Transcriptome analysis of soybean (Glycine max) root genes differentially expressed in rhizobial, arbuscular mycorrhizal, and dual symbiosis.

Soybean (Glycine max) roots establish associations with nodule-inducing rhizobia and arbuscular mycorrhizal (AM) fungi. Both rhizobia and AM fungi have been shown to affect the activity of and colonization by the other, and their interactions can be detected within host plants. Here, we report the transcription profiles of genes differentially expressed in soybean roots in the presence of rhizobial, AM, or rhizobial-AM dual symbiosis, compared with those in control (uninoculated) roots. Following inoculation, soybean plants were grown in a glasshouse for 6 weeks; thereafter their root transcriptomes were analyzed using an oligo DNA microarray. Among the four treatments, the root nodule number and host plant growth were highest in plants with dual symbiosis. We observed that the expression of 187, 441, and 548 host genes was up-regulated and 119, 1,439, and 1,298 host genes were down-regulated during rhizobial, AM, and dual symbiosis, respectively. The expression of 34 host genes was up-regulated in each of the three symbioses. These 34 genes encoded several membrane transporters, type 1 metallothionein, and transcription factors in the MYB and bHLH families. We identified 56 host genes that were specifically up-regulated during dual symbiosis. These genes encoded several nodulin proteins, phenylpropanoid metabolism-related proteins, and carbonic anhydrase. The nodulin genes up-regulated by the AM fungal colonization probably led to the observed increases in root nodule number and host plant growth. Some other nodulin genes were down-regulated specifically during AM symbiosis. Based on the results above, we suggest that the contribution of AM fungal colonization is crucial to biological N2-fixation and host growth in soybean with rhizobial-AM dual symbiosis.

Improved kinetic model of Escherichia coli central carbon metabolism in batch and continuous cultures.

Many kinetic models of Escherichia coli central metabolism have been built, but few models accurately reproduced the dynamic behaviors of wild type and multiple genetic mutants. In 2016, our latest kinetic model improved problems of existing models to reproduce the cell growth and glucose uptake of wild type, ΔpykA:pykF and Δpgi in a batch culture, while it overestimated the glucose uptake and cell growth rates of Δppc and hardly captured the typical characteristics of the glyoxylate and TCA cycle fluxes for Δpgi and Δppc. Such discrepancies between the simulated and experimental data suggested biological complexity. In this study, we overcame these problems by assuming critical mechanisms regarding the OAA-regulated isocitrate dehydrogenase activity, aceBAK gene regulation and growth suppression. The present model accurately predicts the extracellular and intracellular dynamics of wild type and many gene knockout mutants in batch and continuous cultures. It is now the most accurate, detailed kinetic model of E. coli central carbon metabolism and will contribute to advances in mathematical modeling of cell factories.

Robustness analysis of the detailed kinetic model of an ErbB signaling network by using dynamic sensitivity.

The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK*) and Akt activity (Akt*), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK* and Akt* change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK* and Akt*. ERK* increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt* had a gradual wide-range response to HRG and a blunt response to EGF. Akt* was sensitive to perturbations of intracellular kinetics, while ERK* was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK* and Akt* in response to the dose of EGF and HRG, illustrated the response characteristics of ERK* and Akt*, and estimated mechanisms for generating robustness in the ErbB signaling network.

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli.

BACKGROUND: A kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters. RESULTS: In this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture. CONCLUSIONS: This model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.

Proteomic and metabolomic analyses of soybean root tips under flooding stress.

Flooding is one of the serious problems for soybean plants because it inhibits growth. Proteomic and metabolomic techniques were used to determine whether proteins and metabolites are altered in the root tips of soybeans under flooding stress. Two-day-old soybean plants were flooded for 2 days, and proteins and metabolites were extracted from root tips. Flooding-responsive proteins were identified using two-dimensional- or SDS-polyacrylamide gel electrophoresis- based proteomics techniques. Using both techniques, 172 proteins increased and 105 proteins decreased in abundance in the root tips of flood-stressed soybean. The abundance of methionine synthase, heat shock cognate protein, urease, and phosphoenol pyruvate carboxylase was significantly increased by flooding stress. Furthermore, 73 flooding-responsive metabolites were identified using capillary electrophoresis-mass spectrometry. The levels of gamma-aminobutyric acid, glycine, NADH2, and phosphoenol pyruvate were increased by flooding stress. Taken together, these results suggest that synthesis of phosphoenol pyruvate by way of oxaloacetate produced in the tricarboxylic acid cycle is activated in soybean root tips in response to flooding stress, and that flooding stress also leads to modulation of the urea cycle in the root tips.