Regulation of the stability and ABA import activity of NRT1.2/NPF4.6 by CEPR2-mediated phosphorylation in Arabidopsis

Abscisic acid(ABA)transport plays an important role in systemic plant responses to environmental factors.However,it remains largely unclear about the precise regulation of ABA transporters in plants.In this study,we show that the C-terminally encoded peptide receptor 2(CEPR2)directly interacts with the ABA transporter NRT1.2/NPF4.6.Genetic and phenotypic analyses revealed that NRT1.2/NPF4.6 positively regulates ABA response and that NRT1.2/NPF4.6 is epistatically and negatively regulated by CEPR2.Further biochemical assays demonstrated that CEPR2 phosphorylates NRT1.2/NPF4.6 at serine 292 to promote its degradation under normal conditions.However,ABA treatment and non-phosphorylation at serine 292 prevented the degradation of NRT1.2/NPF4.6,indicating that ABA inhibits the phosphorylation of this residue.Transport assays in yeast and Xenopus oocytes revealed that non-phosphorylated NRT1.2/NPF4.6 had high levels of ABA import activity,whereas phosphorylated NRT1.2/NPF4.6 did not import ABA.Analyses of complemented nrt1.2 mutants that mimicked non-phosphorylated and phosphorylated NRT1.2/NPF4.6 confirmed that non-phosphorylated NRT1.2S292A had high stability and ABA import activity in planta.Additional experiments showed that NRT1.2/NPF4.6 was degraded via the 26S proteasome and vacuolar degradation pathways.Furthermore,we found that three E2 ubiquitin-conjugating enzymes,UBC32,UBC33,and UBC34,interact with NRT1.2/NPF4.6 in the endoplasmic reticulum and mediate its ubiquitination.NRT1.2/NPF4.6 is epistatically and negatively regulated by UBC32,UBC33,and UBC34 inplanta.Taken together,these results suggest that the stability and ABA import activity of NRT1.2/NPF4.6 are precisely regulated by its phosphorylation and degradation in response to environmental stress.

A robust and efficient structural reliability method combining radial-based importance sampling and Kriging

Simulation based structural reliability analysis suffers from a heavy computational burden, as each sample needs to be evaluated on the performance function, where structural analysis is performed. To alleviate the computational burden, related research focuses mainly on reduction of samples and application of surrogate model, which substitutes the performance function. However,the reduction of samples is achieved commonly at the expense of loss of robustness, and the construction of surrogate model is computationally expensive. In view of this, this paper presents a robust and efficient method in the same direction. The present method uses radial-based importance sampling (RBIS) to reduce samples without loss of robustness. Importantly, Kriging is fully used to efficiently implement RBIS. It not only serves as a surrogate to classify samples as we all know, but also guides the procedure to determine the optimal radius, with which RBIS would reduce samples to the highest degree. When used as a surrogate, Kriging is established through active learning, where the previously evaluated points to determine the optimal radius are reused. The robustness and efficiency of the present method are validated by five representative examples, where the present method is compared mainly with two fundamental reliability methods based on active learning Kriging.