Background: The importance of mRNA methylation erased by ALKBH5 in mRNA biogenesis, decay, and translation control is an emerging research focus. Ectopically activated YAP is associated with the development of many human cancers. However, the mechanism whereby ALKBH5 regulates YAP expression and activity to inhibit NSCLC tumor growth and metastasis is not clear. Methods: Protein and transcript interactions were analyzed in normal lung cell and NSCLC cells. Gene expression was evaluated by qPCR and reporter assays. Protein levels were determined by immunochemical approaches. Nucleic acid interactions and status were analyzed by immunoprecipitation. Cell behavior was analyzed by standard biochemical tests. The m 6 A modification was analyzed by MeRIP. Results: Our results show that YAP expression is negatively correlated with ALKBH5 expression and plays an opposite role in the regulation of cellular proliferation, invasion, migration, and EMT of NSCLC cells. ALKBH5 reduced m 6 A modification of YAP. YTHDF3 combined YAP pre-mRNA depending on m 6 A modification. YTHDF1 and YTHDF2 competitively interacted with YTHDF3 in an m 6 A-independent manner to regulate YAP expression. YTHDF2 facilitated YAP mRNA decay via the AGO2 system, whereas YTHDF1 promoted YAP mRNA translation by interacting with eIF3a; both these activities are regulated by m 6 A modification. Furthermore, ALKBH5 decreased YAP activity by regulating miR-107/LATS2 axis in an HuR-dependent manner. Further, ALKBH5 inhibited tumor growth and metastasis in vivo by reducing the expression and activity of YAP. Conclusions: The presented findings suggest m 6 A demethylase ALKBH5 inhibits tumor growth and metastasis by reducing YTHDFs-mediated YAP expression and inhibiting miR-107/LATS2-mediated YAP activity in NSCLC. Moreover, effective inhibition of m 6 A modification of ALKBH5 might constitute a potential treatment strategy for lung cancer.

Brain ischemia inhibits immune function systemically, with resulting infectious complications. Whether in stroke different immune alterations occur in brain and periphery and whether analogous mechanisms operate in these compartments remains unclear. Here we show that in patients with ischemic stroke and in mice subjected to middle cerebral artery occlusion, natural killer (NK) cells display remarkably distinct temporal and transcriptome profiles in the brain as compared to the periphery. The activation of catecholaminergic and hypothalamic-pituitary-adrenal axis leads to splenic atrophy and contraction of NK cell numbers in the periphery through a modulated expression of SOCS3, whereas cholinergic innervation-mediated suppression of NK cell responses in the brain involves RUNX3. Importantly, pharmacological or genetic ablation of innervation preserved NK cell function and restrained post-stroke infection. Thus, brain ischemia compromises NK cell-mediated immune defenses through mechanisms that differ in the brain versus the periphery, and targeted inhibition of neurogenic innervation limits post-stroke infection.

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Intrusion detection can identify unknown attacks from network traffics and has been an effective means of network security. Nowadays, existing methods for network anomaly detection are usually based on traditional machine learning models, such as KNN, SVM, etc. Although these methods can obtain some outstanding features, they get a relatively low accuracy and rely heavily on manual design of traffic features, which has been obsolete in the age of big data. To solve the problems of low accuracy and feature engineering in intrusion detection, a traffic anomaly detection model BAT is proposed. The BAT model combines BLSTM (Bidirectional Long Short-term memory) and attention mechanism. Attention mechanism is used to screen the network flow vector composed of packet vectors generated by the BLSTM model, which can obtain the key features for network traffic classification. In addition, we adopt multiple convolutional layers to capture the local features of traffic data. As multiple convolutional layers are used to process data samples, we refer BAT model as BAT-MC. The softmax classifier is used for network traffic classification. The proposed end-to-end model does not use any feature engineering skills and can automatically learn the key features of the hierarchy. It can well describe the network traffic behavior and improve the ability of anomaly detection effectively. We test our model on a public benchmark dataset, and the experimental results demonstrate our model has better performance than other comparison methods.