信号灯
生物
表型
遗传学
抗原
等位基因
基因
受体
作者
Axel Seltsam,Susanne Strigens,C. Levene,Vered Yahalom,Marilyn Moulds,John J. Moulds,Hein Hustinx,Volker Weisbach,Dolores Figueroa,Christina Bade‐Doeding,David S. DeLuca,Rainer Blasczyk
出处
期刊:Transfusion
[Wiley]
日期:2006-12-20
卷期号:47 (1): 133-146
被引量:33
标识
DOI:10.1111/j.1537-2995.2007.01076.x
摘要
BACKGROUND: Semaphorin 7A (Sema7A), the protein that carries the JMH blood group antigen, is involved in immune responses and plays an important role in axon growth and guidance. Because previous serologic studies on red blood cells (RBCs) suggested a considerable diversity of Sema7A, the present study was designed to elucidate the complex picture of the molecular diversity of this protein. STUDY DESIGN AND METHODS: The JMH antigen status was determined by serology, flow cytometry, and Western blot. Genomic and transcript analysis of SEMA7A was performed by nucleotide sequencing. Recombinant Sema7A proteins were used for genotype‐phenotype correlation. A three‐dimensional model of Sema7A was generated for topologic analyses. RESULTS: Our studies on 44 individuals with unusual JMH phenotypes and their family members revealed that aberrant Sema7A expression can be an inherited or an acquired phenomenon and is based on reduced surface expression or qualitative changes in Sema7A. These different phenotypes are caused by variations of the SEMA7A gene or seem to be generated by autoimmune‐related or RBC lineage–specific mechanisms. The variant JMH phenotypes were related to the presence of missense mutations in SEMA7A , predicting amino acid changes in the semaphorin domain of Sema7A. Sequence analysis of the variant SEMA7A alleles revealed mutations affecting codons 207 and 460/461. Topologic analyses showed that Sema7A polymorphisms were prominently located on the top and bottom of the semaphorin domain, suggesting a functional relevance of these sites. CONCLUSION: These findings provide a basis with which to delineate the various ligand‐binding surfaces of Sema7A.
科研通智能强力驱动
Strongly Powered by AbleSci AI