Soybean (Glycine max) seeds are an important source of seed storage compounds, including protein, oil, and sugar used for food, feed, chemical, and biofuel production. We assessed detailed temporal transcriptional and metabolic changes in developing soybean embryos to gain a systems biology view of developmental and metabolic changes and to identify potential targets for metabolic engineering. Two major developmental and metabolic transitions were captured enabling identification of potential metabolic engineering targets specific to seed filling and to desiccation. The first transition involved a switch between different types of metabolism in dividing and elongating cells. The second transition involved the onset of maturation and desiccation tolerance during seed filling and a switch from photoheterotrophic to heterotrophic metabolism. Clustering analyses of metabolite and transcript data revealed clusters of functionally related metabolites and transcripts active in these different developmental and metabolic programs. The gene clusters provide a resource to generate predictions about the associations and interactions of unknown regulators with their targets based on “guilt-by-association” relationships. The inferred regulators also represent potential targets for future metabolic engineering of relevant pathways and steps in central carbon and nitrogen metabolism in soybean embryos and drought and desiccation tolerance in plants.

As it is becoming easier and faster to generate various types of high-throughput data, one would expect that by now we should have a comprehensive systems-level understanding of biology, biochemistry, and physiology at least in major prokaryotic and eukaryotic model systems. Despite the wealth of available data, we only get a glimpse of what is going on at the molecular level from the global perspective. The major reason is the high level of cellular complexity and our limited ability to identify all (or at least important) components and their interactions in virtually infinite number of internal and external conditions. Metabolism can be modeled mathematically by the use of genome-scale models (GEMs). GEMs are in silico metabolic flux models derived from available genome annotation. These models predict the combination of flux values of a defined metabolic network given the influence of internal and external signals. GEMs have been successfully implemented to model bacterial metabolism for over a decade. However, it was not until 2009 when the first GEM for Arabidopsis thaliana cell-suspension cultures was generated. Genome-scale modeling ("GEMing") in plants brings new challenges primarily due to the missing components and complexity of plant cells represented by the existence of: (i) photosynthesis; (ii) compartmentation; (iii) variety of cell and tissue types; and (iv) diverse metabolic responses to environmental and developmental cues as well as pathogens, insects, and competing weeds. This review presents a critical discussion of the advantages of existing plant GEMs, while identifies key targets for future improvements. Plant GEMs tend to be accurate in predicting qualitative changes in selected aspects of central carbon metabolism, while secondary metabolism is largely neglected mainly due to the missing (unknown) genes and metabolites. As such, these models are suitable for exploring metabolism in plants grown in favorable conditions, but not in field-grown plants that have to cope with environmental changes in complex ecosystems. AraGEM is the first GEM describing a photosynthetic and photorespiring plant cell (Arabidopsis thaliana). We demonstrate the use of AraGEM given the current (limited) knowledge of plant metabolism and reveal the unexpected robustness of AraGEM by a series of in silico simulations. The major focus of these simulations is on the assessment of the: (i) network connectivity; (ii) influence of CO₂ and photon uptake rates on cellular growth rates and production of individual biomass components; and (iii) stability of plant central carbon metabolism with internal pH changes.

Plant immunity relies on the perception of microbe-associated molecular patterns (MAMPs) from invading microbes to induce defense responses that suppress attempted infections. It has been proposed that MAMP-triggered immunity (MTI) suppresses bacterial infections by suppressing the onset of bacterial virulence. However, the mechanisms by which plants exert this action are poorly understood. Here, we showed that MAMP perception in Arabidopsis (Arabidopsis thaliana) induces the accumulation of free amino acids in a salicylic acid (SA)-dependent manner. When co-infiltrated with Glutamine and Serine, two of the MAMP-induced highly accumulating amino acids, Pseudomonas syringae pv. tomato DC3000 expressed low levels of virulence genes and failed to produce robust infections in otherwise susceptible plants. When applied exogenously, Glutamine and Serine directly suppressed bacterial virulence and growth, bypassing MAMP perception and SA signaling. In addition, an increased level of endogenous Glutamine in the leaf apoplast of a gain-of-function mutant of Glutamine Dumper-1 rescued the partially compromised bacterial virulence- and growth-suppressing phenotype of the SA-induced deficient-2 (sid2) mutant. Our data suggest that MTI suppresses bacterial infections by delaying the onset of virulence with an excess of amino acids at the early stages of infection.

Developing soybean seeds accumulate oils, proteins, and carbohydrates that are used as oxidizable substrates providing metabolic precursors and energy during seed germination. The accumulation of these storage compounds in developing seeds is highly regulated at multiple levels, including at transcriptional and post-transcriptional regulation. RNA sequencing was used to provide comprehensive information about transcriptional and post-transcriptional events that take place in developing soybean embryos. Bioinformatics analyses lead to the identification of different classes of alternatively spliced isoforms and corresponding changes in their levels on a global scale during soybean embryo development. Alternative splicing was associated with transcripts involved in various metabolic and developmental processes, including central carbon and nitrogen metabolism, induction of maturation and dormancy, and splicing itself. Detailed examination of selected RNA isoforms revealed alterations in individual domains that could result in changes in subcellular localization of the resulting proteins, protein-protein and enzyme-substrate interactions, and regulation of protein activities. Different isoforms may play an important role in regulating developmental and metabolic processes occurring at different stages in developing oilseed embryos.

Urushiols are the allergenic components of Toxicodendron radicans (poison ivy) as well as other Toxicodendron species. They are alk-(en)-yl catechol derivatives with a 15- or 17-carbon side chain having different degrees of unsaturation. Although several methods have been developed for analysis of urushiols in plant tissues, the in situ localization of the different urushiol congeners has not been reported. Here, we report on the first analysis of urushiols in poison ivy stems by matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI). Our results show that the urushiol congeners with 15-carbon side chains are mainly localized to the resin ducts, while those with 17-carbon side chains are widely distributed in cortex and vascular tissues. The presence of these urushiols in stem extracts of poison ivy seedlings was confirmed by GC-MS. These novel findings provide new insights into the spatial tissue distribution of urushiols that might be biosynthetically or functionally relevant.