Suppr超能文献

使用钥孔戚血蓝蛋白-神经节苷脂共轭物进行特异性免疫接种。

Specific immunization using keyhole limpet hemocyanin-ganglioside conjugates.

作者信息

Jennemann R, Gnewuch C, Bosslet S, Bauer B L, Wiegandt H

机构信息

Institut für Physiologische Chemie, Philipps-Universität, Marburg, Germany.

出版信息

J Biochem. 1994 Jun;115(6):1047-52. doi: 10.1093/oxfordjournals.jbchem.a124455.

Abstract

In a search for adjuvants of non-bacterial origin for immunization with ganglioside, we investigated whether chemical coupling to immune stimulatory protein could increase the immunogenicity of sialoglycosphingolipid. A novel method for the linkage of glycosphingolipids, including gangliosides, to protein was established. The procedure includes lysis of the sphingoid double bond by ozone, reduction of the ozonolysis product to the aldehyde, and coupling to amino groups, either directly by reductamination, or by conjugation via a long aliphatic chain dicarboxylic acid linker. Using this method, gangliosides Gfpt1 (IV2-Fuc-, II3NeuAc-Gg4Cer), Glac2 [II3(NeuAc)2-LacCer], and Gtet1 (II3NeuAc-Gg4Cer) were coupled to keyhole limpet hemocyanin (KLH), and the immunogenicity of the conjugates was tested. Immunization of mice with the KLH-ganglioside conjugates led in each case to the formation of IgG- and IgM antibodies that recognized the underivatized gangliosides, respectively. In contrast to this, mixtures of KLH and ganglioside proved ineffective for immunization. KLH-tumor-associated ganglioside conjugates may, therefore, be considered as possible vaccines in immune therapy of cancer.

摘要

为了寻找用于神经节苷脂免疫的非细菌来源佐剂,我们研究了与免疫刺激蛋白的化学偶联是否能增强唾液糖鞘脂的免疫原性。建立了一种将包括神经节苷脂在内的糖鞘脂与蛋白质连接的新方法。该过程包括用臭氧裂解鞘氨醇双键,将臭氧分解产物还原为醛,并通过直接还原胺化或通过长脂肪链二羧酸连接子进行偶联,使其与氨基结合。利用这种方法,将神经节苷脂Gfpt1(IV2-Fuc-,II3NeuAc-Gg4Cer)、Glac2 [II(NeuAc)2-LacCer]和Gtet1(II3NeuAc-Gg4Cer)与钥孔戚血蓝蛋白(KLH)偶联,并测试了偶联物的免疫原性。用KLH-神经节苷脂偶联物免疫小鼠,在每种情况下都分别导致了识别未衍生化神经节苷脂的IgG和IgM抗体的形成。与此相反,KLH和神经节苷脂的混合物被证明对免疫无效。因此,KLH-肿瘤相关神经节苷脂偶联物可被视为癌症免疫治疗中可能的疫苗。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验