在T细胞受体谱中确定特异性组
Jacob Glanville1,2, Huang Huang2,3, Allison Nau2,3
1Computational and Systems Immunology Program, Stanford University School of Medicine, Stanford, California 94305, USA.
Nature
|June 22, 2017
概括
GLIPH算法通过识别保存的基因来聚合T细胞受体 (TCR) 序列,使得具有共享抗原特异性的TCR能够可靠地分组,并加速免疫反应分析.
科学领域:
- 免疫学
- 生物信息学
- 结构生物学
背景情况:
- T细胞受体 (TCR) 序列表现出极大的多样性,超过一个人的T细胞数量.
- 了解TCR抗原的特异性对于解读免疫反应至关重要.
研究的目的:
- 确定TCR抗原特异性的最低要求.
- 开发一个计算工具,以集群具有共同特异性的TCR.
主要方法:
- 使用主要基因相容性复合物 (pMHC) -四聚体排序的细胞和结构数据分析TCR序列.
- 开发和应用GLIPH算法 (通过帕拉托普热点对淋巴细胞相互作用进行分组).
- 通过使用Mycobacterium结核病感染个体的TCR序列进行独立验证.
主要成果:
- GLIPH算法可靠地将来自不同捐赠者的共同特异性的TCR组合在一起.
- 在TCR中保存的互补性决定区域3 (CDR3) 动机被确定为抗原识别的关键.
- 对Mycobacterium结核病反应T细胞的分析确定了141个TCR特异性组,其中16个是多个个体共享的.
结论:
- GLIPH算法有效地分析大型TCR序列数据集,以定义共享的特异性组.
- 这种方法加快了T细胞反应的分析和特定的主要基因相容复合体 (pMHC) 配体的识别.
- GLIPH促进了识别特定抗原的TCR的发现,有助于免疫疗法和诊断的开发.
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