Studies suggest that natural autoantibodies may be part of an immunological network which maintains the normal homeostatic response seen in controls. Any defect in this network leading to autoimmunity may be represented in the anti-retinal antibody response observed in patients. Characterisation of the humoral autoimmune response occurring during active uveitis may provide valuable information on the immune mechanisms, both humoral and cellular, involved in uveitis. Serum titres and ELISA based tests can only partially describe an antibody response, a more complete description requires access to the B-cell repertoire constituting the response. In the past hybridoma technology has generated a wealth of vital information on antibody responses in animals, but with limited success when applied to humans, producing unstable cell lines with poor antigen affinity. Using scFv phage display antibody technology we attempted to isolate the immune response occurring during active uveitis using a phage display library derived from peripheral blood lymphocyte mRNA of a patient with active uveitis. In this study, we report the isolation and characterisation of human autoimmune recombinant scFv's from two libraries, a uveitis patient derived library and a healthy non uveitis donor derived library. Anti-IRBP and S-Antigen autoantibodies were successfully selected from both libraries. Sequence analysis of these selected autoantibodies revealed possible differential epitope targeting of disease associated anti-S-Ag autoantibodies, and exclusive use of the VH segment, DP49 was revealed among selected anti-S-Ag scFv's. In addition ELISA studies using the selected scFv's, and both patient and control serum, indicated that it may be possible to distinguish the 'natural' and disease associated anti-S-Ag responses at the idiotype/anti-idiotype network level.
Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:311201 |
Date | January 1999 |
Creators | O'Brien, Siobhan Helen |
Publisher | University of Aberdeen |
Source Sets | Ethos UK |
Detected Language | English |
Type | Electronic Thesis or Dissertation |
Source | http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU123996 |
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