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http://hdl.handle.net/1942/35091
Title: | Identification of spinal cord injury-induced autoantibodies as prognostic biomarkers using serological antigen selection | Authors: | Bovens, Becky | Advisors: | FRAUSSEN, Judith | Issue Date: | 2021 | Publisher: | tUL | Abstract: | Spinal cord injury (SCI) is a devastating condition, leading to sensory and motor function loss. SCI disrupts the blood-spinal cord barrier, releasing SCI-related proteins in the blood stream, triggering autoimmune responses and the production of autoantibodies. Since there are no predictive strategies for the SCI-outcome, these SCI-induced autoantibodies can be valuable as prognostic disease markers, especially in patients with worsening of the SCI. Our aim was to identify a full spectrum of SCI-induced autoantibodies from SCI patients’ plasma using serological antigen selection (SAS). Immunoglobulin (Ig)M and IgG levels in SCI patients’ plasma were determined 0-4 days post-injury (DPI) and 20-33 DPI using ELISA. Next, IgM ELISA and SAS procedures were optimised. SAS used a cDNA phage display library expressing spinal cord proteins, which was screened for SCI-induced autoantibody reactivity using pooled plasma of 12 SCI patients with known worsening of the SCI. Phage expressing antigens to which SCI-induced autoantibodies bound were selected. Selected antigenic targets were characterised using colony PCR and DNA fingerprinting. SCI patients’ total plasma IgM levels were significantly increased (p=0.0491) 20-33 DPI compared to 0-4 DPI, in contrast, IgG levels remained stable (p=0.3474). SAS and phage ELISA data suggested an increased selection of phage expressing antigens with SCI-induced autoantibody reactivity. DNA fingerprinting of the selected clones identified five digestion patterns, confirming enrichment of phage. | Notes: | Master of Biomedical Sciences-Molecular Mechanisms in Health and Disease | Document URI: | http://hdl.handle.net/1942/35091 | Category: | T2 | Type: | Theses and Dissertations |
Appears in Collections: | Master theses |
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c1230f7b-bf54-4aa3-932e-0928572e4b02.pdf | 1.12 MB | Adobe PDF | View/Open |
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