The increased plasma cell, IFN, Treg, and inflammatory cytokine signatures were most strongly related to the AA ancestral bias of having increased anti-RNP/SM autoantibodies and multiple autoantibodies. L-cysteine were built. Although standard therapy affected every gene signature and significantly increased myeloid cell signatures, logistic regression analysis determined that ancestral background significantly changed 23 of 34 gene signatures. Additionally, the strongest association to gene expression changes was found with autoantibodies, and this also had etiology in ancestry: the AA predisposition to have both RNP and dsDNA autoantibodies compared with EA predisposition to have only anti-dsDNA. A machine learning approach was used to determine a gene signature characteristic to distinguish AA SLE and was most influenced by genes characteristic of the perturbed B cell axis in AA SLE patients. and = 1566) GSVA scores were determined and compared with HC values to determine whether patients had increased (+1), decreased (C1), or normal (zero) L-cysteine values. GSVA enrichment gene symbols for each module are in Supplemental Table 5. (B and C) Percentage of patients within each ancestry (AA, = 216; NAA, = 232; EA, = 1118) with 1 (B) or 1 (C) SD GSVA scores for each cell type and process module. Fishers exact 0.05 are indicated by different color asterisk: black asterisks for comparisons between all 3, red asterisks between NAA and AA/EA, orange asterisks between NAA and EA, light blue asterisks between AA and EA, and dark blue asterisks between AA and NAA/EA. Exact values and percentages are listed in Supplemental Table 6. (D) WGCNA was carried out on data set “type”:”entrez-geo”,”attrs”:”text”:”GSE88884″,”term_id”:”88884″GSE88884 ILL1 and ILL2 cohorts separately. Pearson correlation values to ancestry were determined for each module and listed if 0.05. Weighted gene coexpression network analysis (WGCNA) confirmed L-cysteine the association of ancestry with cellular signatures. WGCNA of female patients from the 2 2 cohorts of data set “type”:”entrez-geo”,”attrs”:”text”:”GSE88884″,”term_id”:”88884″GSE88884 was carried out separately and demonstrated a significant positive correlation of AA ancestry to plasma cell, T cell, and Treg gene modules and a significant negative correlation to granulocyte and myeloid cell modules. NAA ancestry exhibited positive correlations to IGS, granulocyte, platelet, and erythrocyte modules and negative correlations to T cell and lymphocyte modules. EA ancestry was positively correlated to 1 1 myeloid cell module and negatively correlated to IGS, plasma cell, platelet, and erythrocyte modules (Figure 1D and Supplemental Table 8). This, an orthogonal approach using coexpression-defined gene Cd19 clusters, confirmed the ancestral-related gene expression differences. Ancestry provides the gene expression backbone for SLE gene expression abnormalities. Analyses of DEGs detected between different ancestries showed that AA populations had decreased expression of the Duffy blood group antigen in comparison with NAA and EA populations (Supplemental Table 4); these genes have previously been described as risk alleles resulting in decreased expression in AA (36C38). We hypothesized that ancestral-related gene expression differences detected between SLE patients may be related to heritable differences in expressed genes in L-cysteine hematopoietic cells of healthy subjects. In order to address this question, DE analysis was carried out between AA and EA healthy subjects from 2 separate data sets (Supplemental Table 9) and compared with the DEGs that differed between AA to EA SLE patients. There was a highly significant overlap in transcripts differentially expressed between healthy AA and EA subjects and transcripts differentially expressed between AA and EA SLE patients (Figure 2A). GSVA was carried out on the healthy AA and EA subjects, and enrichment scores were compared for the 34 cell and process modules. Ten of the 34 signatures were significantly different between AA and EA healthy subjects. Healthy EA subjects had significantly increased granulocyte, inflammasome, monocyte cell surface, monocyte, inflammatory secreted, and DC GSVA enrichment scores compared with AA healthy subjects, and AA healthy subjects demonstrated significantly increased T cellCactivated, B cell, erythrocyte, and platelet GSVA enrichment scores compared with healthy EA subjects. No differences in LDG, plasma cell, T cell, IGS, or the other signatures were determined (Figure 2B). Thus, in the absence of disease, significant and reproducible gene expression differences exist between AA and EA and appear to be contributing to the molecular heterogeneity in gene expression. Open in a separate window Figure 2 Gene expression differences in SLE patients are similar to ancestral gene expression differences in healthy controls.(A) Limma DE analysis was carried out between HC AA and EA for 2 separate data sets (Supplemental Table 9). Increased (Up in AA) and decreased (Up in EA) transcripts were compared with 4 SLE cohorts of AA DE to EA. Overlap values were all below 1 10C22 for OR above 1. (B) GSVA for the 34 cell and process modules was carried out on healthy AA and EA subjects from 2 separate data sets. Welchs test was used to determine significant differences.