Optimization of a Top-Down Proteomics Platform for Closely Related Pathogenic Bacterial Discrimination

The current technique
used for microbial identification in hospitals
is matrix-assisted laser desorption/ionization time-of-flight mass
spectrometry (MALDI-TOF MS). However, it suffers from important limitations,
in particular, for closely related species or when the database used
for the identification lacks the appropriate reference. In this work,
we set up a liquid chromatography (LC)–MS/MS top-down proteomics
platform, which aims at discriminating closely related pathogenic
bacteria through the identification of specific proteoforms. Using Escherichia coli as a model, all steps of the workflow
were optimized: protein extraction, on-line LC separation, MS method,
and data analysis. Using optimized parameters, about 220 proteins,
corresponding to more than 500 proteoforms, could be identified in
a single run. We then used this platform for the discrimination of
enterobacterial pathogens undistinguishable by MALDI-TOF, although
leading to very different clinical outcomes. For each pathogen, we
identified specific proteoforms that could potentially be used as
biomarkers. We also improved the characterization of poorly described
bacterial strains. Our results highlight the advantage of addressing
proteoforms rather than peptides for accurate bacterial characterization
and qualify top-down proteomics as a promising tool in clinical microbiology.
Data are available via ProteomeXchange with the identifier
PXD019247.