Abstract
BackgroundWe performed expression quantitative trait locus (eQTL) analysis in single classical (CL) and non-classical (NCL) monocytes from patients with systemic lupus erythematosus (SLE) to quantify the impact of well-established genetic risk alleles on transcription at single-cell resolution.MethodsSingle-cell gene expression was quantified using qPCR in purified monocyte subpopulations (CD14++CD16− CL and CD14dimCD16+ NCL) from SLE patients. Novel analysis methods were used to control for the within-person correlations observed, and eQTLs were compared between cell types and risk alleles.ResultsThe SLE-risk alleles demonstrated significantly more eQTLs in NCLs as compared to CLs (p = 0.0004). There were 18 eQTLs exclusive to NCL cells, 5 eQTLs exclusive to CL cells, and only one shared eQTL, supporting large differences in the impact of the risk alleles between these monocyte subsets. The SPP1 and TNFAIP3 loci were associated with the greatest number of transcripts. Patterns of shared influence in which different SNPs impacted the same transcript also differed between monocyte subsets, with greater evidence for synergy in NCL cells. IRF1 expression demonstrated an on/off pattern, in which expression was zero in all of the monocytes studied from some individuals, and this pattern was associated with a number of SLE risk alleles. We observed corroborating evidence of this IRF1 expression pattern in public data sets.ConclusionsWe document multiple SLE-risk allele eQTLs in single monocytes which differ greatly between CL and NCL subsets. These data support the importance of the SPP1 and TNFAIP3 risk variants and the IRF1 transcript in SLE patient monocyte function.
Highlights
Systemic lupus erythematosus (SLE) is a poorly understood autoimmune syndrome driven by the interplay of genetic and environmental influences, which lead to a break in immunologic self-tolerance
Unique expression quantitative trait locus (eQTL) associations between CL and non-clas‐ sical (NCL) monocytes Using the four different analysis methods to query the data resulted in a total of 25 eQTL associations meeting a FDR < 0.1 (Table 1, Fig. 1)
For a given single nucleotide polymorphisms (SNPs), the eQTL associated transcripts largely differed between cell types, with only one transcript-eQTL shared between CL and NCL cells (SPP1 rs9138 with the Interferon regulatory factor 1 (IRF1) transcript)
Summary
Systemic lupus erythematosus (SLE) is a poorly understood autoimmune syndrome driven by the interplay of genetic and environmental influences, which lead to a break in immunologic self-tolerance. Most of the genetic polymorphisms associated with SLE are not coding-change variants [3, 4] They are either located in non-coding regulatory regions near the 5′ and 3′ regions of genes, in DNAse hyper-sensitivity sites, or are in perfect LD with DNAse hypersensitivity sites. This suggests modulation of transcription as a likely mechanism by which many SLE-risk loci impact immune system biology [2], and data from many complex diseases support this idea [5]. We performed expression quantitative trait locus (eQTL) analysis in single classical (CL) and non-clas‐ sical (NCL) monocytes from patients with systemic lupus erythematosus (SLE) to quantify the impact of well-estab‐ lished genetic risk alleles on transcription at single-cell resolution
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