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Phosphate-dependent regulation of vacuolar trafficking of OsSPX-MFSs is critical for maintaining intracellular phosphate homeostasis in rice

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Molecular Plant 2023年第8期16卷 1304-1320页

作者： Runze Guo Qi Zhang Kun Qian Yinghui Ying Wenying Liao lening gan Chuanzao Mao Yong Wang James Whelan Huixia Shou State Key Laboratory of Piant Physiology and Biochemistry Cllege of Life SciencesZhejiang UniversityHangzhouZhejang 310058P.R.China Hainan Institute Zhejiang UniversitySanya 572025China The Provincial International Science and Technology Cooperation Base on Engineering Biology International Campus of Zhejiang UniversityHainingZhejang 314400China Zhijiang lab Hangzhou 310012China

Vacuolar storage of inorganic phosphate(Pi)is essential for Pi homeostasis in plants.The SPX-MFS family proteins have been demonstrated to be vacuolar Pi transporters in many plant species.Transcriptional regulation of the predominant transporter among rice SPX-MFSs,OsSPX-MFS3,was only moderately suppressed by Pi starvation.Thus,post-transcriptional mechanisms were hypothesized to regulate the activity of OsSPX-MFS3.In this study,we found that the tonoplast localization of OsSPX-MFSs is inhibited under Pi-depleted conditions,resulting in their retention in the pre-vacuolar compartments(PVCs).A yeast two-hybrid screen identified that two SNARE proteins,OsSYP21 and OsSYP22,interact with the MFS domain of OsSPX-MFS3.Further genetic and cytological analyses indicate that OsSYP21 and OsSYP22 facilitate trafficking of OsSPX-MFS3 from PVCs to the tonoplast.Although a homozygous frameshift mutation in OsSYP22 appeared to be lethal,tonoplast localization of OsSPX-MFS3 was significantly inhibited in transgenic plants expressing a negative-dominant form of OsSYP22(OsSYP22-ND),resulting in reduced vacuolar Pi concentrations in OsSYP22-ND plants.Under Pi-depleted conditions,the interaction between OsSYP22 and OsSPX-MFS3 was disrupted,and this process depended on the presence of the SPX domain.Deleting the SPX domains of OsSPX-MFSs resulted in their tonoplast localization under both Pi-depleted and Pi-replete conditions.Complementation of the osspx-mfs1/2/3 triple mutants with the MFS domain or the SPX domain of OsSPX-MFS3 confirmed that the MFS and SPX domains are responsive to Pi transport activity and Pi-dependent regulation,respectively.These data indicated that the SPX domains of OsSPX-MFSs sense cellular Pi(InsP)levels and,under Pi-depleted conditions,inhibit the interaction between OsSPX-MFSs and OsSYP21/22 and subsequent trafficking of OsSPX-MFSs from PVCs to the tonoplast.

关键词： fir homeostasis inhibited

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胞质磷酸盐浓度调控水稻液泡磷转运蛋白OsSPX-MFS的分选与转运的分子机制

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科学通报 2023年第26期68卷 3418-3420页

作者：干乐宁郭润泽寿惠霞浙江大学海南研究院三亚572025 浙江大学生命科学学院杭州310058

磷是植物生长发育所必需的大量元素,参与光合、呼吸、氧化还原平衡维持和能量代谢等诸多生理过程,同时也是核酸、磷脂及多种辅酶等重要分子的组分[1].植物主要通过根部吸收土壤中的游离磷酸盐.

关键词：大量元素氧化还原平衡磷酸盐浓度能量代谢磷转运蛋白分子机制

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Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

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Chinese Journal of Chemical Engineering 2020年第7期28卷 1875-1883页

作者： Wende Tian Zijian Liu lening Li Shifa Zhang Chuankun Li College of Chemical Engineering Qingdao University of Science&TechnologyQingdao 266042China Qingdao NOVAN Electromechanical Technology Qingdao 266100China State Key Laboratory of Safety and Control for Chemicals SINOPEC Qingdao Research Institute of Safety EngineeringQingdao 266071China

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(gan)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

关键词： Chemical process Deep Belief Networks Fault identification Generative Adversarial Networks Spearman Rank Correlation

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