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Yokoyama, G.

Publications and source records attributed to Yokoyama, G..

2 recordsLinked to original sources

Stomatal, mesophyll, and biochemical limitation to photosynthesis of soybeans under waterlogging and reoxygenation

While waterlogging stress slows photosynthetic rate (Asat), the underlying processes remain poorly understood. Here, we aimed to characterize the limitations to photosynthesis imposed by stomatal conductance (gs), mesophyll conductance (gm), and biochemical processes under waterlogging and subsequent reoxygenation. Two soybean cultivars (Glycine max L. cv. Fukuyutaka and Iyodaizu) were subjected to 6 days of waterlogging, after which excess water was drained. The responses of Asat, gs, gm, and the maximum carboxylation rate (Vcmax) were investigated. In both cultivars, A declined significantly within 4 days of waterlogging and did not recover completely by two weeks of reoxygenation. During waterlogging, CO2 concentration at carboxylation site decreased in parallel with gs and gm, indicating that photosynthesis was mainly limited by diffusional factors (combination of gs and gm). After drainage, diffusional limitation persisted during early reoxygenation, whereas biochemical limitation due to reduced Vcmax became dominant after 7 days of reoxygenation. Therefore, maintaining high diffusional conductances and Vcmax during waterlogging and reoxygenation, respectively, is important for enhancing photosynthetic tolerance to waterlogging stress. Overall, our results demonstrate that Asat under waterlogging and reoxygenation is dynamically constrained by multiple factors, emphasizing the need for comprehensive assessment of gas diffusion and carbon assimilation processes.

plant biology↗

Screening and machine-learning assisted prediction of translation-enhancing peptides reducing ribosomal stalling in Escherichia coli

We previously reported that the nascent SKIK peptide enhances translation and alleviates ribosomal stalling caused by arrest peptides (APs) such as SecM and polyproline when positioned immediately upstream of the APs in both Escherichia coli in vivo and in vitro translation systems. In this study, we performed a comprehensive screening of translation-enhancing peptides (TEPs) using a randomized artificial tetrapeptide library. The screening was based on the ability of the peptides to suppress SecM AP-induced translational stalling in E. coli cells. Various TEPs exhibiting a range of translation-enhancing activities were identified. In vitro translation analysis suggested that the fourth amino acid in the tetrapeptide plays a key role in reducing SecM AP-mediated stalling. Furthermore, we developed a machine learning model using a random forest algorithm to predict TEP activity. The predicted values showed a strong correlation with experimentally measured activities. These findings offer a compact peptide toolkit and a data-driven approach for mitigating AP-induced ribosome stalling, with potential applications in synthetic biology.

synthetic biology↗