Proteomic profiling to identify markers of bacterial meningitis. Issue 2 (January 2015)

• Record Type: Journal Article
• Authors: Carrol, Enitan D.; Gómez-Baena, L; Laing, G; Beynon, R
• Is Part Of: New horizons in translational medicine. Volume 2:Issue 2(2015)
• Page Start: 66
• Publication Date: 2015-01
• Subjects: Medicine -- Periodicals; Medicine -- Research -- Periodicals; 610
• DOI: 10.1016/j.nhtm.2014.11.039
• Languages: English
• ISSNs: 2307-5023

Abstract

Abstract: Bacterial meningitis is usually fatal without treatment and prompt and accurate diagnosis coupled with the timely administration of parenteral antibiotics are necessary in order to save lives. Despite the availability of highly effective antibiotics, the complications from bacterial meningitis (such as deafness, hydrocephalus, seizures and cerebral palsy) remain high. In areas with a high incidence of human immunodeficiency virus infection, Streptococcus pneumoniae is the commonest cause of bacterial meningitis. The diagnosis of bacterial meningitis can sometimes be delayed whilst samples are analysed in a laboratory using traditional methods of microscopy and antigen testing. We used cutting-edge high definition and quantitative mass spectrometry to identify specific protein signatures in cerebrospinal fluid associated with Streptococcus pneumoniae infection which could lead to the development of assays or point-of-care devices to improve the speed and accuracy of diagnosis, and consequently to enhance the prognosis of adults and children with bacterial meningitis. A range of samples (cases and controls, n=12) from Malawian children has been analysed. Our data indicate some clear trends, and confirm that quantitative proteomics analysis will be successful in generating a comprehensive protein list from which markers might be nominated. We identified a total of 519 proteins in data dependent discovery proteomics and obtained quantitative data for 161 proteins using data independent Hi3 quantification. Using Progenesis LCMS we obtained a list of 202 potential candidates using data dependent acquisition approach and 109 using data independent acquisition, 82 proteins being common to both workflows. The protein profiles clearly differentiated cases and controls and have the potential to inform diagnosis and management of bacterial meningitis, especially in the developing world where the disease burden and mortality is greatest.