Macroautophagy/autophagy is a conserved catabolic process that recycles cytoplasmic material during low energy conditions. BECN1/Beclin1 (Beclin 1, autophagy related) is an essential protein for function of the class 3 phosphatidylinositol 3-kinase (PtdIns3K) complexes that play a key role in autophagy nucleation and elongation. Here, we show that AMP-activated protein kinase (AMPK) regulates autophagy by phosphorylating BECN1 at Thr388. Phosphorylation of BECN1 is required for autophagy upon glucose withdrawal. BECN1 T388A , a phosphorylation defective mutant, suppresses autophagy through decreasing the interaction between PIK3C3 (phosphatidylinositol 3-kinase catalytic subunit type 3) and ATG14 (autophagy-related 14). The BECN1 T388A mutant has a higher affinity for BCL2 than its wild-type counterpart; the mutant is more prone to dimer formation. Conversely, a BECN1 phosphorylation mimic mutant, T388D, has stronger binding to PIK3C3 and ATG14, and promotes higher autophagy activity than the wild-type control. These findings uncover a novel mechanism of autophagy regulation.

The Unc-51 like autophagy activating kinase 1 (ULK1) complex plays a central role in the initiation stage of autophagy. However, the function of ULK1 in the late stage of autophagy is unknown. Here, we report that ULK1, a central kinase of the ULK1 complex involved in autophagy initiation, promotes autophagosome–lysosome fusion. PKCα phosphorylates ULK1 and prevents autolysosome formation. PKCα phosphorylation of ULK1 does not change its kinase activity; however, it decreases autophagosome–lysosome fusion by reducing the affinity of ULK1 for syntaxin 17 (STX17). Unphosphorylated ULK1 recruited STX17 and increased STX17′s affinity towards synaptosomal-associated protein 29 (SNAP29). Additionally, phosphorylation of ULK1 enhances its interaction with heat shock cognate 70 kDa protein (HSC70) and increases its degradation through chaperone-mediated autophagy (CMA). Our study unearths a key mechanism underlying autolysosome formation, a process in which the kinase activity of PKCα plays an instrumental role, and reveals the significance of the mutual regulation of macroautophagy and CMA in maintaining the balance of autophagy.

As a promising research area in Internet of Things (IoT), Internet of Vehicles (IoV) has attracted much attention in wireless communication and network. In general, vehicle localization can be achieved by the Global Positioning Systems (GPS). However, in some special scenarios, such as cloud cover, tunnels or some places where the GPS signals are weak, GPS cannot perform well. The continuous and accurate localization services cannot be guaranteed. In order to improve the accuracy of vehicle localization, an assistant vehicle localization method based on Direction-of-Arrival (DOA) estimation is proposed in this paper. The assistant vehicle localization system is composed of three Base Stations (BS) equipped with a Multiple Input Multiple Output (MIMO) array. The locations of vehicles can be estimated if the positions of the three BSs and the DOAs of vehicles estimated by the BSs are known. However, the DOA estimated accuracy maybe degrade dramatically when the electromagnetic environment is complex. In the proposed method, a Sparse Bayesian Learning (SBL) based robust DOA estimation approach is first proposed to achieve the off-grid DOA estimation of the target vehicles under the condition of non-uniform noise, where the covariance matrix of non-uniform noise is estimated by a Least Squares (LS) procedure, and a grid refinement procedure implemented by finding the roots of a polynomial is performed to refine the grid points to reduce the off-grid error. Then, according to the DOA estimation results, the target vehicle is cross-located once by each two BSs in the localization system. Finally, robust localization can be realized based on the results of three-time cross-location. Plenty of simulation results demonstrate the effectiveness and superiority of the proposed method.