The genetics of systemic lupus erythematosus and implications for targeted therapy

Observations of familial aggregation (λs=8–29) and a 40% identical twin concordance rate prompted recent work towards a comprehensive genetic analysis of systemic lupus erythematosus (SLE). Since 2007, the number of genetic effects known to be associated with human lupus has increased by fivefold, underscoring the complexity of inheritance that probably contributes to this disease. Approximately 35 genes associated with lupus have either been replicated in multiple samples or are near the threshold for genome-wide significance (p>5×10−8). Some are rare variants that convincingly contribute to lupus only in specific subgroups. Strong associations have been found with a large haplotype block in the human leucocyte antigen region, with Fcγ receptors, and with genes coding for complement components, in which a single gene deletion may cause SLE in rare familial cases and copy number variation is more common in the larger population of SLE patients. Examples of newly discovered genes include ITGAM, STAT4 and MECP2/IRAK1. Ongoing studies to build models in which combinations of associated genes might contribute to specific disease manifestations should contribute to improved understanding of disease pathology. In addition, pharmacogenomic components of ongoing clinical trials are likely to provide insights into fundamental disease pathology as well as contributing to informed patient selection for targeted treatments and biomarkers to guide dosing and gauge responsiveness. Besides these potentially valuable new insights into the pathophysiology of an enigmatic, potentially deadly, and, as yet, unsolved disease, genetic studies are likely to suggest novel molecular targets for strategic development of safer and more effective therapeutics

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

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

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

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

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