Search

Abstract Background Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks. Results We introduce the TargetSearch package, an open source tool which is a flexible and accurate method for pre-processing even very large numbers of GC-MS samples within hours. We developed a novel strategy to iteratively correct and update retention time indices for searching and identifying metabolites. The package is written in the R programming language with computationally intensive functions written in C for speed and performance. The package includes a graphical user interface to allow easy use by those unfamiliar with R. Conclusions TargetSearch allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software. We validate our method by carrying out an analysis against both a set of known chemical standard mixtures and of a biological experiment. In addition we demonstrate its capabilities and speed by comparing it with other GC-MS pre-processing tools. We believe this package will greatly ease current bottlenecks and facilitate the analysis of metabolic profiling data.

Collections of insertional mutants have been instrumental for characterizing the functional relevance of genes in different model organisms, including Arabidopsis (Arabidopsis thaliana). However, mutations may often result in subtle phenotypes, rendering it difficult to pinpoint the function of a knocked-out gene. Here, we present a data-integrative modeling approach that enables predicting the effects of mutations on metabolic traits and plant growth. To test the approach, we gathered lipidomics data and physiological read-outs for a set of 64 Arabidopsis lines with mutations in lipid metabolism. Use of flux sums as a proxy for metabolite concentrations allowed us to integrate the relative abundance of lipids and facilitated accurate predictions of growth and biochemical phenotype in approximately 73% and 76% of the mutants, respectively, for which phenotypic data were available. Likewise, we showed that this approach can pinpoint alterations in metabolic pathways related to silent mutations. Therefore, our study paves the way for coupling model-driven characterization of mutant lines from different mutagenesis approaches with metabolomic technologies, as well as for validating knowledge structured in large-scale metabolic networks of plants and other species., Competing Interests: Conflict of interest statement. None declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society of Plant Biologists.)

Grapes, one of the oldest agricultural crops, are cultivated to produce table fruits, dried fruits, juice, and wine. Grapevine variety is composed of clones that share common morphological traits. However, they can differ in minor genetic mutations which often result in not only notorious morphological changes but also in other non-visible sensorial distinctive attributes. In the present work, we identified three Vitis vinifera cv. Pinot noir clones grown under identical field conditions that showed different grape cluster types. Here, sensorial analysis together with non-targeted metabolite profiles by Ultra High performance Liquid Chromatography (UPLC) couples to Ultra High Resolution Mass Spectrometry (FT-ICR-MS) of wines elaborated from the three different grape cluster types was studied with the aim of (i) finding sensorial differences among these three types of wines, and, if there were, (ii) determining the molecular features (metabolites) associated with these sensorial attributes by a multivariate statistical approach., Competing Interests: The authors declare no conflict of interest.

Heterosis has been extensively exploited for yield gain in maize (Zea mays L.). Here we conducted a comparative metabolomics-based analysis of young roots from in vitro germinating seedlings and from leaves of field-grown plants in a panel of inbred lines from the Dent and Flint heterotic patterns as well as selected F 1 hybrids. We found that metabolite levels in hybrids were more robust than in inbred lines. Using state-of-the-art modeling techniques, the most robust metabolites from roots and leaves explained up to 37 and 44% of the variance in the biomass from plants grown in two distinct field trials. In addition, a correlation-based analysis highlighted the trade-off between defense-related metabolites and hybrid performance. Therefore, our findings demonstrated the potential of metabolic profiles from young maize roots grown under tightly controlled conditions to predict hybrid performance in multiple field trials, thus bridging the greenhouse-field gap., (© 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.)

Plant primary metabolism is a highly coordinated, central, and complex network of biochemical processes regulated at both the genetic and post-translational levels. The genetic basis of this network can be explored by analyzing the metabolic composition of genetically diverse genotypes in a given plant species. Here, we report an integrative strategy combining quantitative genetic mapping and metabolite‒transcript correlation networks to identify functional associations between genes and primary metabolites in Arabidopsis thaliana. Genome-wide association study (GWAS) was used to identify metabolic quantitative trait loci (mQTL). Correlation networks built using metabolite and transcript data derived from a previously published time-course stress study yielded metabolite‒transcript correlations identified by covariation. Finally, results obtained in this study were compared with mQTL previously described. We applied a statistical framework to test and compare the performance of different single methods (network approach and quantitative genetics methods, representing the two orthogonal approaches combined in our strategy) with that of the combined strategy. We show that the combined strategy has improved performance manifested by increased sensitivity and accuracy. This combined strategy allowed the identification of 92 candidate associations between structural genes and primary metabolites, which not only included previously well-characterized gene‒metabolite associations, but also revealed novel associations. Using loss-of-function mutants, we validated two of the novel associations with genes involved in tyrosine degradation and in β-alanine metabolism. In conclusion, we demonstrate that applying our integrative strategy to the largely untapped resource of metabolite-transcript associations can facilitate the discovery of novel metabolite-related genes. This integrative strategy is not limited to A. thaliana, but generally applicable to other plant species., Competing Interests: The authors have declared that no competing interests exist.

Trichoderma spp. are versatile opportunistic plant symbionts which can colonize the apoplast of plant roots. Microarrays analysis of Arabidopsis thaliana roots inoculated with Trichoderma asperelloides T203, coupled with qPCR analysis of 137 stress responsive genes and transcription factors, revealed wide gene transcript reprogramming, proceeded by a transient repression of the plant immune responses supposedly to allow root colonization. Enhancement in the expression of WRKY18 and WRKY40, which stimulate JA-signaling via suppression of JAZ repressors and negatively regulate the expression of the defense genes FMO1, PAD3 and CYP71A13, was detected in Arabidopsis roots upon Trichoderma colonization. Reduced root colonization was observed in the wrky18/wrky40 double mutant line, while partial phenotypic complementation was achieved by over-expressing WRKY40 in the wrky18 wrky40 background. On the other hand increased colonization rate was found in roots of the FMO1 knockout mutant. Trichoderma spp. stimulate plant growth and resistance to a wide range of adverse environmental conditions. Arabidopsis and cucumber (Cucumis sativus L.) plants treated with Trichoderma prior to salt stress imposition show significantly improved seed germination. In addition, Trichoderma treatment affects the expression of several genes related to osmo-protection and general oxidative stress in roots of both plants. The MDAR gene coding for monodehydroascorbate reductase is significantly up-regulated and, accordingly, the pool of reduced ascorbic acid was found to be increased in Trichoderma treated plants. 1-Aminocyclopropane-1-carboxylate (ACC)-deaminase silenced Trichoderma mutants were less effective in providing tolerance to salt stress, suggesting that Trichoderma, similarly to ACC deaminase producing bacteria, can ameliorate plant growth under conditions of abiotic stress, by lowering ameliorating increases in ethylene levels as well as promoting an elevated antioxidative capacity.