Correlative gene associations in normal B cell responses and in hyperresponsive B cell responses.

<p>Pearson correlation was utilized to estimate the correlation coefficients. Negative correlations are shown in blue; while positive correlations are shown in red. Genes examined are listed in table on the left. Gene numbers (right column) are used as coordinates along x and y-axis. Select gene expression graphs are shown on the far right with the two SLE patient cell lines depicted in red and the two control cell lines depicted in blue. The order of genes is maintained giving clear visualization of differences in gene associations.</p

Variable gene clustering after stimulation of B cells from lupus patients and controls.

<p>Normalized gene expression data (average = 0, standard deviation = 1) from stimulated hyperresponsive B cells from SLE patients (left) and normal response B cells from control (right). Blue indicates negative normalized expression data and red indicates positive normalized expression data. Six gene clusters and the corresponding cluster profiles are shown to the right side of the heat-maps.</p

Gene network interaction after B cell stimulation of SLE patient and normal control samples.

<p>A gene network interaction map (from the initial analysis group) built using the partial correlations method is shown. Genes were grouped in different colored clusters representing maximum expression levels at various time points after B cell stimulation. The center gene clusters represent genes with maximum expression levels obtained after 0.5 hours of stimulation; then followed by colored circles for genes with maximum expression levels at 1, 2, 4, 8 hours, while the genes in peripheral circle reach maximum expression levels 16 to 24 hours after stimulation. Blue lines linking genes represent gene associations found in normal control samples. Red lines represent gene associations found in SLE patient samples. Black lines indicate gene associations found in both groups. Dashed lines represent negative gene associations.</p

Differences in gene dynamics between normal control SLE samples.

<p>The shown profiles are representative of gene dynamics observed in duplicated experiments. Three differences in gene dynamics are shown: quantitative differences (left), changes in gene profiles (middle), and changes from hyper variable to stable (right). Graphs were shown as hours after stimulation (x-axis) and normalized gene expressions (y-axis). Each line on the graph represents one cell line. Each cell line was classified as a high responder (solid line) or low responder (hatched line). SLE patient sample gene expression is shown in red; while normal control sample gene expression is shown in blue.</p

Transcription factor analysis of uniquely activated genes in control and SLE patient samples.

<p>The transcription factors tested are shown at the top of the figure. Blue text represents unique genes identified in control samples; while red text identifies unique genes identified in SLE patient samples. Individual elements of the matrix are colored by the significance of the p-values (threshold p = 0.05): over-representation in the matrix is indicated in red, under-representation is indicated in green.</p

Evidence of Dynamically Dysregulated Gene Expression Pathways in Hyperresponsive B Cells from African American Lupus Patients

<div><p>Recent application of gene expression profiling to the immune system has shown a great potential for characterization of complex regulatory processes. It is becoming increasingly important to characterize functional systems through multigene interactions to provide valuable insights into differences between healthy controls and autoimmune patients. Here we apply an original systematic approach to the analysis of changes in regulatory gene interconnections between in Epstein-Barr virus transformed hyperresponsive B cells from SLE patients and normal control B cells. Both traditional analysis of differential gene expression and analysis of the dynamics of gene expression variations were performed in combination to establish model networks of functional gene expression. This Pathway Dysregulation Analysis identified known transcription factors and transcriptional regulators activated uniquely in stimulated B cells from SLE patients.</p></div

Schematic of pathway dysregulation analysis.

<p>The self-verified experimental design of pathway dysregulation analysis is depicted.</p

Transcription factor analysis of the variable gene clusters.

<p>Transcription factors tested are presented at the top of the diagram. The cluster content is shown along the right. Individual elements of the matrix are colored by the significance of the p-values (threshold p = 0.05): over-representation in the matrix is indicated in red, under-representation is indicated in green.</p

pERK1/2 is upregulated in hyperresponsive B cells after stimulation.

<p>B cells isolated from SLE patients and matched controls were stimulated with anti-human IgM F(ab)’₂ for 30 seconds or 2 minutes. pERK1/2 (A) and normalized pERK1/2 intensity (B) shows increase in hyperresponsive B cells after stimulation.</p

The Effect of Inversion at 8p23 on <i>BLK</i> Association with Lupus in Caucasian Population

<div><p>To explore the potential influence of the polymorphic 8p23.1 inversion on known autoimmune susceptibility risk at or near <i>BLK</i> locus, we validated a new bioinformatics method that utilizes SNP data to enable accurate, high-throughput genotyping of the 8p23.1 inversion in a Caucasian population. Methods: Principal components analysis (PCA) was performed using markers inside the inversion territory followed by k-means cluster analyses on 7416 European derived and 267 HapMaP CEU and TSI samples. A logistic regression conditional analysis was performed. Results: Three subgroups have been identified; inversion homozygous, heterozygous and non-inversion homozygous. The status of inversion was further validated using HapMap samples that had previously undergone Fluorescence in situ hybridization (FISH) assays with a concordance rate of above 98%. Conditional analyses based on the status of inversion were performed. We found that overall association signals in the <i>BLK</i> region remain significant after controlling for inversion status. The proportion of lupus cases and controls (cases/controls) in each subgroup was determined to be 0.97 for the inverted homozygous group (1067 cases and 1095 controls), 1.12 for the inverted heterozygous group (1935 cases 1717 controls) and 1.36 for non-inverted subgroups (924 cases and 678 controls). After calculating the linkage disequilibrium between inversion status and lupus risk haplotype we found that the lupus risk haplotype tends to reside on non-inversion background. As a result, a new association effect between non-inversion status and lupus phenotype has been identified ((p = 8.18×10⁻⁷, OR = 1.18, 95%CI = 1.10–1.26). Conclusion: Our results demonstrate that both known lupus risk haplotype and inversion status act additively in the pathogenesis of lupus. Since inversion regulates expression of many genes in its territory, altered expression of other genes might also be involved in the development of lupus.</p></div