Design an optimal alpha-synuclein (ASyn) vaccine to elicit antibodies that selectively target toxic species of ASyn and avoid cross-reactivity with physiologic and non-toxic forms of ASyn.
Vaccination against pathogenic species of Asyn (toxic oligomers, small soluble seeding fibrils), has the potential to protect against synucleinopathies. Vaccine constructs containing computationally-derived conformational B cell epitopes in the EKTKEQ region of misfolded pathogenic ASyn (aa 57-62) were tested in mice. The potential advantage of this approach, as opposed to inducing pan-ASyn reactivity, lies in preserving normal ASyn function and minimizing the diversion of active antibody by the more abundant non-toxic forms of the protein in blood and CNS.
Mice were vaccinated with four different conformational B cell epitopes conjugated to KLH and formulated with QS-21 adjuvant. Serum IgG titers against the peptide epitopes were measured by ELISA. The binding profile of the antibodies was assessed against monomers and pathogenic Asyn in soluble brain homogenates from dementia with Lewy bodies (DLB) patients by SPR, and reactivity with Lewy bodies/neurites by immunohistochemistry.
All 4 epitopes elicited robust antibody responses when administered either individually or together as part of a quadrivalent vaccine. The serum antibodies reacted with pathogenic ASyn in DLB soluble brain homogenate but not with physiologic monomers or non-toxic insoluble fibril deposits in brain sections. Comparison of binding responses to DLB brain homogenate and dissociation rates of equal amounts of IgG from immune serum of monovalent vaccines vs mixtures of 2, 3 or 4 sera was used to rank all 15 possible vaccine configurations. Maximal, equivalent reactivity was observed with a combination of immune IgG from 2 select epitopes or a mixture of all 4.
Vaccination with conformational B cell epitopes produced high affinity antibodies with the desired selectivity for pathogenic Asyn. Optimal vaccine configurations were identified that are being tested in disease models.