AUTHOR=Trautwein-Schult Anke , Maaß Sandra , Plate Kristina , Otto Andreas , Becher Dörte 

TITLE=A Metabolic Labeling Strategy for Relative Protein Quantification in Clostridioides difficile

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 9 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2018.02371

DOI=10.3389/fmicb.2018.02371

ISSN=1664-302X

ABSTRACT=Clostridioides difficile (formerly Clostridium difficile) is a Gram-positive, anaerobe, spore-forming pathogen, which causes drug-induced diseases in hospitals worldwide. A detailed analysis of the proteome may provide new targets for drug development or therapeutic strategies to combat this pathogen.
The application of metabolic labeling would allow for accurate quantification of significant differences in protein abundance, even in the case of very small changes. Additionally, it would be possible to perform more accurate studies of the membrane or surface proteomes, which usually require elaborated sample preparation. Such studies are therefore prone to higher standard deviations during the quantification. The implementation of metabolic labeling strategies for C. difficile is complicated due to the lack in arginine and lysine auxotrophy as well as the Stickland dominated metabolism of this anaerobic pathogen. Hence, quantitative proteome analyses could only be carried out by label free or chemical labeling methods so far.
In this paper, a metabolic labeling approach for C. difficile is described. A cultivation procedure with 15N labeled media for strain 630Δerm was established achieving an incorporation rate higher 97%. In a proof-of-principle experiment, the performance of the metabolic labeling approach in C. difficile was tested. The proteome data of the cytosolic subproteome of C. difficile cells grown in complex medium as well as two minimal media in the late exponential and early stationary growth phase obtained via metabolic labeling were compared with two label free relative quantification approaches (NSAF and LFQ). The numbers of identified proteins were comparable within the three approaches, whereas the number of quantified proteins were between 1,110 (metabolic labeling) and 1,861 (LFQ) proteins. A hierarchical clustering showed clearly separated clusters for the different conditions and a small tree height with metabolic labeling approach. Furthermore, it was shown that the quantification based on metabolic labeling revealed significant altered proteins with small fold changes compared to the label free approaches. The quantification based on metabolic labeling was accurate, reproducible, and even more sensitive compared to label free quantification strategies.