Please use this identifier to cite or link to this item: 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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