Project description:Background. The etiology and reasons underlying the ethnic disparities in systemic sclerosis (SSc) remain unknown. African-Americans (AA) are disproportionally affected by SSc, yet dramatically underrepresented in research. The role of DNA methylation in disease risk remains unclear. This analysis was conducted to comprehensive identify differentially methylated loci associated with SSc in AA. Methods. Genomic DNA was isolated from cultured dermal fibroblasts isolated from 15 AA SSc cases and 15 AA controls. All patients met the 2013 ACR/EULAR classification criteria for SSc, most (93%) presenting with diffuse cutaneous SSc. DNA methylation patterns were profiled through reduced representation bisulfite sequencing (RRBS). Alignment and methylation calling were performed using Bismarck v0.16.3 and the GRCh37/hg19 reference genome. Data was filtered, normalized, and analyzed with RnBeads v1.6.1. Differential methylation analysis was conducted on CpG, promoter, gene and system level. Results. We generated DNA methylation data for over 5 million CpGs in each sample and between 10x to 40x coverage in CpGs sites. Using the Combined Score approach implemented in RnBeads, a total of 9 CpG islands, 17 genes and 11 promoters showed significant differential methylation levels between cases and controls at the gene-level. The top differentially methylated genes constitute mostly non-coding RNA genes (42%), followed by pseudogenes (27%), then protein coding genes (19%). Enrichment analysis revealed that both hypo- and hypermethylated genes and their promoter regions were enriched for cell differentiation and immune-related gene ontology terms (hypermethylated regions: mesenchymal cell transition and differentiation; hypomethylated regions: type I IFN signaling). Conclusion. The observed DNA methylation differences between cases and controls support a modest role for DNA methylation differences in mediating susceptibility to SSc in AA. Instead of protein-coding, most differentially methylated genes comprise non-coding RNA genes, supporting a larger contribution of dysregulated regulatory elements to disease. While previous DNA methylation profiling analyses in European-Americans reported an enrichment of extracellular matrix and focal adhesion genes, our data supports a potentially stronger immune-driven etiology in AA.

Project description:Background/Purpose: Systemic lupus erythematosus (SLE) is a complex multi-system autoimmune disease of uncertain etiology. Patients from different ancestral backgrounds demonstrate differences in clinical manifestations and autoantibody profiles. In this study we examined genome-wide transcriptional patterns in major immune cell subsets across 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 subset from each subject was run on an Illumina HumanHT-12 V4 expression BeadChip array (n=208 arrays). Differentially expressed genes (DEGs) were determined by comparing cases and controls of the same ancestral background. Results: The overlap in DEG lists between different cell types from the same ancestral background was very modest (<1%). Typically between 5-10% of DEGs were shared when comparing the same cell type between different ancestral backgrounds (for ex. CD20 AA vs. CD20 EA). Quantitative measurement of global IFN-stimulated gene (ISG) expression revealed that AA subjects demonstrated more concordance across all studied cell types. Two subgroups of patients were identified based on the ISG expression profiles. One subgroup showed higher ISGs expression in all cell types, and the other subgroup had higherISG expression only in T and B lymphocytes but not in monocytes. The correlation of ISG expression with medication data revealed that only the B cells had lower ISG expression in patients taking immunosuppressants, while ISG expression in the other cell types did not differ based upon medication use. Conclusion: We find striking differences in gene expression between different immune cell subsets and between ancestral backgrounds in SLE patients. The IFN signature is diverse, with different transcripts represented in different cell populations, and signature-positive cell subsets differed in EA vs. AA patients. We also find that treatment with the immunosuppressive agents correlates with the down-regulation of B cell ISG expression, and this was not observed in other cell types. 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 subset from each subject was run on an Illumina HumanHT-12 V4 expression BeadChip array (n=208 arrays).

Project description:Background/Purpose: Systemic lupus erythematosus (SLE) is a complex multi-system autoimmune disease of uncertain etiology. Patients from different ancestral backgrounds demonstrate differences in clinical manifestations and autoantibody profiles. In this study we examined genome-wide transcriptional patterns in major immune cell subsets across 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 subset from each subject was run on an Illumina HumanHT-12 V4 expression BeadChip array (n=208 arrays). Differentially expressed genes (DEGs) were determined by comparing cases and controls of the same ancestral background. Results: The overlap in DEG lists between different cell types from the same ancestral background was very modest (<1%). Typically between 5-10% of DEGs were shared when comparing the same cell type between different ancestral backgrounds (for ex. CD20 AA vs. CD20 EA). Quantitative measurement of global IFN-stimulated gene (ISG) expression revealed that AA subjects demonstrated more concordance across all studied cell types. Two subgroups of patients were identified based on the ISG expression profiles. One subgroup showed higher ISGs expression in all cell types, and the other subgroup had higherISG expression only in T and B lymphocytes but not in monocytes. The correlation of ISG expression with medication data revealed that only the B cells had lower ISG expression in patients taking immunosuppressants, while ISG expression in the other cell types did not differ based upon medication use. Conclusion: We find striking differences in gene expression between different immune cell subsets and between ancestral backgrounds in SLE patients. The IFN signature is diverse, with different transcripts represented in different cell populations, and signature-positive cell subsets differed in EA vs. AA patients. We also find that treatment with the immunosuppressive agents correlates with the down-regulation of B cell ISG expression, and this was not observed in other cell types.

Dataset Information

Transcriptomic Analysis of Classical Monocytes from African American Patients with Systemic Sclerosis

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