Structural basis of the monkeypox virus mRNA cap N7 methyltransferase complex.

The global outbreak of Mpox, caused by the monkeypox virus (MPXV), has attracted international attention and become another major infectious disease event after COVID-19. The mRNA cap N7 methyltransferase (RNMT) of MPXV methylates the N7 position of the added guanosine to the 5'-cap structure of mRNAs and plays a vital role in evading host antiviral immunity. MPXV RNMT is composed of the large subunit E1 and the small subunit E12. How E1 and E12 of MPXV assembly remains unclear. Here, we report the crystal structures of E12, the MTase domain of E1 with E12 (E1CTD-E12) complex, and the E1CTD-E12-SAM ternary complex, revealing the detailed conformations of critical residues and the structural changes upon E12 binding to E1. Functional studies suggest that E1CTD N-terminal extension (Asp545-Arg562) and the small subunit E12 play an essential role in the binding process of SAM. Structural comparison of the AlphaFold2-predicted E1, E1CTD-E12 complex, and the homologous D1-D12 complex of vaccinia virus (VACV) indicates an allosteric activating effect of E1 in MPXV. Our findings provide the structural basis for the MTase activity stimulation of the E1-E12 complex and suggest a potential interface for screening the anti-poxvirus inhibitors.

ADSeg: A flap-attention-based deep learning approach for aortic dissection segmentation.

Accurate and rapid segmentation of the lumen in an aortic dissection (AD) is an important prerequisite for risk evaluation and medical planning for patients with this serious condition. Although some recent studies have pioneered technical advances for the challenging AD segmentation task, they generally neglect the intimal flap structure that separates the true and false lumens. Identification and segmentation of the intimal flap may simplify AD segmentation, and the incorporation of long-distance z axis information interaction along the curved aorta may improve segmentation accuracy. This study proposes a flap attention module that focuses on key flap voxels and performs operations with long-distance attention. In addition, a pragmatic cascaded network structure with feature reuse and a two-step training strategy are presented to fully exploit network representation power. The proposed ADSeg method was evaluated on a multicenter dataset of 108 cases, with or without thrombus; ADSeg outperformed previous state-of-the-art methods by a significant margin and was robust against center variation.