Abstract
Natalizumab inhibits the transmigration of activated T lymphocytes into the brain and is highly efficacious in multiple sclerosis (MS). However, from a pharmacogenomic perspective, its efficacy and safety in specific patients remain unclear. Here our goal was to analyze the effects of epithelial V-like antigen (EVA) on anti-alpha&sub4; integrin (VLA4) efficacy in a mouse model of MS, experimental autoimmune encephalomyelitis (EAE). EVA has been previously characterized in human CD4 T lymphocytes, mouse thymic development, and choroid plexus epithelial cells. Further analysis here demonstrated expression in B lymphocytes and an increase in EVA⁺ lymphocytes following immunization. Following active induction of EAE using the MOG³&sup5;⁻&sup5;&sup5; active immunization model, EVA deficient mice developed more severe EAE and white matter tissue injury as compared to wild type controls. This severe EAE phenotype did not respond to anti-VLA4 treatment. In both the control antibody and anti-VLA4 conditions, these mice demonstrated persistent CNS invasion of mature B lymphocyte (CD19⁺, CD21⁺, sIgG⁺), increased serum autoantibody levels, and extensive complement and IgG deposition within lesions containing CD5⁺IgG⁺ cells. Wild type mice treated with control antibody also demonstrated the presence of CD19⁺, CD21⁺, sIgG⁺ cells within the CNS during peak EAE disease severity and detectable serum autoantibody. In contrast, wild type mice treated with anti-VLA4 demonstrated reduced serum autoantibody levels as compared to wild type controls and EVA-knockout mice. As expected, anti-VLA4 treatment in wild type mice reduced the total numbers of all CNS mononuclear cells and markedly decreased CD4 T lymphocyte invasion. Treatment also reduced the frequency of CD19⁺, CD21⁺, sIgG⁺ cells in the CNS. These results suggest that anti-VLA4 treatment may reduce B lymphocyte associated autoimmunity in some individuals and that EVA expression is necessary for an optimal therapeutic response. We postulate that these findings could optimize the selection of treatment responders.