Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in Helicobacter pylori
Abstract
Helical cell shape is necessary for efficient stomach colonization by Helicobacter pylori, but the molecular mechanisms for generating helical shape remain unclear. The helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod H. pylori. Quantitative 3D microscopy analysis of short pulses with either N-acetylmuramic acid or D-alanine metabolic probes showed that cell wall growth is enhanced at both sidewall curvature extremes. Immunofluorescence revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most abundant at positive Gaussian curvature. Strains expressing CcmA variants with altered polymerization properties lose helical shape and associated positive Gaussian curvatures. We thus propose a model where CcmA and MreB promote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this patterning is one mechanism necessary for maintaining helical shape.
Data availability
The MATLAB scripts used to reconstruct cell surfaces and perform the geometric enrichment analyses are publicly available under a BSD 3-clause license at https://github.com/PrincetonUniversity/shae-cellshape-public and archived at https://doi.org/10.5281/zenodo.1248978.
Article and author information
Author details
Funding
National Institutes of Health (R01 AI136946)
- Nina Reda Salama
National Science Foundation (DGE-1256082)
- Jennifer A Taylor
- Kris M Blair
Department of Defense (National Defense Science & Engineering Graduate Fellowship (NDSEG))
- Jennifer A Taylor
GO-MAP (Graduate Opportunity Program Research Assistantship Award)
- Sophie R Sichel
National Science Foundation (PHY-1734030)
- Benjamin P Bratton
- Josh W Shaevitz
Glenn Centers for Aging Research
- Benjamin P Bratton
National Institutes of Health (R21 AI121828)
- Benjamin P Bratton
- Josh W Shaevitz
National Institutes of Health (GM113172)
- Michael S VanNieuwenhze
National Institutes of Health (U01 CA221230)
- Catherine L Grimes
- Nina Reda Salama
National Institutes of Health (T32 CA009657)
- Kris M Blair
National Institutes of Health (T32 GM95421)
- Sophie R Sichel
National Institutes of Health (T32 GM008550)
- Kristen E DeMeester
National Institutes of Health (P30 CA015704)
- Nina Reda Salama
National Center for Research Resources (Stanford Imaging Award Number 1S10OD01227601)
- Nina Reda Salama
Wellcome (101824/Z/13/Z)
- Waldemar Vollmer
National Science Foundation (DGE-0718124)
- Jennifer A Taylor
The funders had no role in study design, datacollection and interpretation, or the decision to submit the work for publication. The opinions, findings, and conclusions or recommendationsexpressed in this material contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCRR,the National Institutes of Health, the Department of Defense, or the National Science Foundation.
Reviewing Editor
- Tâm Mignot, CNRS-Aix Marseille University, France
Version history
- Received: October 5, 2019
- Accepted: January 7, 2020
- Accepted Manuscript published: January 9, 2020 (version 1)
- Version of Record published: February 11, 2020 (version 2)
Copyright
© 2020, Taylor 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
-
- 4,109
- views
-
- 532
- downloads
-
- 39
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
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)
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.