Here we present a metabolic profiling strategy employing direct infusion Orbitrap mass spectrometry (MS) and gas chromatography-mass spectrometry (GC/MS) for the monitoring of soybean’s (L. potential for applications in biotechnology, crop breeding, and agrochemical and food industries. Introduction Metabolomics is usually a robust bioanalytical tool for the comprehensive analysis and monitoring of herb metabolome [1]C[3]. However, its application for monitoring the regulation of the global herb metabolism in response to biotic stresses is still in its infancy, receiving increasing attention [4]C[6]. This, could provide valuable information for applications in herb biotechnology, biomarker-assisted selection, and agrochemical, food, and pharmaceutical industries [7], and in turn could boost agricultural production. The recent advances in bioanalytical protocols, analyzers, metabolite databases, and bioinformatics software enable the recording of a vast number of chemical features in the analyzed herb samples, whose identification and biological interpretation is challenging. Moreover, there is an increasing demand for standardization of data reporting for large-scale metabolomics [8], which will help researchers to cross-reference results from different studies with profound benefits. Within this context, we have undertaken the task of developing a high-throughput metabolomics/bioinformatics protocol for the robust dissection of plant-fungal pathogen conversation using the pathosystem; soybean [(L.) Merrill, Leguminosae] and its soil-borne fungal pathogen-Khn (anastomosis group 4, AG4). For the analysis of soybean’s metabolome direct infusion Orbitrap mass spectrometry (DIMS) and gas chromatography-MS (GC/MS) analyzers were employed, which exhibit complimentary capabilities for metabolite detection and identification. Soybean is usually a crop grown on almost 6% of arable land [9] and among the most important herb sources of human food, animal feed protein, and cooking oil [10], phytoestrogens [11], and biodiesel [12]. It is the first legume species with a complete sequence [13], and therefore, a key reference for the development of high-throughput herb metabolomics protocols. Various biotic constraints such as, bacteria, fungi, nematodes, and insects threaten its production by directly reducing seed yield and/or quality [14]. Among them is the soil-borne fungal pathogen Contamination The complexity of plants’ metabolome makes their deconvolution challenging, requiring often the utilization of more than one analyzer. For DIMS-based metabolomics, ion suppression can impact the validity of analysis, however, information on its dynamics is usually yet fragmented. Here, analysis of samples with comparable metabolite profiles resulted in consistent ion suppression as revealed by the tight clustering among biological replications performing multivariate analyses (Fig. S1). The latter confirms the potential of DIMS for high-throughput metabolomics applications in line with recent studies [19], [20]. The developed protocol (Fig. S2) enabled the in-depth deconvolution of DIMS data, as confirmed by the large number of obtained frames using the software SIEVE (Table S1). On the other hand, GC/MS analyses facilitated the construction of a matrix composed of 135 features, reproducibly detected across treatments. In total, 377 putatively or completely identified metabolites were statistically significant different between controls and infected soybean sprouts (Data Set S1). MS spectra of identified metabolites of biological origin from GC/MS analysis, and MS/MS spectra from DIMS Orbitrap analysis provided in the Data Set S2 and Data Sets S3 and S4, respectively. Sets of original GC/MS and DIMS Orbitrap data can be found at the public repository of Metabolights (http://www.ebi.ac.uk/metabolights/) (Accession # MTBLS118 and MTBLS117, respectively). The complexity of undergoing biochemical events during soybean-interaction (Fig. S3) is usually indicated by the diversity of chemical groups and biosynthetic pathways involved (Figs. 1 and ?and2,2, Fig. S4). Up-regulated metabolites also detected in fungal profiles, which could have leverage on data interpretation, were omitted from analyses. Physique 1 Classification of soybean metabolites into chemical groups in response to contamination. Physique 2 Classification of metabolites signatory of the soybean’s response to invasion based on their participation in herb 13241-28-6 metabolic pathways/functions, measured as instances, since a metabolite can 13241-28-6 be involved in more than one pathway. Principal component analysis (PCA) was performed initially for the whole dataset revealing no outliers (data not shown). In a second step, PLS-DA revealed a strong discrimination between metabolite profiles of control 13241-28-6 and invasion at Rabbit Polyclonal to GATA4 24 h and 48 h post-inoculation based on their participation in biosynthetic pathways, measured as instances, since a metabolite can be involved in more than … The vast majority of identified signatory metabolites of the infection belong to carboxylic and amino acids, carbohydrates, and flavonoids (Fig. 1dissection of its sub-networks. Using Cytoscape’s plug-in BisoGenet [21], selected metabolites such as phytoalexins and flavonoids, and biosynthetic precursors, whose relative concentrations significantly increased at 48 h post-inoculation and possible interconnecting paths between them are highlighted (Fig. 3at 48h post-inoculation (contamination at 24 h and 48 h post-inoculation, including portions of the amino acid biosynthesis, and the isoflavonoid and phenylpropanoid biosynthetic pathways, … Physique 5 Fluctuations in the at 24 h and 48 h post-inoculation coded using a color code based on in activation of soybean defense mechanisms. Contamination Substantially Alters the Primary Metabolism.