生物
内质网
细胞生物学
高尔基体
突变体
跨膜结构域
跨膜蛋白
基因
病毒学
遗传学
受体
作者
Małgorzata Graul,Edyta Kisielnicka,Michał Rychłowski,Marieke C. Verweij,Kurt Tobler,Mathias Ackermann,Emmanuel J. H. J. Wiertz,Krystyna Bieńkowska-Szewczyk,Andrea D. Lipińska
摘要
Bovine herpesvirus 1 (BoHV-1)-encoded UL49.5 (a homologue of herpesvirus glycoprotein N) can combine different functions, regulated by complex formation with viral glycoprotein M (gM). We aimed to identify the mechanisms governing the immunomodulatory activity of BoHV-1 UL49.5. In this study, we addressed the impact of gM/UL49.5-specific regions on heterodimer formation, folding and trafficking from the endoplasmic reticulum (ER) to the trans-Golgi network (TGN) – events previously found to be responsible for abrogation of the UL49.5-mediated inhibition of the transporter associated with antigen processing (TAP). We first established, using viral mutants, that no other viral protein could efficiently compensate for the chaperone function of UL49.5 within the complex. The cytoplasmic tail of gM, containing putative trafficking signals, was dispensable either for ER retention of gM or for the release of the complex. We constructed cell lines with stable co-expression of BoHV-1 gM with chimeric UL49.5 variants, composed of the BoHV-1 N-terminal domain fused to the transmembrane region (TM) from UL49.5 of varicella-zoster virus or TM and the cytoplasmic tail of influenza virus haemagglutinin. Those membrane-anchored N-terminal domains of UL49.5 were sufficient to form a complex, yet gM/UL49.5 folding and ER–TGN trafficking could be affected by the UL49.5 TM sequence. Finally, we found that leucine substitutions in putative glycine zipper motifs within TM helices of gM resulted in strong reduction of complex formation and decreased ability of gM to interfere with UL49.5-mediated major histocompatibility class I downregulation. These findings highlight the importance of gM/UL49.5 transmembrane domains for the biology of this conserved herpesvirus protein complex.
科研通智能强力驱动
Strongly Powered by AbleSci AI