Autophagosome and lysosome fusion is an important macroautophagy/autophagy process for cargo degradation, and SNARE proteins, including STX17, SNAP29, VAMP7 and VAMP8, are key players in this process. However, the manner in which this process is precisely regulated is poorly understood. Here, we show that VAMP7B, a SNARE domain-disrupted isoform of R-SNARE protein VAMP7, competes with SNARE domain functional isoform VAMP7A to bind to STX17 and inhibits autophagosome-lysosome fusion. Moreover, we show that DIPK2A, a late endosome-and lysosome-localized protein, binds to VAMP7B, which inhibits the interaction of VAMP7B with STX17 and enhances the binding of STX17 to VAMP7A, thus enhancing autophagosome-lysosome fusion. Furthermore, DIPK2A participates in autophagic degradation of mitochondria proteins and alleviates apoptosis. Thus, we reveal a new aspect of autophagosome-lysosome fusion in which different isoforms of VAMP7 compete with STX17 and their regulation by DIPK2A.

With the rapid development of the Internet, the number of Internet users has grown rapidly, and the Internet has become more and more influential on people's lives. As a result, the amount of network text is increasing rapidly, and it is difficult to extract interested event information from it only by manual reading. Therefore, event extraction technique automatically extracting useful information from a large amount of unstructured texts becomes increasingly important. Event detection is the first step of event extraction task and plays a vital role in it. However, current event detection research lacks comprehensive consideration of the context of the trigger words. A Chinese event detection method based on multi-feature fusion and BiLSTM is proposed in this paper. The contextual information of word is divided into sentence-level and document-level in the method. The contextual information is captured based on BiLSTM model. At the same time, a word representation method suitable for trigger word classification tasks is proposed in this paper. The word representation incorporates semantic information, grammar information, and document-level context information of word. The word vectors in the sentence are sequentially inputted into BiLSTM model to obtain output vectors containing sentence-level contextual information. Finally, output vectors of BiLSTM are inputted into the Softmax classifier to realize the identification of the trigger words. The experimental results show that Chinese Event Detection Based on Multi-feature Fusion and BiLSTM method proposed in this paper has high accuracy.

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