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

• Published in: PloS one 2018-12, Vol.13 (12), p.e0208132-e0208132
• Main Authors: Labonte, Adam C, Kegerreis, Brian, Geraci, Nicholas S, Bachali, Prathyusha, Madamanchi, Sushma, Robl, Robert, Catalina, Michelle D, Lipsky, Peter E, Grammer, Amrie C
• Format: Article
• Language: English
• Subjects: Abnormalities; Antigens; Autoimmune diseases; Biology and Life Sciences; CD163 antigen; Cell activation; Cellular communication; Chemokines; Chronic conditions; Comparative analysis; Computational Biology; Cytokines; Datasets; Datasets as Topic; Disease; Gene expression; Gene Expression Profiling; Gene Expression Regulation - immunology; Gene Regulatory Networks - immunology; Genes; Genetic aspects; Humans; Infections; Inflammation; Learning algorithms; Lupus; Lupus Erythematosus, Systemic - blood; Lupus Erythematosus, Systemic - genetics; Lupus Erythematosus, Systemic - immunology; Lymphocytes; Lymphocytes B; Lymphocytes T; Machine Learning; Macrophage Activation - genetics; Macrophages; Macrophages - immunology; Macrophages - metabolism; Mc gene; Medicine and Health Sciences; Metabolism; Modules; Myeloid cells; Network analysis; Patients; Phenotypes; Remission; Rheumatology; Signatures; Smart firearms; Stat1 protein; Stat3 protein; Stat6 protein; Statistical models; Stem cells; Symptom Flare Up; Systemic lupus erythematosus; Transcriptome - immunology; Viral infections
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0208132

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.