Identification of alterations in macrophage activation associated with disease activity in systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is characterized by abnormalities in B cell and T cell function, but the role of disturbances in the activation status of macrophages (Mϕ) has not been well described in human patients. To address this, gene expression profiles from isolated lymphoid and myeloid populations were analyzed to identify differentially expressed (DE) genes between healthy controls and patients with either inactive or active SLE. While hundreds of DE genes were identified in B and T cells of active SLE patients, there were no DE genes found in B or T cells from patients with inactive SLE compared to healthy controls. In contrast, large numbers of DE genes were found in myeloid cells (MC) from both active and inactive SLE patients. Among the DE genes were several known to play roles in Mϕ activation and polarization, including the M1 genes STAT1 and SOCS3 and the M2 genes STAT3, STAT6, and CD163. M1-associated genes were far more frequent in data sets from active versus inactive SLE patients. To characterize the relationship between Mϕ activation and disease activity in greater detail, weighted gene co-expression network analysis (WGCNA) was used to identify modules of genes associated with clinical activity in SLE patients. Among these were disease activity-correlated modules containing activation signatures of predominantly M1-associated genes. No disease activity-correlated modules were enriched in M2-associated genes. Pathway and upstream regulator analysis of DE genes from both active and inactive SLE MC were cross-referenced with high-scoring hits from the drug discovery Library of Integrated Network-based Cellular Signatures (LINCS) to identify new strategies to treat both stages of SLE. A machine learning approach employing MC gene modules and a generalized linear model was able to predict the disease activity status in unrelated gene expression data sets. In summary, altered MC gene expression is characteristic of both active and inactive SLE. However, disease activity is associated with an alteration in the activation of MC, with a bias toward the M1 proinflammatory phenotype. These data suggest that while hyperactivity of B cells and T cells is associated with active SLE, MC potentially direct flare-ups and remission by altering their activation status toward the M1 state.

系统性红斑狼疮（SLE）以B细胞（B cell）和T细胞（T cell）功能异常为特征，但巨噬细胞（macrophages, Mϕ）激活状态紊乱在人类患者中的作用尚未得到充分阐明。为解决这一问题，研究人员对分离得到的淋巴样与髓系细胞群的基因表达谱进行分析，以鉴定健康对照与静息型或活动型SLE患者之间的差异表达（differentially expressed, DE）基因。尽管在活动型SLE患者的B细胞与T细胞中鉴定出了数百个差异表达基因，但与健康对照相比，静息型SLE患者的B或T细胞中未发现任何差异表达基因。与之形成对比的是，在活动型与静息型SLE患者的髓系细胞（myeloid cells, MC）中均发现了大量差异表达基因。其中若干已知参与巨噬细胞激活与极化的基因，包括M1型相关基因STAT1、SOCS3以及M2型相关基因STAT3、STAT6和CD163，均位列差异表达基因之中。与M2型相关基因相比，M1型相关基因在活动型SLE患者的数据集内出现频率更高。为更细致地表征巨噬细胞激活与疾病活动度之间的关联，研究人员采用加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA），鉴定出与SLE患者临床活动度相关的基因模块。其中包含以M1型相关基因激活特征为主的疾病活动度关联模块，未发现任何富集M2型相关基因的疾病活动度关联模块。研究人员将活动型与静息型SLE患者髓系细胞的差异表达基因进行通路与上游调控因子分析，并与药物发现领域的整合网络基元细胞信号库（Library of Integrated Network-based Cellular Signatures, LINCS）中的高分靶点进行交叉比对，以识别可用于治疗SLE两个疾病阶段的新策略。采用髓系细胞基因模块与广义线性模型的机器学习方法，能够在无关基因表达数据集内预测疾病活动状态。综上所述，髓系细胞基因表达改变是活动型与静息型SLE的共同特征。然而，疾病活动度与髓系细胞的激活改变密切相关，且偏向M1型促炎表型。上述数据表明，尽管B细胞与T细胞的过度激活与活动型SLE相关，但髓系细胞可能通过将自身激活状态转向M1型，从而介导疾病发作与缓解。