Blood Genomics Identifies Three Subtypes of Systemic Lupus Erythematosus: “IFN-High,” “NE-High,” and “Mixed”

• Published in: Mediators of inflammation 2021, Vol.2021, p.6660164-12
• Main Authors: Cui, Mintian, Li, Taotao, Yan, Xinwei, Wang, Chao, Shen, Qi, Ren, Hongbiao, Li, Liangshuang, Zhang, Ruijie
• Format: Article
• Language: English
• Subjects: Adult; Analysis; Autoantibodies; Autoimmune diseases; Autoimmunity; B cells; Big Data; Care and treatment; Child; Cytokines; Datasets; Development and progression; Diagnosis; Disease; Drug development; Extracellular Traps; Gene expression; Genes; Genomics; Humans; Immune system; Immunosuppressive agents; Interferon; Interferon-alpha - metabolism; Interferon-alpha - therapeutic use; Leukocytes (neutrophilic); Lupus; Lupus Erythematosus, Systemic; Lymphocytes; Molecular modelling; Neutrophils; Neutrophils - metabolism; Pathogenesis; Pathogens; Patients; Pediatrics; Systemic lupus erythematosus; Transcriptomes; Variables; Virus diseases; α-Interferon
• ISSN: 0962-9351; 1466-1861
• DOI: 10.1155/2021/6660164

Purpose. Systemic lupus erythematosus (SLE) is a systemic and multifactorial autoimmune disease, and its diverse clinical manifestations affect molecular diagnosis and drug benefits. Our study was aimed at defining the SLE subtypes based on blood transcriptome data, analyzing functional patterns, and elucidating drug benefits. Methods. Three data sets were used in this paper that were collected from the Gene Expression Omnibus (GEO) database, which contained two published data sets of pediatric and adult SLE patients (GSE65391, GSE49454) and public longitudinal data (GSE72754) from a cohort of SLE patients treated with IFN-α Kinoid (IFN-K). Based on disease activity scores and gene expression data, we defined a global SLE signature and merged three clustering algorithms to develop a single-sample subtype classifier (SSC). Systematic analysis of coexpression networks based on modules revealed the molecular mechanism for each subtype. Results. We identified 92 genes as a signature of the SLE subtypes and three intrinsic subsets (“IFN-high,” “NE-high,” and “mixed”), which varied in disease severity. We speculated that IFN-high might be due to the overproduction of interferons (IFNs) caused by viral infection, leading to the formation of autoantibodies. NE-high might primarily result from bacterial and fungal infections that stimulated neutrophils (NE) to produce neutrophil extracellular traps (NETs) and induced individual autoimmune responses. The mixed type contained both of these molecular mechanisms and showed an intrinsic connection. Conclusions. Our research results indicated that identifying the molecular mechanism associated with different SLE subtypes would benefit the molecular diagnosis and stratified therapy. Moreover, repositioning of IFN-K based on subtypes also revealed an improved therapeutic effect, providing a new direction for disease treatment and drug development.