Session Type: Abstract Submissions (ACR)
Background/Purpose: Systemic lupus erythematosus (SLE) is a complex multi-system autoimmune disease of uncertain etiology. Different ancestral backgrounds demonstrate different clinical manifestations and autoantibody profiles. Whole blood gene expression studies in SLE have demonstrated a prominent type I interferon (IFN) signature. Presumably different cell types will exhibit different patterns of gene expression, and in this study we sorted major immune cell populations from PBMC and examined genome-wide transcription patterns in cell subsets and in different ancestral backgrounds.
Methods: Peripheral blood was collected from 21 African-American (AA) and 21 European-American (EA) SLE patients, 5 AA controls, and 5 EA controls. CD4+ T-cells, CD8+ T-cells, monocytes and B cells were purified by flow sorting. Each cell population from each subject was run on an Illumina HumanHT-12 V4 expression BeadChip array. The raw data were filtered to exclude the non-detected genes. Quantile normalization and Log2 transformation were applied prior to between-group comparisons. Differentially expressed genes (DEGs) were determined by comparing cases and controls of the same ancestral background, and then DEG lists were compared between cell types and between ancestral backgrounds. Because IFN-related gene expression differed between cell types, we also developed a novel metric to encompass IFN-induced gene expression more globally and comprehensively than pathway analyses, generating a quantitative metric.
Results: While we observed approximately 1000 DEGs in each cell type that was isolated, the overlap in DEG lists between different cell types was very modest (<1%), supporting the idea that different transcripts are upregulated in different cell types. Typically between 5 to 10% of DEGs were shared when comparing the same cell type in different ancestral backgrounds (for ex. CD20 AA vs. CD20 EA). Interestingly, gene expression profiles different cell types from the same subject exhibited individualized patterns on unsupervised hierarchical clustering, supporting some cell-type independent inter-individual variations. Quantitative measurement of global IFN-induced gene expression revealed that AA subjects demonstrated more concordance across all studied cell types in IFN-induced gene expression. In EA subjects, a subset of patients demonstrated increased IFN-induced gene expression in all lymphocyte populations but not monocytes, and another subgroup demonstrated IFN-induced gene expression in monocytes and B-cells but not in CD4 or CD8 T-cells.
Conclusion: We find fascinating differences in gene expression between different immune cell populations and between ancestral backgrounds in SLE patients. It seems that the IFN signature is relatively diverse, affecting different cell populations in different ways, and behaving differently in EA vs. AA patients. These data may impact efforts to target this pathway therapeutically.
T. B. Niewold,
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ACR Meeting Abstracts - https://acrabstracts.org/abstract/genome-wide-transcriptional-profiling-of-isolated-immune-cell-populations-from-sle-patients-with-different-ancestral-backgrounds/