毛皮
生物
内源性逆转录病毒
跨膜蛋白
生物信息学
遗传学
劈理(地质)
谱系(遗传)
细胞生物学
跨膜结构域
蛋白酶
基因组
蛋白质结构域
病毒包膜
点突变
计算生物学
人类基因组
内生
基因
灵长类动物
病毒学
外域
进化生物学
蛋白酵素
功能(生物学)
聚糖
CLPB公司
电池类型
膜蛋白
被膜
前病毒
合胞滋养细胞
HEK 293细胞
作者
Anthony Béguin,Marianne Chasseriaud,G A Hollaender,Yves Jacob,Guillaume Mousseau,Thiérry Heidmann,Odile Heidmann
标识
DOI:10.1073/pnas.2515527122
摘要
Endogenous retroviruses (ERV) represent 8 to 10% of mammalian genome. While most ERV are defective, a few retroviral genes, such as the envelope syncytins, were exapted during evolution and likely contributed to the emergence of placental mammals. We have previously identified the oldest full-length retroviral envelope gene in the human genome, HEMO (Human Endogenous MER34 ORF), endogenized in an ancestral mammal approximately 100 Mya. The transmembrane HEMO protein is predominantly expressed in the placenta and solid tumors. It is unexpectedly secreted from the cell surface as a soluble SHED form which can be detected in pregnant women blood. HEMO is nonfusogenic, having lost both its furin cleavage site between the SU and TM subunits, and its fusion peptide. To identify a potential receptor or partner of HEMO, we developed an original strategy leveraging the fusogenic property of the Measles virus proteins to screen a human ORFeome expression library by cell-cell fusion. We successfully identified the transmembrane aspartic protease BACE2 (ß-site APP-cleaving enzyme 2) as a specific interacting partner for both SHED and transmembrane HEMO proteins. We further determined the emergence time of this interaction, using in silico reconstructed ancestral "HEMO" sequences from several mammals. We identified two specific point mutations-resulting in two new cysteines in close proximity and loss of the SU-TM furin cleavage of HEMO-that appeared in the simian catarrhine lineage 30 to 45 Mya, and enabled interaction of this ancestral primate HEMO with BACE2, possibly for another physiological function prolonging HEMO conservation for tens of millions of years until humans.
科研通智能强力驱动
Strongly Powered by AbleSci AI