Multi-cell type gene coexpression network analysis reveals coordinated interferon response and cross-cell type correlations in systemic lupus erythematosus.
Multi-cell type gene coexpression network analysis reveals coordinated interferon response and cross-cell type correlations in systemic lupus erythematosus.
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DOI：
10.1101/gr.265249.120
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发表时间：
2021-04
期刊：
影响因子：
7
通讯作者：
Ay F
中科院分区：
文献类型：
--
作者：
Panwar B;Schmiedel BJ;Liang S;White B;Rodriguez E;Kalunian K;McKnight AJ;Soloff R;Seumois G;Vijayanand P;Ay F
系统性红斑狼疮（SLE）是一种无法治愈的自身免疫性疾病，不成比例地影响妇女。在寻找SLE靶向治疗的一个主要障碍是其临床表现的显著异质性以及不同细胞类型的参与。为了确定细胞特异性靶标以及不同细胞类型表达程序之间的交叉相关关系，我们在这里分析了SLE患者血液中的六种主要循环免疫细胞类型。我们的研究结果表明，干扰素应答特征的存在将患者分为两个不同的组（IFNneg与IFNpos）。使用差异基因表达和差异基因共表达分析比较这两组，我们优先考虑来自经典单核细胞的相对较少的基因列表，包括两种已知的免疫调节剂：TNFSF 13 B/BAFF（贝利木单抗的靶点，一种获批的SLE治疗剂）和IL 1 RN（阿那白滞素的基础，一种类风湿性关节炎治疗剂）。然后，我们开发了一个多细胞类型的加权基因共表达网络分析（WGCNA）框架的扩展，称为mWGCNA。将mWGCNA应用于来自六个分选的免疫细胞群体的RNA-seq数据（15例SLE，10例健康供体），我们鉴定了所有细胞类型中与干扰素刺激基因（ISG）的共表达模块，以及将特异性T辅助细胞标志物的表达与B细胞应答以及与来自骨髓细胞的TNFSF 13 B表达联系起来的跨细胞类型相关性，所有这些又与IFN-β患者的疾病严重程度相关。我们的研究结果证明了一种无假设和数据驱动的方法的力量，可以发现药物靶点，并揭示SLE中细胞类型之间的新交叉相关性，并对其他自身免疫性疾病产生影响。
Systemic lupus erythematosus (SLE) is an incurable autoimmune disease disproportionately affecting women. A major obstacle in finding targeted therapies for SLE is its remarkable heterogeneity in clinical manifestations as well as in the involvement of distinct cell types. To identify cell-specific targets as well as cross-correlation relationships among expression programs of different cell types, we here analyze six major circulating immune cell types from SLE patient blood. Our results show that presence of an interferon response signature stratifies patients into two distinct groups (IFNneg vs. IFNpos). Comparing these two groups using differential gene expression and differential gene coexpression analysis, we prioritize a relatively small list of genes from classical monocytes including two known immune modulators: TNFSF13B/BAFF (target of belimumab, an approved therapeutic for SLE) and IL1RN (the basis of anakinra, a therapeutic for rheumatoid arthritis). We then develop a multi–cell type extension of the weighted gene coexpression network analysis (WGCNA) framework, termed mWGCNA. Applying mWGCNA to RNA-seq data from six sorted immune cell populations (15 SLE, 10 healthy donors), we identify a coexpression module with interferon-stimulated genes (ISGs) among all cell types and a cross–cell type correlation linking expression of specific T helper cell markers to B cell response as well as to TNFSF13B expression from myeloid cells, all of which in turn correlates with disease severity of IFNpos patients. Our results demonstrate the power of a hypothesis-free and data-driven approach to discover drug targets and to reveal novel cross-correlation across cell types in SLE with implications for other autoimmune diseases.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
Jvenn：交互式Venn图观看器。
影响因子：
3
作者：
Bardou P;Mariette J;Escudié F;Djemiel C;Klopp C
通讯作者：
Klopp C
影响因子：
2.6
作者：
Cui, Y. X.;Fu, C. W.;Meng, W.
通讯作者：
Meng, W.
影响因子：
11
作者：
Devarapu SK;Anders HJ
通讯作者：
Anders HJ
影响因子：
8
作者：
Bocharnikov, Alexandra, V;Keegan, Joshua;Rao, Deepak A.
通讯作者：
Rao, Deepak A.