Lookup NU author(s): Dr Beth Lawry,
Dr Christopher Johnson,
Dr Keith Flanagan,
Emeritus Professor Calum McNeil,
Professor Anil Wipat,
Dr Neil Keegan
This is the authors' accepted manuscript of an article that has been published in its final definitive form by American Chemical Society, 2018.
For re-use rights please refer to the publisher's terms and conditions.
Clostridium difficile is a Gram-positive, spore-forming bacterium that continues to present a world-wide problem in healthcare settings. The bacterium causes disease, the symptoms of which include diarrhoea, fever, nausea, abdominal pain and even death. Despite the prevalence of the disease, the diagnosis of C. difficile infection is still challenging, with a variety of methods available, each varying in their effectiveness. In this work we sought to identify a new biomarker for C. difficile, develop affinity reagents and design a diagnostic assay for C. difficile infection which could be used in a typical two-step testing algorithm. Initially a bioinformatics pipeline was developed that identified a surface associated biomarker “AKDGSTKEDQLVDALA” present in all C. difficile strains sequenced to-date and unique to the C. difficile species. Monoclonal antibodies were subsequently raised against peptides corresponding to the biomarker sequence. During characterisation studies, monoclonal antibody 521 (mAb521) was shown to bind all known C. difficile surface layer types, but not closely related strains. Surface plasmon resonance measurements were used to calculate an apparent equilibrium dissociation constant of 36.5 nM between the purified protein target containing the biomarker (surface layer protein A) and mAb521. We demonstrate a limit of detection of 12.4 ng/ml against surface layer protein A and 1.7 x 106 cells/ml in minimally processed C. difficile cultures. The utility of this computational approach to antibody design for diagnostic tests is the ability to produce antibodies which can act as universal species identifiers whilst mitigating the likelihood of false-positive detection by intelligently screening potential biomarkers against RefSeq data for other non-target bacteria.
Author(s): Lawry BM, Johnson CL, Flanagan K, Spoors JA, McNeil CJ, Wipat A, Keegan N
Publication type: Article
Publication status: Published
Journal: Analytical Chemistry
Print publication date: 20/11/2018
Online publication date: 31/10/2018
Acceptance date: 22/10/2018
ISSN (print): 0003-2700
ISSN (electronic): 1520-6882
Publisher: American Chemical Society
Data Source Location: https://doi.org/10.17634/122638-3
PubMed id: 30379538
Notes: Due to a production error, this paper was published on the Web on October 31, 2018, with part of the funding information missing. The corrected version was reposted on November 1, 2018.
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