Published June 5, 2019 | Version v1
Journal article Open

Comparison of three methods for cell surface proteome extraction of Listeria monocytogenes biofilms

Description

The cell surface proteome of the foodborne pathogen Listeria monocytogenes, the etiological agent of listeriosis,
is critical for understanding the physiological processes associated with stress resistance and persistence in the
environment. In this context, the most widespread mode of growth for bacterial cells in natural and industrial
environments is in biofilms. Cell surface proteins are, however, challenging to characterize because of their low
abundance and poor solubility. Moreover, cell surface protein extracts are usually contaminated with cytoplasmic
proteins that constitute the main signal in proteomic analysis. This study aimed to compare the efficiency of three
methods to extract and explore surface proteins of L. monocytogenes growing in a biofilm: trypsin shaving,
biotinylation, and cell fractionation. Peptide separation and identification were performed by shotgun proteomics
using high-performance liquid chromatography combined with tandem mass spectrometry (LC-MS/MS). The
biotinylation method was the most effective in extracting surface proteins, with the lowest rate of contamination
by cytoplasmic proteins. Although presenting a higher contamination rate in cytoplasmic proteins, the other two
techniques allowed the identification of additional surface proteins. Seven proteins were commonly retrieved by
the three methods. The extracted proteins belong to several functional classes, involved in virulence, transport, or
metabolic pathways. Finally, the three extraction methods seemed complementary and their combined use improved
the exploration of the bacterial surface proteome. These new findings collectively inform future discovery
and translational proteomics for clinical, environmental health, and industrial applications.

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Funding

List_MAPS – Training and research in Listeria monocytogenes Adaptation through Proteomic and Transcriptome deep Sequencing Analysis 641984
European Commission