MAG2 and three MAG2-INTERACTING PROTEINs form an ER-localized complex to facilitate storage protein transport in Arabidopsis thaliana.

In Arabidopsis thaliana, MAIGO 2 (MAG2) is involved in protein transport between the endoplasmic reticulum (ER) and the Golgi apparatus via its association with the ER-localized t-SNARE components SYP81/AtUfe1 and SEC20. To characterize the molecular machinery of MAG2-mediated protein transport, we explored MAG2-interacting proteins using transgenic A. thaliana plants expressing TAP-tagged MAG2. We identified three proteins, which were designated as MAG2-INTERACTING PROTEIN 1-3 [MIP1 (At2g32900), MIP2 (At5g24350) and MIP3 (At2g42700)]. Both MIP1 and MAG2 localized to the ER membrane. All of the mag2, mip1, mip2 and mip3 mutants exhibited a defect in storage protein maturation, and developed abnormal storage protein body (MAG body) structures in the ER of seed cells. These observations suggest that MIPs are closely associated with MAG2 and function in protein transport between the ER and Golgi apparatus. MIP1 and MIP2 contain a Zeste-White 10 (ZW10) domain and a Sec39 domain, respectively, but have low sequence identities (21% and 23%) with respective human orthologs. These results suggest that the plant MAG2-MIP1-MIP2 complex is a counterpart of the triple-subunit tethering complexes in yeast (Tip20p-Dsl1p-Sec39p) and humans (RINT1-ZW10-NAG). Surprisingly, the plant complex also contained a fourth member (MIP3) with a Sec1 domain. There have been no previous reports showing that a Sec1-containing protein is a subunit of ER-localized tethering complexes. Our results suggest that MAG2 and the three MIP proteins form a unique complex on the ER that is responsible for efficient transport of seed storage proteins.

Evaluation of Students' Online Learning Behavior and Perception of Psychological State Based on Data Mining.

This paper first analyzes the characteristics of students' OLB and stimulates students' online learning motivation based on the locus of control (LoC) stimulation model. Through data mining technology, this paper puts forward the analysis model of OLB, the sequence mining model of OLB, and the interactive model of OLB. Finally, a psychological state perception model based on OLB analysis is constructed; MAE and MSE are used as indexes to compare the fitting effects of GBRT, AdaBoostRegression, LinearSVR, and LinearRegression algorithms on the model. The results show that the relationship between students' psychological state and OLB is very close. While the model constructed has certain predictive ability for students' psychological state perception of OLB, which has certain practical significance for the regulation of students' learning behavior.