Impact of Sequential Passaging on Protein Expression of E. coli Using Proteomics Analysis

• Main Authors: Mohammed S. Alhajouj, Ghadah S. Alsharif, Ahmed A. Mirza
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: International Journal of Microbiology
• Online Access: http://dx.doi.org/10.1155/2020/2716202

Urinary tract infection (UTI) is one of the most prevalent bacterial infections in the world affecting the bladder and the kidney. Escherichia coli (E. coli) is the main causative agent of 80–90% of community-acquired UTIs, about 40% of nosocomial UTIs, and 25% of recurrent UTIs. The field of proteomics has emerged as a great tool to analyze expressed proteins to identify possible biomarkers associated with many pathological states and, to the same extent, those associated with bacterial pathogenesis and their ability to cause recurrent infections. Here, in a descriptive cross-sectional pilot study, we employed proteomic techniques to investigate the effects of environmental stress on protein profiles of E. coli simulated by sequential passaging of samples from patients with UTIs to screen for unique proteins that arise under stressful environment and could aid in the early detection of UTIs. Four urine samples were collected from individuals with recurrent UTI and sequentially subcultured; protein samples were extracted from bacterial pellets and analyzed using 2-dimensional gel electrophoresis (2DGE). Protein spots of interest arising from changes in the protein profile were analyzed using liquid chromatography-mass spectrometry (LC-MS/MS) and matched against known databases to identify related proteins. We identified ATPB_ECOBW, ASPA ECOLI, DPS ECOL6, and DCEB ECOLI as proteins associated with higher passaging. We concluded that passaging resulted in identifiable changes in the protein profile of E. coli, namely, proteins that are associated with survival and possible adaptation of bacteria, suggestive of factors contributing to antibiotic resistance and recurrent UTIs. Furthermore, our method could be further used to identify indicator-protein candidates that could be a part of a growing protein database to diagnose and identify causative agents in UTIs.