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微生物超抗原在系统性自身免疫病发病机制中的潜在作用。

A potential role for microbial superantigens in the pathogenesis of systemic autoimmune disease.

作者信息

Friedman S M, Posnett D N, Tumang J R, Cole B C, Crow M K

机构信息

Cornell University Medical College, New York, New York.

出版信息

Arthritis Rheum. 1991 Apr;34(4):468-80. doi: 10.1002/art.1780340412.

Abstract

We have attempted herein to demonstrate how microbial superantigens could promote an abnormal form of "cognate" T helper-B cell interaction, analogous to that which may occur during GVH disease, leading to B cell activation and systemic autoimmunity. In vitro studies performed at our laboratory and others have demonstrated that resting human B cells bind microbial superantigens and present them to superantigen-reactive autologous T helper cells, resulting in T cell activation and polyclonal IgM and IgG production by the superantigen-bearing B cells. In vitro studies of microbial superantigen-mediated murine T helper-B cell interactions demonstrate preferential help for B cells that have encountered specific antigen. Both in humans and in mice, the cellular interactions involved and the B cell responses induced are highly analogous to those mediated by allospecific T helper-B cell interaction. Finally, the results of studies carried out on T cell-deficient (nude) mice suggest that microbial superantigens may trigger similar T helper cell-dependent polyclonal IgM and IgG responses in vivo. These mice will be studied over time and tested for the development of autoantibodies characteristic of SLE and of autoimmune organ system damage, the occurrence of which are predicted by our model.

摘要

我们在此试图证明微生物超抗原如何促进一种异常形式的“同源”T辅助细胞与B细胞的相互作用,类似于移植物抗宿主病(GVH病)期间可能发生的情况,从而导致B细胞活化和全身性自身免疫。我们实验室及其他机构进行的体外研究表明,静息的人B细胞可结合微生物超抗原,并将其呈递给超抗原反应性自体T辅助细胞,导致T细胞活化以及携带超抗原的B细胞产生多克隆IgM和IgG。微生物超抗原介导的鼠T辅助细胞与B细胞相互作用的体外研究表明,对遇到特定抗原的B细胞有优先辅助作用。在人和小鼠中,所涉及的细胞相互作用以及诱导的B细胞反应都与同种异体特异性T辅助细胞与B细胞相互作用介导的情况高度相似。最后,对T细胞缺陷(裸)小鼠进行的研究结果表明,微生物超抗原可能在体内引发类似的T辅助细胞依赖性多克隆IgM和IgG反应。将对这些小鼠进行长期研究,并检测是否出现系统性红斑狼疮(SLE)特征性自身抗体以及自身免疫性器官系统损伤,我们的模型预测了这些情况的发生。

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