VAV1 and BAFF, via NFκB pathway, are genetic risk factors for myasthenia gravis

Objective To identify novel genetic loci that predispose to early‐onset myasthenia gravis (EOMG) applying a two‐stage association study, exploration, and replication strategy. Methods Thirty‐four loci and one confirmation loci, human leukocyte antigen (HLA)‐DRA, were selected as candidate genes by team members of groups involved in different research aspects of MG. In the exploration step, these candidate genes were genotyped in 384 EOMG and 384 matched controls and significant difference in allele frequency were found in eight genes. In the replication step, eight candidate genes and one confirmation loci were genotyped in 1177 EOMG patients and 814 controls, from nine European centres. Results Allele frequency differences were found in four novel loci: CD86, AKAP12, VAV1, B‐cell activating factor (BAFF), and tumor necrosis factor‐alpha (TNF‐α), and these differences were consistent in all nine cohorts. Haplotype trend test supported the differences in allele frequencies between cases and controls. In addition, allele frequency difference in female versus male patients at HLA‐DRA and TNF‐α loci were observed. Interpretation The genetic associations to EOMG outside the HLA complex are novel and of interest as VAV1 is a key signal transducer essential for T‐ and B‐cell activation, and BAFF is a cytokine that plays important roles in the proliferation and differentiation of B‐cells. Moreover, we noted striking epistasis between the predisposing VAV1 and BAFF haplotypes; they conferred a greater risk in combination than alone. These, and CD86, share the same signaling pathway, namely nuclear factor‐kappaB (NFκB), thus implicating dysregulation of proinflammatory signaling in predisposition to EOMG

Integrative analysis of methylome and transcriptome in human blood identifies extensive sex- and immune cell-specific differentially methylated regions

<div><p>The relationship between DNA methylation and gene expression is complex and elusive. To further elucidate these relations, we performed an integrative analysis of the methylome and transcriptome of 4 circulating immune cell subsets (B cells, monocytes, CD4⁺, and CD8⁺ T cells) from healthy females. Additionally, in light of the known sex bias in the prevalence of several immune-mediated diseases, the female datasets were compared with similar public available male data sets. Immune cell-specific differentially methylated regions (DMRs) were found to be highly similar between sexes, with an average correlation coefficient of 0.82; however, numerous sex-specific DMRs, shared by the cell subsets, were identified, mainly on autosomal chromosomes. This provides a list of highly interesting candidate genes to be studied in disorders with sexual dimorphism, such as autoimmune diseases. Immune cell-specific DMRs were mainly located in the gene body and intergenic region, distant from CpG islands but overlapping with enhancer elements, indicating that distal regulatory elements are important in immune cell specificity. In contrast, sex-specific DMRs were overrepresented in CpG islands, suggesting that the epigenetic regulatory mechanisms of sex and immune cell specificity may differ. Both positive and, more frequently, negative correlations between subset-specific expression and methylation were observed, and cell-specific DMRs of both interactions were associated with similar biological pathways, while sex-specific DMRs were linked to networks of early development or estrogen receptor and immune-related molecules. Our findings of immune cell- and sex-specific methylome and transcriptome profiles provide novel insight on their complex regulatory interactions and may particularly contribute to research of immune-mediated diseases.</p></div

Interferon-Beta Induces Distinct Gene Expression Response Patterns in Human Monocytes versus T cells

<div><p>Background</p><p>Monocytes, which are key players in innate immunity, are outnumbered by neutrophils and lymphocytes among peripheral white blood cells. The cytokine interferon-β (IFN-β) is widely used as an immunomodulatory drug for multiple sclerosis and its functional pathways in peripheral blood mononuclear cells (PBMCs) have been previously described. The aim of the present study was to identify novel, cell-specific IFN-β functions and pathways in tumor necrosis factor (TNF)-α-activated monocytes that may have been missed in studies using PBMCs.</p><p>Methodology/Principal Findings</p><p>Whole genome gene expression profiles of human monocytes and T cells were compared following <i>in vitro</i> priming to TNF-α and overnight exposure to IFN-β. Statistical analyses of the gene expression data revealed a cell-type-specific change of 699 transcripts, 667 monocyte-specific transcripts, 21 T cell-specific transcripts and 11 transcripts with either a difference in the response direction or a difference in the magnitude of response. RT-PCR revealed a set of differentially expressed genes (DEGs), exhibiting responses to IFN-β that are modulated by TNF-α in monocytes, such as <i>RIPK2</i> and <i>CD83</i>, but not in T cells or PBMCs. Known IFN-β promoter response elements, such as ISRE, were enriched in T cell DEGs but not in monocyte DEGs. The overall directionality of the gene expression regulation by IFN-β was different in T cells and monocytes, with up-regulation more prevalent in T cells, and a similar extent of up and down-regulation recorded in monocytes.</p><p>Conclusions</p><p>By focusing on the response of distinct cell types and by evaluating the combined effects of two cytokines with pro and anti-inflammatory activities, we were able to present two new findings First, new IFN-β response pathways and genes, some of which were monocytes specific; second, a cell-specific modulation of the IFN-β response transcriptome by TNF-α.</p></div

Volcano plots for the differential gene expression following IFN-β treatment of monocytes and T cells.

<p>A. monocytes; B. T cells. The X axis describes the fold change in expression levels between cells treated with IFN-β relative to untreated cells, for each transcript in a log2 scale. The Y axis shows the statistical significance expressed as -log10(p-value) from the simple comparison. Transcripts with log2 difference of ≥1 and with -log10(p-value) ≥3.8, which is the equivalent of p≤0.05 after FDR adjustment, were defined as differentially expressed genes (DEGs) and are highlighted with blue for down-regulated and red for up-regulated DEGs.</p

Ten top canonical pathways enriched in monocytes and T cells datasets.

1<p>Ratio indicates the number of DEGs participating in a pathway divided by the total number of molecules participating in that pathway. Data in this table was generated by IPA.</p

Differentially expressed genes in both monocytes and T cells (25 out of 106 genes).

1<p>Expression level in treated samples divided by expression level in untreated samples.</p>2<p>FDR adjusted p-value for IFN-β effect within each cell type.</p>3<p>FDR adjusted p-value of the two-way ANOVA for the cell-type*IFN-β interaction.</p

Networks regulated by IFN-β in monocytes include functions of cell migration, and cellular development and proliferation.

<p>Genes were mapped to IPA-generated networks based on known gene interactions, and the networks were ranked according to the number of biological connections between the transcripts by IPA. Shown in this figure are two high-score networks (IPA score = 29 for both), which we named based on the IPA-suggested keywords: A. Cell migration; B. Cellular development and proliferation. Green nodes indicate down-regulation and red indicate up-regulation. White nodes indicate molecules that were incorporated into the network through relationships with other molecules but are not DEGs. Dotted lines represent indirect interaction, solid lines represent direct interaction. Protein symbols are explained in the symbol legend in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062366#pone-0062366-g006" target="_blank">figure 6C</a>. C. RT-PCR analyses for the genes <i>EDN1</i> from Cell migration network, and <i>IL1B</i> and <i>RXRA</i> from cellular development and proliferation network for monocytes and T cells (n = 6). The Y axis depicts the changes in RNA expression levels in response to IFN-β as fold change (2^−ΔΔCT); the fold change following the IFN-β treatment was significant for all genes in both monocytes and T cells (P-values<0.03, Wilcoxon signed rank test). *p-value <0.01 by Mann Whitney test for comparison of fold change between monocytes and T cells. Horizontal bars indicate median values for each column of data points.</p

Differentially expressed genes in T cells but not in monocytes (21 in total).

1<p>Expression level in treated samples divided by expression level in untreated samples.</p>2<p>FDR adjusted p-value for IFN-β effect within each cell type.</p>3<p>FDR adjusted p-value of the two-way ANOVA for the cell-type*IFN-β interaction.</p