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在模型指导识别与免疫失调相关的新型JAK1变异

Marie Jeanpierre¹, Orianne Debeaupuis^1,2, Camille Brunaud¹

¹Université Paris Cité, Institut Imagine, Laboratoire d'immunogénétique des maladies autoimmunes pédiatriques, INSERM UMR1163, Paris, France.

EMBO molecular medicine

|October 24, 2025

概括

研究人员使用基于结构的AI模型确定了导致免疫缺陷的新型JAK1基因变异. 用托法西提尼布治疗改善了患者的症状,证明了针对JAK1功能增益障碍的向治疗的潜力.

科学领域:

遗传学和分子生物学
免疫学免疫学免疫学
计算生物学计算生物学

背景情况:

初级免疫失调和缺乏症疾病与JAK/STAT通路中的过度激活变体有关.
JAK1蛋白对免疫细胞的发育和反应至关重要,使其变体成为研究的关键领域.

研究的目的:

通过基于结构的预测框架,系统地评估致病的Janus Kinase 1 (JAK1) 变体.
在怀疑原发性免疫失调和缺陷的患者中识别新的JAK1变异.

主要方法:

开发了一个预测框架,适应AlphaFold2来建模JAK1.1的活性和非活性构造.
在21,926个JAK1变体上进行了双态建模,以区分致病性和良性.
将框架应用于患者队列,确定了五种新的功能增益变体.

主要成果:

确定了五种新的JAK1变异,预计在关键的监管和催化领域具有功能获取活性.
这些变异的子宫外表达导致细胞系中JAK1和STAT过活化.
在两名患者中,托法西提尼布治疗使细胞因子水平正常化,干扰素标志和临床症状改善.

结论:

已经揭示了JAK1功能获取 (GOF) 的多种机制,扩大了这些疾病的临床谱.
精确的JAK1变异的特征对于开发有效的个性化疗法至关重要.
基于结构的AI建模为识别和理解致病变体提供了强大的工具.

关键词:

免疫系统的调节失调.雅克抑制剂的使用方法詹姆斯1的第一名.个性化医疗是个性化的医疗.蛋白质建模模型

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