Characterising novel biomarkers for use in point of need sensors :a Clostridium difficile case study

Abstract

Clostridium difficile infection (CDI) is the leading cause of nosocomial diarrhoea in the developed world and community-acquired infection is increasing in incidence. The current ‘gold standard’ for diagnosing CDI is stool culture, followed by a cell cytotoxicity neutralisation assay. However, these methods are labour intensive and time consuming, so are seldom used. More rapid tests for diagnosing CDI, for example, toxin enzyme immunoassays, are less sensitive and/or specific. Accurate and early detection, especially at the appropriate point of need (PoN), is crucial for the successful treatment of CDI and minimising transmission of the disease between patients. In this study, a novel bioinformatics approach was used to identify protein sequence diagnostic biomarkers that are unique for bacteria within a particular group of interest (GOI). This approach incorporated a previously designed system, IDRIS, to mine all fully annotated genomes within the NCBI RefSeq database, together with a bioinformatics workflow that was designed and applied within this project. The workflow uses several bioinformatics tools to identify biomarkers that are a minimum of 15 amino acids, are unique to a GOI and are surface accessible within whole cells. Biomarkers from surface accessible proteins were selected as they would provide targets that facilitate PoN detection of whole cells, by minimising the need for time consuming pre-treatment of samples. The most promising target identified was a 16 amino acid biomarker within the surface layer protein, SlpA. Monoclonal antibodies (mAbs) were produced against this unique biomarker and were shown to bind specifically to whole C. difficile cells, including strains from each of the 13 surface layer type clades, which represent the entire C. difficile species. The selected mAb did not bind to species closely related to C. difficile, highlighting the molecules promise for PoN CDI detection. The sensitive and specific binding of the developed mAb highlights the value of the bioinformatics approach, which could be used to provide similar results for other bacteria, including other pathogens.