Gene regulatory network inference from CRISPR perturbations in primary CD4+ T cells elucidates the genomic basis of immune disease.

IF 11.1 Q1 CELL BIOLOGY Cell genomics Pub Date : 2024-10-10 DOI:10.1016/j.xgen.2024.100671
Joshua S Weinstock, Maya M Arce, Jacob W Freimer, Mineto Ota, Alexander Marson, Alexis Battle, Jonathan K Pritchard
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Abstract

The effects of genetic variation on complex traits act mainly through changes in gene regulation. Although many genetic variants have been linked to target genes in cis, the trans-regulatory cascade mediating their effects remains largely uncharacterized. Mapping trans-regulators based on natural genetic variation has been challenging due to small effects, but experimental perturbations offer a complementary approach. Using CRISPR, we knocked out 84 genes in primary CD4+ T cells, targeting inborn error of immunity (IEI) disease transcription factors (TFs) and TFs without immune disease association. We developed a novel gene network inference method called linear latent causal Bayes (LLCB) to estimate the network from perturbation data and observed 211 regulatory connections between genes. We characterized programs affected by the TFs, which we associated with immune genome-wide association study (GWAS) genes, finding that JAK-STAT family members are regulated by KMT2A, an epigenetic regulator. These analyses reveal the trans-regulatory cascades linking GWAS genes to signaling pathways.

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从原代 CD4+ T 细胞的 CRISPR 干扰推断基因调控网络,阐明免疫疾病的基因组基础。
遗传变异对复杂性状的影响主要是通过基因调控的变化产生的。虽然许多基因变异与顺式目标基因有关,但介导其效应的反式调控级联在很大程度上仍未定性。由于影响较小,基于自然遗传变异绘制跨调控因子图谱一直具有挑战性,但实验扰动提供了一种补充方法。我们利用CRISPR技术敲除了原代CD4+ T细胞中的84个基因,靶向先天性免疫错误(IEI)疾病转录因子(TFs)和与免疫疾病无关的TFs。我们开发了一种名为线性潜在因果贝叶斯(LLCB)的新型基因网络推断方法,以从扰动数据中估计网络,并观察到基因之间有211个调控连接。我们描述了受TFs影响的程序,并将其与免疫全基因组关联研究(GWAS)基因联系起来,发现JAK-STAT家族成员受表观遗传调控因子KMT2A的调控。这些分析揭示了连接全基因组关联研究(GWAS)基因与信号通路的跨调节级联。
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