Abstract
Systemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups. We leverage cell-sorted RNA-seq data (CD14+ monocytes, B cells, CD4+ T cells, and NK cells) from 120 SLE patients (63 Asian and 57 White individuals) and apply a four-tier approach including unsupervised clustering, differential expression analyses, gene co-expression analyses, and machine learning to identify SLE subgroups within this multiethnic cohort. K-means clustering on each cell-type resulted in three clusters for CD4 and CD14, and two for B and NK cells. To understand the identified clusters, correlation analysis revealed significant positive associations between the clusters and clinical parameters including disease activity as well as ethnicity. We then explored differentially expressed genes between Asian and White groups for each cell-type. The shared differentially expressed genes across cells were involved in SLE or other autoimmune-related pathways. Co-expression analysis identified similarly regulated genes across samples and grouped these genes into modules. Finally, random forest classification of disease activity in the White and Asian cohorts showed the best classification in CD4+ T cells in White individuals. The results from these analyses will help stratify patients based on their gene expression signatures to enable SLE precision medicine.
Highlights
Systemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups
After profiling 120 SLE patients for cell sorted bulk RNA-seq data (CD4+ T cells, CD14+ monocytes, B cells, and NK cells) from a multi-racial/ethnic cohort, a total of 10% of the samples were removed after QC filtering retaining 415 samples (91 NK, 105 B cells, 108 CD4+ Tcells, and 111 CD14+ monocytes) (Supplementary Fig. 1A)
Using the QC’ed data, we wanted to look for specifictranscriptomic effects in each of these cell types using a four-tier approach which included: unsupervised clustering; differential expression analyses, gene co-expression analyses, and machine learning (Fig. 1)
Summary
Systemic lupus erythematosus (SLE) is an autoimmune disease in which outcomes vary among different racial groups. Previous studies have used machine learning (ML) and clustering approaches to try to stratify patients with SLE based on different parameters including clinical data, expression quantitative trait loci (eQTLs), methylation, and transcriptomic data[12,13,14,15]. These studies have been mainly conducted on whole blood or peripheral blood mononuclear cells (PBMCs), not considering the involvement of different immune cell types in disease, and in White cohorts, without taking into account the relationship between disease activity and ethnic background
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