ESTIMATING AUTOANTIBODY SIGNATURES TO DETECT AUTOIMMUNE DISEASE PATIENT SUBSETS

Autoimmune diseases are characterized by highly specific immune responses against molecules in self-tissues. Different autoimmune diseases are characterized by distinct immune responses, making autoantibodies useful for diagnosis and prediction. In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of labeled, immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel Electrophoresis and band detection on Autoradiograms (GEA)]. Human recognition of patterns is not optimal when the patterns are complex or scattered across many samples. Multiple sources of errors - including irrelevant intensity differences and warping of gels - have challenged automation of pattern discovery from autoradiograms. In this paper, we address these limitations using a Bayesian hierarchical model with shrinkage priors for pattern alignment and spatial dewarping. The Bayesian model combines information from multiple gel sets and corrects spatial warping for coherent estimation of autoantibody signatures defined by presence or absence of a grid of landmark proteins. We show the preprocessing creates better separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering. Finally, we demonstrate the utility of the proposed methods with GEA data from scleroderma patients

Enhanced autoantigen expression in regenerating muscle cells in idiopathic inflammatory myopathy

Unique autoantibody specificities are strongly associated with distinct clinical phenotypes, making autoantibodies useful for diagnosis and prognosis. To investigate the mechanisms underlying this striking association, we examined autoantigen expression in normal muscle and in muscle from patients with autoimmune myositis. Although myositis autoantigens are expressed at very low levels in control muscle, they are found at high levels in myositis muscle. Furthermore, increased autoantigen expression correlates with differentiation state, such that myositis autoantigen expression is increased in cells that have features of regenerating muscle cells. Consistent with this, we found that cultured myoblasts express high levels of autoantigens, which are strikingly down-regulated as cells differentiate into myotubes in vitro. These data strongly implicate regenerating muscle cells rather than mature myotubes as the source of ongoing antigen supply in autoimmune myositis. Myositis autoantigen expression is also markedly increased in several cancers known to be associated with autoimmune myositis, but not in their related normal tissues, demonstrating that tumor cells and undifferentiated myoblasts are antigenically similar. We propose that in cancer-associated myositis, an autoimmune response directed against cancer cross-reacts with regenerating muscle cells, enabling a feed-forward loop of tissue damage and antigen selection. Regulating pathways of antigen expression may provide unrecognized therapeutic opportunities in autoimmune diseases

Histidyl–tRNA Synthetase and Asparaginyl–tRNA Synthetase, Autoantigens in Myositis, Activate Chemokine Receptors on T Lymphocytes and Immature Dendritic Cells

Autoantibodies to histidyl–tRNA synthetase (HisRS) or to alanyl–, asparaginyl–, glycyl–, isoleucyl–, or threonyl–tRNA synthetase occur in ∼25% of patients with polymyositis or dermatomyositis. We tested the ability of several aminoacyl–tRNA synthetases to induce leukocyte migration. HisRS induced CD4+ and CD8+ lymphocytes, interleukin (IL)-2–activated monocytes, and immature dendritic cells (iDCs) to migrate, but not neutrophils, mature DCs, or unstimulated monocytes. An NH2-terminal domain, 1–48 HisRS, was chemotactic for lymphocytes and activated monocytes, whereas a deletion mutant, HisRS-M, was inactive. HisRS selectively activated CC chemokine receptor (CCR)5-transfected HEK-293 cells, inducing migration by interacting with extracellular domain three. Furthermore, monoclonal anti-CCR5 blocked HisRS-induced chemotaxis and conversely, HisRS blocked anti-CCR5 binding. Asparaginyl–tRNA synthetase induced migration of lymphocytes, activated monocytes, iDCs, and CCR3-transfected HEK-293 cells. Seryl–tRNA synthetase induced migration of CCR3-transfected cells but not iDCs. Nonautoantigenic aspartyl–tRNA and lysyl–tRNA synthetases were not chemotactic. Thus, autoantigenic aminoacyl–tRNA synthetases, perhaps liberated from damaged muscle cells, may perpetuate the development of myositis by recruiting mononuclear cells that induce innate and adaptive immune responses. Therefore, the selection of a self-molecule as a target for an autoantibody response may be a consequence of the proinflammatory properties of the molecule itself

Interferon and autoantigens: intersection in autoimmunity

Interferon (IFN) is a key component of the innate immune response. For reasons that remain incompletely understood, the IFN system is upregulated in several rheumatic diseases, particularly those that feature autoantibody production, such as SLE, Sjögren’s syndrome, myositis and systemic sclerosis. Interestingly, many of the autoantigens targeted in these diseases are components of the IFN system, representing IFN-stimulated genes (ISGs), pattern recognition receptors (PRRs), and modulators of the IFN response. In this review, we describe features of these IFN-linked proteins that may underlie their status as autoantigens. Note is also made of anti-IFN autoantibodies that have been described in immunodeficiency states