

Beyond the one-way ANOVA for 'omics data

BACKGROUND

With ever increasing accessibility to high throughput technologies, more complex treatment structures can be assessed in a variety of 'omics applications. This adds an extra layer of complexity to the analysis and interpretation, in particular when inferential univariate methods are applied en masse. It is well-known that mass univariate testing suffers from multiplicity issues and although this has been well documented for simple comparative tests, few approaches have focussed on more complex explanatory structures.

RESULTS

Two frameworks are introduced incorporating corrections for multiplicity whilst maintaining appropriate structure in the explanatory variables. Within this paradigm, a choice has to be made as to whether multiplicity corrections should be applied to the saturated model, putting emphasis on controlling the rate of false positives, or to the predictive model, where emphasis is on model selection. This choice has implications for both the ranking and selection of the response variables identified as differentially expressed. The theoretical difference is demonstrated between the two approaches along with an empirical study of lipid composition in Arabidopsis under differing levels of salt stress.

CONCLUSIONS

Multiplicity corrections have an inherent weakness when the full explanatory structure is not properly incorporated. Although a unifying 'single best' recommendation is not provided, two reasonable alternatives are provided and the applicability of these approaches is discussed for different scenarios where the aims of analysis will differ. The key result is that the point at which multiplicity is incorporated into the analysis will fundamentally change the interpretation of the results, and the choice of approach should therefore be driven by the specific aims of the experiment.

Application of global metabolomic profiling of synovial fluid for osteoarthritis biomarkers

Osteoarthritis affects over 250 million individuals worldwide. Currently, there are no options for early diagnosis of osteoarthritis, demonstrating the need for biomarker discovery. To find biomarkers of osteoarthritis in human synovial fluid, we used high performance liquid-chromatography mass spectrometry for global metabolomic profiling. Metabolites were extracted from human osteoarthritic (n = 5), rheumatoid arthritic (n = 3), and healthy (n = 5) synovial fluid, and a total of 1233 metabolites were detected. Principal components analysis clearly distinguished the metabolomic profiles of diseased from healthy synovial fluid. Synovial fluid from rheumatoid arthritis patients contained expected metabolites consistent with the inflammatory nature of the disease. Similarly, unsupervised clustering analysis found that each disease state was associated with distinct metabolomic profiles and clusters of co-regulated metabolites. For osteoarthritis, co-regulated metabolites that were upregulated compared to healthy synovial fluid mapped to known disease processes including chondroitin sulfate degradation, arginine and proline metabolism, and nitric oxide metabolism. We utilized receiver operating characteristic analysis to determine the diagnostic value of each metabolite and identified 35 metabolites as potential biomarkers of osteoarthritis, with an area under the receiver operating characteristic curve >0.9. These metabolites included phosphatidylcholine, lysophosphatidylcholine, ceramides, myristate derivatives, and carnitine derivatives. This pilot study provides strong justification for a larger cohort-based study of human osteoarthritic synovial fluid using global metabolomics. The significance of these data is the demonstration that metabolomic profiling of synovial fluid can identify relevant biomarkers of joint disease.

Comprehensive transcriptome analyses correlated with untargeted metabolome reveal differentially expressed pathways in response to cell wall alterations

This research provides new insights into plant response to cell wall perturbations through correlation of transcriptome and metabolome datasets obtained from transgenic plants expressing cell wall-modifying enzymes. Plants respond to changes in their cell walls in order to protect themselves from pathogens and other stresses. Cell wall modifications in Arabidopsis thaliana have profound effects on gene expression and defense response, but the cell signaling mechanisms underlying these responses are not well understood. Three transgenic Arabidopsis lines, two with reduced cell wall acetylation (AnAXE and AnRAE) and one with reduced feruloylation (AnFAE), were used in this study to investigate the plant responses to cell wall modifications. RNA-Seq in combination with untargeted metabolome was employed to assess differential gene expression and metabolite abundance. RNA-Seq results were correlated with metabolite abundances to determine the pathways involved in response to cell wall modifications introduced in each line. The resulting pathway enrichments revealed the deacetylation events in AnAXE and AnRAE plants induced similar responses, notably, upregulation of aromatic amino acid biosynthesis and changes in regulation of primary metabolic pathways that supply substrates to specialized metabolism, particularly those related to defense responses. In contrast, genes and metabolites of lipid biosynthetic pathways and peroxidases involved in lignin polymerization were downregulated in AnFAE plants. These results elucidate how primary metabolism responds to extracellular stimuli. Combining the transcriptomics and metabolomics datasets increased the power of pathway prediction, and demonstrated the complexity of pathways involved in cell wall-mediated signaling.

Raising orphans from a metadata morass: A researcher's guide to re-use of public 'omics data

More than 15 petabases of raw RNAseq data is now accessible through public repositories. Acquisition of other 'omics data types is expanding, though most lack a centralized archival repository. Data-reuse provides tremendous opportunity to extract new knowledge from existing experiments, and offers a unique opportunity for robust, multi-'omics analyses by merging metadata (information about experimental design, biological samples, protocols) and data from multiple experiments. We illustrate how predictive research can be accelerated by meta-analysis with a study of orphan (species-specific) genes. Computational predictions are critical to infer orphan function because their coding sequences provide very few clues. The metadata in public databases is often confusing; a test case with Zea mays mRNA seq data reveals a high proportion of missing, misleading or incomplete metadata. This metadata morass significantly diminishes the insight that can be extracted from these data. We provide tips for data submitters and users, including specific recommendations to improve metadata quality by more use of controlled vocabulary and by metadata reviews. Finally, we advocate for a unified, straightforward metadata submission and retrieval system.

Exploring Polypharmacology in Drug Discovery and Repurposing Using the CANDO Platform

BACKGROUND

Traditional drug discovery approaches focus on a limited set of target molecules for treatment against specific indications/diseases. However, drug absorption, dispersion, metabolism, and excretion (ADME) involve interactions with multiple protein systems. Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others. The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations of the traditional reductionist paradigm in drug discovery.

METHODS

We developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis that drugs function by interacting with multiple protein targets to create a molecular interaction signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible with minimal side effects, followed by an 'all-compounds' vs 'all-proteins' fragment-based multitarget docking with dynamics screen to construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner.

RESULTS

We have previously applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting (polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate drug predictions using our proteomic and evolutionary drug discovery and repurposing platform.

CONCLUSION

These results have implications for future drug development and repurposing in the context of polypharmacology.

Spatial Mapping and Profiling of Metabolite Distributions during Germination

Germination is a highly complex process by which seeds begin to develop and establish themselves as viable organisms. In this study, we utilize a combination of gas chromatography-mass spectrometry, liquid chromatography-fluorescence, and mass spectrometry imaging approaches to profile and visualize the metabolic distributions of germinating seeds from two different inbreds of maize (Zea mays) seeds, B73 and Mo17. Gas chromatography and liquid chromatography analyses demonstrate that the two inbreds are highly differentiated in their metabolite profiles throughout the course of germination, especially with regard to amino acids, sugar alcohols, and small organic acids. Crude dissection of the seed followed by gas chromatography-mass spectrometry analysis of polar metabolites also revealed that many compounds were highly sequestered among the various seed tissue types. To further localize compounds, matrix-assisted laser desorption/ionization mass spectrometry imaging was utilized to visualize compounds in fine detail in their native environments over the course of germination. Most notably, the fatty acyl chain-dependent differential localization of phospholipids and triacylglycerols was observed within the embryo and radicle, showing correlation with the heterogeneous distribution of fatty acids. Other interesting observations include unusual localization of ceramides on the endosperm/scutellum boundary and subcellular localization of ferulate in the aleurone.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

Marine microorganisms as a promising and sustainable source of bioactive molecules

There is an urgent need to discover new drug entities due to the increased incidence of severe diseases as cancer and neurodegenerative pathologies, and reducing efficacy of existing antibiotics. Recently, there is a renewed interest in exploring the marine habitat for new pharmaceuticals also thanks to the advancement in cultivation technologies and in molecular biology techniques. Microorganisms represent a still poorly explored resource for drug discovery. The possibility of obtaining a continuous source of bioactives from marine microorganisms, more amenable to culturing compared to macro-organisms, may be able to meet the challenging demands of pharmaceutical industries. This would enable a more environmentally-friendly approach to drug discovery and overcome the over-utilization of marine resources and the use of destructive collection practices. The importance of the topic is underlined by the number of EU projects funded aimed at improving the exploitation of marine organisms for drug discovery.

Modeling the Metabolism of Arabidopsis thaliana: Application of Network Decomposition and Network Reduction in the Context of Petri Nets

Motivation:Arabidopsis thaliana is a well-established model system for the analysis of the basic physiological and metabolic pathways of plants. Nevertheless, the system is not yet fully understood, although many mechanisms are described, and information for many processes exists. However, the combination and interpretation of the large amount of biological data remain a big challenge, not only because data sets for metabolic paths are still incomplete. Moreover, they are often inconsistent, because they are coming from different experiments of various scales, regarding, for example, accuracy and/or significance. Here, theoretical modeling is powerful to formulate hypotheses for pathways and the dynamics of the metabolism, even if the biological data are incomplete. To develop reliable mathematical models they have to be proven for consistency. This is still a challenging task because many verification techniques fail already for middle-sized models. Consequently, new methods, like decomposition methods or reduction approaches, are developed to circumvent this problem. Methods: We present a new semi-quantitative mathematical model of the metabolism of Arabidopsis thaliana. We used the Petri net formalism to express the complex reaction system in a mathematically unique manner. To verify the model for correctness and consistency we applied concepts of network decomposition and network reduction such as transition invariants, common transition pairs, and invariant transition pairs. Results: We formulated the core metabolism of Arabidopsis thaliana based on recent knowledge from literature, including the Calvin cycle, glycolysis and citric acid cycle, glyoxylate cycle, urea cycle, sucrose synthesis, and the starch metabolism. By applying network decomposition and reduction techniques at steady-state conditions, we suggest a straightforward mathematical modeling process. We demonstrate that potential steady-state pathways exist, which provide the fixed carbon to nearly all parts of the network, especially to the citric acid cycle. There is a close cooperation of important metabolic pathways, e.g., the de novo synthesis of uridine-5-monophosphate, the γ-aminobutyric acid shunt, and the urea cycle. The presented approach extends the established methods for a feasible interpretation of biological network models, in particular of large and complex models.

Integrated omics analysis of specialized metabolism in medicinal plants

Medicinal plants are a rich source of highly diverse specialized metabolites with important pharmacological properties. Until recently, plant biologists were limited in their ability to explore the biosynthetic pathways of these metabolites, mainly due to the scarcity of plant genomics resources. However, recent advances in high-throughput large-scale analytical methods have enabled plant biologists to discover biosynthetic pathways for important plant-based medicinal metabolites. The reduced cost of generating omics datasets and the development of computational tools for their analysis and integration have led to the elucidation of biosynthetic pathways of several bioactive metabolites of plant origin. These discoveries have inspired synthetic biology approaches to develop microbial systems to produce bioactive metabolites originating from plants, an alternative sustainable source of medicinally important chemicals. Since the demand for medicinal compounds are increasing with the world's population, understanding the complete biosynthesis of specialized metabolites becomes important to identify or develop reliable sources in the future. Here, we review the contributions of major omics approaches and their integration to our understanding of the biosynthetic pathways of bioactive metabolites. We briefly discuss different approaches for integrating omics datasets to extract biologically relevant knowledge and the application of omics datasets in the construction and reconstruction of metabolic models.

Fluxomics links cellular functional analyses to whole-plant phenotyping

Fluxes through metabolic pathways reflect the integration of genetic and metabolic regulations. While it is attractive to measure all the mRNAs (transcriptome), all the proteins (proteome), and a large number of the metabolites (metabolome) in a given cellular system, linking and integrating this information remains difficult. Measurement of metabolome-wide fluxes (termed the fluxome) provides an integrated functional output of the cell machinery and a better tool to link functional analyses to plant phenotyping. This review presents and discusses sets of methodologies that have been developed to measure the fluxome. First, the principles of metabolic flux analysis (MFA), its 'short time interval' version Inst-MFA, and of constraints-based methods, such as flux balance analysis and kinetic analysis, are briefly described. The use of these powerful methods for flux characterization at the cellular scale up to the organ (fruits, seeds) and whole-plant level is illustrated. The added value given by fluxomics methods for unravelling how the abiotic environment affects flux, the process, and key metabolic steps are also described. Challenges associated with the development of fluxomics and its integration with 'omics' for thorough plant and organ functional phenotyping are discussed. Taken together, these will ultimately provide crucial clues for identifying appropriate target plant phenotypes for breeding.

Discovery and Characterization of the 3-Hydroxyacyl-ACP Dehydratase Component of the Plant Mitochondrial Fatty Acid Synthase System

We report the characterization of the Arabidopsis (Arabidopsis thaliana) 3-hydroxyacyl-acyl carrier protein dehydratase (mtHD) component of the mitochondrial fatty acid synthase (mtFAS) system, encoded by AT5G60335. The mitochondrial localization and catalytic capability of mtHD were demonstrated with a green fluorescent protein transgenesis experiment and by in vivo complementation and in vitro enzymatic assays. RNA interference (RNAi) knockdown lines with reduced mtHD expression exhibit traits typically associated with mtFAS mutants, namely a miniaturized morphological appearance, reduced lipoylation of lipoylated proteins, and altered metabolomes consistent with the reduced catalytic activity of lipoylated enzymes. These alterations are reversed when mthd-rnai mutant plants are grown in a 1% CO₂ atmosphere, indicating the link between mtFAS and photorespiratory deficiency due to the reduced lipoylation of glycine decarboxylase. In vivo biochemical feeding experiments illustrate that sucrose and glycolate are the metabolic modulators that mediate the alterations in morphology and lipid accumulation. In addition, both mthd-rnai and mtkas mutants exhibit reduced accumulation of 3-hydroxytetradecanoic acid (i.e. a hallmark of lipid A-like molecules) and abnormal chloroplastic starch granules; these changes are not reversible by the 1% CO₂ atmosphere, demonstrating two novel mtFAS functions that are independent of photorespiration. Finally, RNA sequencing analysis revealed that mthd-rnai and mtkas mutants are nearly equivalent to each other in altering the transcriptome, and these analyses further identified genes whose expression is affected by a functional mtFAS system but independent of photorespiratory deficiency. These data demonstrate the nonredundant nature of the mtFAS system, which contributes unique lipid components needed to support plant cell structure and metabolism.

Using silver and bighead carp cell lines for the identification of a unique metabolite fingerprint from thiram-specific chemical exposure

Conservation biology often requires the control of invasive species. One method is the development and use of biocides. Identifying new chemicals as part of the biocide registration approval process can require screening millions of compounds. Traditionally, screening new chemicals has been done in vivo using test organisms. Using in vitro (e.g., cell lines) and in silico (e.g., computer models) methods decrease test organism requirements and increase screening speed and efficiency. These methods, however, would be greatly improved by better understanding how individual fish species metabolize selected compounds. We combined cell assays and metabolomics to create a powerful tool to facilitate the identification of new control chemicals. Specifically, we exposed cell lines established from bighead carp and silver carp larvae to thiram (7 concentrations) then completed metabolite profiling to assess the dose-response of the bighead carp and silver carp metabolome to thiram. Forty one of the 700 metabolomic markers identified in bighead carp exhibited a dose-response to thiram exposure compared to silver carp in which 205 of 1590 metabolomic markers exhibited a dose-response. Additionally, we identified 11 statistically significant metabolomic markers based upon volcano plot analysis common between both species. This smaller subset of metabolites formed a thiram-specific metabolomic fingerprint which allowed for the creation of a toxicant specific, rather than a species-specific, metabolomic fingerprint. Metabolomic fingerprints may be used in biocide development and improve our understanding of ecologically significant events, such as mass fish kills.

Comparative mass spectrometry-based metabolomics strategies for the investigation of microbial secondary metabolites

Covering: 2000 to 2016The labor-intensive process of microbial natural product discovery is contingent upon identifying discrete secondary metabolites of interest within complex biological extracts, which contain inventories of all extractable small molecules produced by an organism or consortium. Historically, compound isolation prioritization has been driven by observed biological activity and/or relative metabolite abundance and followed by dereplication via accurate mass analysis. Decades of discovery using variants of these methods has generated the natural pharmacopeia but also contributes to recent high rediscovery rates. However, genomic sequencing reveals substantial untapped potential in previously mined organisms, and can provide useful prescience of potentially new secondary metabolites that ultimately enables isolation. Recently, advances in comparative metabolomics analyses have been coupled to secondary metabolic predictions to accelerate bioactivity and abundance-independent discovery work flows. In this review we will discuss the various analytical and computational techniques that enable MS-based metabolomic applications to natural product discovery and discuss the future prospects for comparative metabolomics in natural product discovery.