Metabolic profiling of plant extracts using direct-injection electrospray ionization mass spectrometry allows for high-throughput phenotypic characterization according to genetic and environmental effects

Genetic mapping of maize metabolites using high-throughput mass profiling

Plant metabolites are the basis of human nutrition and have biological relevance in ecology. Farmers selected plants with favorable characteristics since prehistoric times and improved the cultivars, but without knowledge of underlying mechanisms. Understanding the genetic basis of metabolite production can facilitate the successful breeding of plants with augmented nutritional value. To identify genetic factors related to the metabolic composition in maize, we generated mass profiles of 198 recombinant inbred lines (RILs) and their parents (B73 and Mo17) using direct-injection electrospray ionization mass spectrometry (DLI-ESI MS). Mass profiling allowed the correct clustering of samples according to genotype. We quantified 71 mass features from grains and 236 mass features from leaf extracts. For the corresponding ions, we identified tissue-specific metabolic 'Quantitative Trait Loci' (mQTLs) distributed across the maize genome. These genetic regions could regulate multiple metabolite biosynthesis pathways. Our findings demonstrate that DLI-ESI MS has sufficient analytical resolution to map mQTLs. These identified genetic loci will be helpful in metabolite-focused maize breeding. Mass profiling is a powerful tool for detecting mQTLs in maize and enables the high-throughput screening of loci responsible for metabolite biosynthesis.

Diversity of Herbicide-Resistance Mechanisms of Avena fatua L. to Acetyl-CoA Carboxylase-Inhibiting Herbicides in the Bajio, Mexico

Herbicide resistance is an evolutionary process that affects entire agricultural regions’ yield and productivity. The high number of farms and the diversity of weed management can generate hot selection spots throughout the regions. Resistant biotypes can present a diversity of mechanisms of resistance and resistance factors depending on selective conditions inside the farm; this situation is similar to predictions by the geographic mosaic theory of coevolution. In Mexico, the agricultural region of the Bajio has been affected by herbicide resistance for 25 years. To date, Avena fatua L. is one of the most abundant and problematic weed species. The objective of this study was to determine the mechanism of resistance of biotypes with failures in weed control in 70 wheat and barley crop fields in the Bajio, Mexico. The results showed that 70% of farms have biotypes with target site resistance (TSR). The most common mutations were Trp–1999–Cys, Asp–2078–Gly, Ile–2041–Asn, and some of such mutations confer cross-resistance to ACCase-inhibiting herbicides. Metabolomic fingerprinting showed four different metabolic expression patterns. The results confirmed that in the Bajio, there exist multiple selection sites for both resistance mechanisms, which proves that this area can be considered as a geographic mosaic of resistance.

Metabolic Footprints of Burkholderia Sensu Lato Rhizosphere Bacteria Active against Maize Fusarium Pathogens

Consistent with their reported abundance in soils, several Burkholderia sensu lato strains were isolated from the rhizosphere of maize plants cultivated at different sites in central México. Comparative analysis of their 16S rRNA gene sequences permitted their separation into three distinctive clades, which were further subdivided into six other clusters by their close resemblance to (1) Trinickia dinghuensis; (2) Paraburkholderia kirstenboschensis, P. graminis, P. dilworthii and P. rhynchosiae; (3) B. gladioli; (4) B. arboris; (5) B. contaminans, or (6) B. metallica representative species. Direct confrontation assays revealed that these strains inhibited the growth of pathogenic Fusarium oxysporum f. sp. radicis-lycopersici, and F. verticillioides within a roughly 3-55% inhibition range. The use of a DIESI-based non-targeted mass spectroscopy experimental strategy further indicated that this method is an option for rapid determination of the pathogen inhibitory capacity of Burkholderia sensu lato strains based solely on the analysis of their exometabolome. Furthermore, it showed that the highest anti-fungal activity observed in B. contaminans and B. arboris was associated with a distinctive abundance of certain m/z ions, some of which were identified as components of the ornbactin and pyochelin siderophores. These results highlight the chemical diversity of Burkholderia sensu lato bacteria and suggest that their capacity to inhibit the Fusarium-related infection of maize in suppressive soils is associated with siderophore synthesis.

Effects of the Developmental Regulator BOLITA on the Plant Metabolome

Transcription factors are important regulators of gene expression. They can orchestrate the activation or repression of hundreds or thousands of genes and control diverse processes in a coordinated way. This work explores the effect of a master regulator of plant development, BOLITA (BOL), in plant metabolism, with a special focus on specialized metabolism. For this, we used an Arabidopsis thaliana line in which the transcription factor activity can be induced. Fingerprinting metabolomic analyses of whole plantlets were performed at different times after induction. After 96 h, all induced replicas clustered as a single group, in contrast with all controls which did not cluster. Metabolomic analyses of shoot and root tissues enabled the putative identification of differentially accumulated metabolites in each tissue. Finally, the analysis of global gene expression in induced vs. non-induced root samples, together with enrichment analyses, allowed the identification of enriched metabolic pathways among the differentially expressed genes and accumulated metabolites after the induction. We concluded that the induction of BOL activity can modify the Arabidopsis metabolome. Future work should investigate whether its action is direct or indirect, and the implications of the metabolic changes for development regulation and bioprospection.

High levels of glucose alter Physcomitrella patens metabolism and trigger a differential proteomic response

Sugars act not only as substrates for plant metabolism, but also have a pivotal role in signaling pathways. Glucose signaling has been widely studied in the vascular plant Arabidopsis thaliana, but it has remained unexplored in non-vascular species such as Physcomitrella patens. To investigate P. patens response to high glucose treatment, we explored the dynamic changes in metabolism and protein population by applying a metabolomic fingerprint analysis (DIESI-MS), carbohydrate and chlorophyll quantification, Fv/Fm determination and label-free untargeted proteomics. Glucose feeding causes specific changes in P. patens metabolomic fingerprint, carbohydrate contents and protein accumulation, which is clearly different from those of osmotically induced responses. The maximal rate of PSII was not affected although chlorophyll decreased in both treatments. The biological process, cellular component, and molecular function gene ontology (GO) classifications of the differentially expressed proteins indicate the translation process is the most represented category in response to glucose, followed by photosynthesis, cellular response to oxidative stress and protein refolding. Importantly, although several proteins have high fold changes, these proteins have no predicted identity. The most significant discovery of our study at the proteome level is that high glucose increase abundance of proteins related to the translation process, which was not previously evidenced in non-vascular plants, indicating that regulation by glucose at the translational level is a partially conserved response in both plant lineages. To our knowledge, this is the first time that metabolome fingerprint and proteomic analyses are performed after a high sugar treatment in non-vascular plants. These findings unravel evolutionarily shared and differential responses between vascular and non-vascular plants.

Anthocyanin Profiling of Maize Grains Using DIESI-MSQD Reveals That Cyanidin-Based Derivatives Predominate in Purple Corn, whereas Pelargonidin-Based Molecules Occur in Red-Pink Varieties from Mexico

Corn seeds contain natural pigments and antioxidants, such as the molecular variants of flavonoids and carotenoids. The aleurone and pericarp tissues from pigmented genotypes were extracted for metabolic fingerprinting and evaluated using UV-vis and mass spectrometry (MS). MS ionomic fingerprints classified samples according to genetic background and kernel color. The MS/MS fragmentation pattern (Daughter and Neutral Loss methods) allowed the tentative identification of 18 anthocyanins with glycosyl, malonyl, and succinyl moieties, including 535 m/z for cyanidin-3-O-(6″-malonyl-glucoside) and 621 m/z for cyanidin-3-O-(3″,6″-dimalonyl-glucoside). We also detected 663 m/z for pelargonidin-3-O-(disuccinyl-glucoside) and 633 m/z for peonidin-3-O-(disuccinyl-glucoside). Cyanidin-based anthocyanins were the most abundant in dark purple colored kernels, while pelargonidins predominated in the red-pink kernels of the "Elote occidental" landrace. Grains of "Conico negro" had a simultaneous pigmentation of aleurone and pericarp, while Vitamaize had purple pigmentation only in the aleurone layer. Most landraces had a white endosperm, while Vitamaize had a yellow endosperm and a dark seed coat. We conclude that Vitamaize grains contain both carotenes and anthocyanins, and therefore it is proposed as a nontransgenic agronomically improved variety of tropical purple maize, a good source for organic superfoods.

Metabolomic effects of the colonization of Medicago truncatula by the facultative endophyte Arthrobacter agilis UMCV2 in a foliar inoculation system

Biofertilizer production and application for sustainable agriculture is already a reality. The methods for biofertilizers delivery in crop fields are diverse. Although foliar spray is gaining wide acceptance, little is known about the influence that the biochemical features of leaves have on the microbial colonization. Arthrobacter agilis UMCV2 is a rhizospheric and endophytic bacteria that promotes plant growth and health. In this study, we determined the capacity of the UMCV2 strain to colonize different leaves from Medicago truncatula in a foliar inoculation system. By using two powerful analytical methods based on mass spectrometry, we determined the chemical profile of the leaves in 15-d old plants. The metabolic signatures between the unifoliate leaf (m1) and the metameric units developing above (m2 and m3) were different, and interestingly, the highest colony forming units (CFU) was found in m1. The occurrence of the endophyte strongly affects the sugar composition in m1 and m2 leaves. Our results suggest that A. agilis UMCV2 colonize the leaves under a foliar inoculation system independently of the phenological age of the leaf and it is capable of modulating the carbohydrate metabolism without affecting the rest of the metabolome.

10,000-Times Diluted Doses of ACCase-Inhibiting Herbicides Can Permanently Change the Metabolomic Fingerprint of Susceptible Avena fatua L. Plants

Intentional use of low dosage of herbicides has been considered the cause of non-target resistance in weeds. However, herbicide drift could be a source of low dosage that could be detected by weeds and change their metabolism. Furthermore, the minimum dose that a plant can detect in the environment is unknown, and it is unclear whether low doses could modify the response of weeds when they are first exposed to herbicides (priming effects). In this study, we determined the metabolomic fingerprinting using GC-MS of susceptible Avena fatua L. plants exposed to a gradient of doses (1, 0.1, 0.001, 0.0001, and 0x) relative to the recommended dose of clodinafop-propargyl. Additionally, we evaluated the primed plants when they received a second herbicide application. The results showed that even a 10,000-fold dilution of the recommended dose could induce a significant change in the plants’ metabolism and that this change is permanent over the biological cycle. There was no evidence that priming increased its resistance level. However, hormesis increased biomass accumulation and survival in A. fatua plants. Better application methods which prevent herbicide drift should be developed in order to avoid contact with weeds that grow around the crop fields.

A Mass Spectrometry-Based Study Shows that Volatiles Emitted by Arthrobacter agilis UMCV2 Increase the Content of Brassinosteroids in Medicago truncatula in Response to Iron Deficiency Stress

Iron is an essential plant micronutrient. It is a component of numerous proteins and participates in cell redox reactions; iron deficiency results in a reduction in nutritional quality and crop yields. Volatiles from the rhizobacterium Arthrobacter agilis UMCV2 induce iron acquisition mechanisms in plants. However, it is not known whether microbial volatiles modulate other metabolic plant stress responses to reduce the negative effect of iron deficiency. Mass spectrometry has great potential to analyze metabolite alterations in plants exposed to biotic and abiotic factors. Direct liquid introduction-electrospray-mass spectrometry was used to study the metabolite profile in Medicago truncatula due to iron deficiency, and in response to microbial volatiles. The putatively identified compounds belonged to different classes, including pigments, terpenes, flavonoids, and brassinosteroids, which have been associated with defense responses against abiotic stress. Notably, the levels of these compounds increased in the presence of the rhizobacterium. In particular, the analysis of brassinolide by gas chromatography in tandem with mass spectrometry showed that the phytohormone increased ten times in plants grown under iron-deficient growth conditions and exposed to microbial volatiles. In this mass spectrometry-based study, we provide new evidence on the role of A. agilis UMCV2 in the modulation of certain compounds involved in stress tolerance in M. truncatula.

Mass Fingerprints of Tomatoes Fertilized with Different Nitrogen Sources Reveal Potential Biomarkers of Organic Farming

Direct-injection electron spray ionization mass spectrometry (DIESI-MS) can be used to quantify the whole set of positive and negative ions in complex biological samples. A cherry tomato cultivar was grown inside a greenhouse in soil pots supplemented with different nitrogen sources. Organic cultivation increased fruit dry matter while conventional chemical fertilizers increased yield due to higher water content. While soluble sugars were unaltered, secondary metabolism of tomato fruit was highly sensitive to compost soil supplied to the roots. From a total of ~1647 DIESI-MS signals, 725 revealed significant differences between treatments. Heatmap biclustering showed that ionomic differences were robustly maintained in independent experiments carried out during three consecutive years. The ionomic fingerprints allowed reproducible sample classification, reflecting the effect of organic farming on tomato fruit quality. Specific biomarker ions could be identified for various nitrogen sources. We propose DIESI-MS as an up-front strategy for plant food characterization aiming to identify the ions with the most significant differences across genotypes or agronomic conditions.

The fluorescent blue glow of banana fruits is not due to symplasmic plastidial catabolism but arises from insoluble phenols estherified to the cell wall

Banana fruits are firstly green due to chlorophyll, then yellow due to carotenoids and finally turn black due to polyphenols. However, bananas glow blue when observed under UV light. It has been reported that chlorophylls fade to give rise to fluorescent chlorophyll catabolites (FCCs) in senescent banana leaves and in ripening banana peels. FCCs are short lived catabolic intermediates that ultimately lead to non-fluorescent chlorophyll catabolites (NCCs). FCCs are abundant in bananas due to hypermodification; therefore, it was concluded that FCC caused yellow bananas to glow blue. Experiments were performed in order to shed new light into the autofluorescence phenomenon. Microscopy performed on living plant samples contradict the interpretation that the fluorescent blue glow is mainly caused by FCC inside the cell. Blue fluorescence in banana emerges from the cell wall, not from the symplasm. It is not primarily caused by soluble chlorophyll catabolites in the vacuoles or senescing plastids. Insoluble phenolics from the apoplast make bananas shine strongly blue under black light. Chlorophyll is a light trap that generates black holes of blue fluorescence, and therefore cells with chloroplasts glow less blue. The white pulp of banana fruits shine more strongly than the outer peel. In both tissues autofluorescence arises from insoluble phenols that are estherified to the cell wall. In monocot species (banana, maize, sugarcanne), blue fluorescense was strongest in the cell wall, whereas in dicots (e.g. arabidopsis, spearmint, hibiscus), blue fluorescence may be dominant from cytosolic, vacuolar or plastidial compartments.

Down-regulation of PvTRX1h increases nodule number and affects auxin, starch, and metabolic fingerprints in the common bean (Phaseolus vulgaris L.)

The legume-rhizobium symbiotic relationship has been widely studied and characterized. However, little information is available about the role of histone lysine methyltransferases in the legume-rhizobium interaction and in the formation of nitrogen-fixing nodules in the common bean. Thus, this study aimed to gain a better understanding of the epigenetic control of nodulation in the common bean. Specifically, we studied the role of PvTRX1h, a histone lysine methyltransferase coding gene, in nodule development and auxin biosynthesis. Through a reverse genetics approach, we generated common bean composite plants to knock-down PvTRX1h expression. Here we found that the down-regulation of PvTRX1h increased the number of nodules per plant, but reduced the number of colony-forming units recovered from nodules. Genes coding for enzymes involved in the synthesis of the indole-3-acetic acid were up-regulated, as was the concentration of this hormone. In addition, PvTRX1h down-regulation altered starch accumulation as determined by the number of amyloplasts per nodule. Metabolic fingerprinting by direct liquid introduction-electrospray ionization-mass spectrometry (DLI-ESI-MS) revealed that the root nodules were globally affected by PvTRX1h down-regulation. Therefore, PvTRX1h likely acts through chromatin histone modifications that alter the auxin signaling network to determine bacterial colonization, nodule number, starch accumulation, hormone levels, and cell proliferation.

Handling Complexity in Animal and Plant Science Research-From Single to Functional Traits: Are We There Yet?

The current knowledge of the main factors governing livestock, crop and plant quality as well as yield in different species is incomplete. For example, this can be evidenced by the persistence of benchmark crop varieties for many decades in spite of the gains achieved over the same period. In recent years, it has been demonstrated that molecular breeding based on DNA markers has led to advances in breeding (animal and crops). However, these advances are not in the way that it was anticipated initially by the researcher in the field. According to several scientists, one of the main reasons for this was related to the evidence that complex target traits such as grain yield, composition or nutritional quality depend on multiple factors in addition to genetics. Therefore, some questions need to be asked: are the current approaches in molecular genetics the most appropriate to deal with complex traits such as yield or quality? Are the current tools for phenotyping complex traits enough to differentiate among genotypes? Do we need to change the way that data is collected and analysed?

An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64

In biological mass spectrometry, crude instrumental data need to be converted into meaningful theoretical models. Several data processing and data evaluation steps are required to come to the final results. These operations are often difficult to reproduce, because of too specific computing platforms. This effect, known as 'workflow decay', can be diminished by using a standardized informatic infrastructure. Thus, we compiled an integrated platform, which contains ready-to-use tools and workflows for mass spectrometry data analysis. Apart from general unit operations, such as peak picking and identification of proteins and metabolites, we put a strong emphasis on the statistical validation of results and Data Mining. MASSyPup64 includes e.g., the OpenMS/TOPPAS framework, the Trans-Proteomic-Pipeline programs, the ProteoWizard tools, X!Tandem, Comet and SpiderMass. The statistical computing language R is installed with packages for MS data analyses, such as XCMS/metaXCMS and MetabR. The R package Rattle provides a user-friendly access to multiple Data Mining methods. Further, we added the non-conventional spreadsheet program teapot for editing large data sets and a command line tool for transposing large matrices. Individual programs, console commands and modules can be integrated using the Workflow Management System (WMS) taverna. We explain the useful combination of the tools by practical examples: (1) A workflow for protein identification and validation, with subsequent Association Analysis of peptides, (2) Cluster analysis and Data Mining in targeted Metabolomics, and (3) Raw data processing, Data Mining and identification of metabolites in untargeted Metabolomics. Association Analyses reveal relationships between variables across different sample sets. We present its application for finding co-occurring peptides, which can be used for target proteomics, the discovery of alternative biomarkers and protein-protein interactions. Data Mining derived models displayed a higher robustness and accuracy for classifying sample groups in targeted Metabolomics than cluster analyses. Random Forest models do not only provide predictive models, which can be deployed for new data sets, but also the variable importance. We demonstrate that the later is especially useful for tracking down significant signals and affected pathways in untargeted Metabolomics. Thus, Random Forest modeling supports the unbiased search for relevant biological features in Metabolomics. Our results clearly manifest the importance of Data Mining methods to disclose non-obvious information in biological mass spectrometry . The application of a Workflow Management System and the integration of all required programs and data in a consistent platform makes the presented data analyses strategies reproducible for non-expert users. The simple remastering process and the Open Source licenses of MASSyPup64 (http://www.bioprocess.org/massypup/) enable the continuous improvement of the system.

Metabolic fingerprinting of Arabidopsis thaliana accessions

In the post-genomic era much effort has been put on the discovery of gene function using functional genomics. Despite the advances achieved by these technologies in the understanding of gene function at the genomic and proteomic level, there is still a big genotype-phenotype gap. Metabolic profiling has been used to analyze organisms that have already been characterized genetically. However, there is a small number of studies comparing the metabolic profile of different tissues of distinct accessions. Here, we report the detection of over 14,000 and 17,000 features in inflorescences and leaves, respectively, in two widely used Arabidopsis thaliana accessions. A predictive Random Forest Model was developed, which was able to reliably classify tissue type and accession of samples based on LC-MS profile. Thereby we demonstrate that the morphological differences among A. thaliana accessions are reflected also as distinct metabolic phenotypes within leaves and inflorescences.