Sensitizing Staphylococcus aureus to antibacterial agents by decoding and blocking the lipid flippase MprF
- Eberhard Karls University Tübingen, Germany
- MorphoSys AG, Germany
- University of Tübingen, Germany
- Broad Institute, United States
Abstract
The pandemic of antibiotic resistance represents a major human health threat demanding new antimicrobial strategies. MprF is the synthase and flippase of the phospholipid lysyl-phosphatidylglycerol that increases virulence and resistance of methicillin-resistant Staphylococcus aureus (MRSA) and other pathogens to cationic host defense peptides and antibiotics. With the aim to design MprF inhibitors that could sensitize MRSA to antimicrobial agents and support the clearance of staphylococcal infections with minimal selection pressure, we developed MprF-targeting monoclonal antibodies, which bound and blocked the MprF flippase subunit. Antibody M-C7.1 targeted a specific loop in the flippase domain that proved to be exposed at both sides of the bacterial membrane, thereby enhancing the mechanistic understanding of bacterial lipid translocation. M-C7.1 rendered MRSA susceptible to host antimicrobial peptides and antibiotics such as daptomycin, and it impaired MRSA survival in human phagocytes. Thus, MprF inhibitors are recommended for new anti-virulence approaches against MRSA and other bacterial pathogens.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files.
Article and author information
Author details
Christoph Josef Slavetinsky
Funding
Deutsche Forschungsgemeinschaft (EXC-2124/1-09.001_0)
- Christoph Josef Slavetinsky
Deutsche Forschungsgemeinschaft (EXC-2124/1-09.010_0)
- Christoph Josef Slavetinsky
Deutsches Zentrum für Infektionsforschung (TTU 08.806)
- Christoph Josef Slavetinsky
Deutsches Zentrum für Infektionsforschung (TTU 08.806)
- Andreas Peschel
Deutsche Forschungsgemeinschaft (SFB 766/1-3,TP A08)
- Andreas Peschel
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- María Mercedes Zambrano, CorpoGen, Colombia
Version history
- Preprint posted: November 14, 2020 (view preprint)
- Received: January 8, 2021
- Accepted: January 18, 2022
- Accepted Manuscript published: January 19, 2022 (version 1)
- Version of Record published: February 1, 2022 (version 2)
Copyright
© 2022, Slavetinsky et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,213
- views
-
- 396
- downloads
-
- 17
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Sensitizing Staphylococcus aureus to antibacterial agents by decoding and blocking the lipid flippase MprF
eLife 11:e66376.
https://doi.org/10.7554/eLife.66376
Further reading
-
- Microbiology and Infectious Disease
Mechanisms by which Mycobacterium tuberculosis (Mtb) evades pathogen recognition receptor activation during infection may offer insights for the development of improved tuberculosis (TB) vaccines. Whilst Mtb elicits NOD-2 activation through host recognition of its peptidoglycan-derived muramyl dipeptide (MDP), it masks the endogenous NOD-1 ligand through amidation of glutamate at the second position in peptidoglycan side-chains. As the current BCG vaccine is derived from pathogenic mycobacteria, a similar situation prevails. To alleviate this masking ability and to potentially improve efficacy of the BCG vaccine, we used CRISPRi to inhibit expression of the essential enzyme pair, MurT-GatD, implicated in amidation of peptidoglycan side-chains. We demonstrate that depletion of these enzymes results in reduced growth, cell wall defects, increased susceptibility to antibiotics, altered spatial localization of new peptidoglycan and increased NOD-1 expression in macrophages. In cell culture experiments, training of a human monocyte cell line with this recombinant BCG yielded improved control of Mtb growth. In the murine model of TB infection, we demonstrate that depletion of MurT-GatD in BCG, which is expected to unmask the D-glutamate diaminopimelate (iE-DAP) NOD-1 ligand, yields superior prevention of TB disease compared to the standard BCG vaccine. In vitro and in vivo experiments in this study demonstrate the feasibility of gene regulation platforms such as CRISPRi to alter antigen presentation in BCG in a bespoke manner that tunes immunity towards more effective protection against TB disease.
-
- Microbiology and Infectious Disease
Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, ‘what you put is what you get’ (WYPIWYG) – that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.
