1. Microbiology and Infectious Disease

Molecular coordination of Staphylococcus aureus cell division

  1. Victoria A Lund
  2. Katarzyna Wacnik
  3. Robert D Turner
  4. Bryony E Cotterell
  5. Christa G Walther
  6. Samuel J Fenn
  7. Fabian Grein
  8. Adam JM Wollman
  9. Mark C Leake
  10. Nicolas Olivier
  11. Ashley Cadby
  12. Stéphane Mesnage
  13. Simon Jones
  14. Simon J Foster  Is a corresponding author
  1. University of Sheffield, United Kingdom
  2. University of Bonn, Germany
  3. University of York, United Kingdom
https://doi.org/10.7554/eLife.32057
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Cite this article
  1. Victoria A Lund
  2. Katarzyna Wacnik
  3. Robert D Turner
  4. Bryony E Cotterell
  5. Christa G Walther
  6. Samuel J Fenn
  7. Fabian Grein
  8. Adam JM Wollman
  9. Mark C Leake
  10. Nicolas Olivier
  11. Ashley Cadby
  12. Stéphane Mesnage
  13. Simon Jones
  14. Simon J Foster
(2018)
Molecular coordination of Staphylococcus aureus cell division
eLife 7:e32057.
https://doi.org/10.7554/eLife.32057
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Abstract

The bacterial cell wall is essential for viability, but despite its ability to withstand internal turgor must remain dynamic to permit growth and division. Peptidoglycan is the major cell wall structural polymer, whose synthesis requires multiple interacting components. The human pathogen Staphylococcus aureus is a prolate spheroid that divides in three orthogonal planes. Here, we have integrated cellular morphology during division with molecular level resolution imaging of peptidoglycan synthesis and the components responsible. Synthesis occurs across the developing septal surface in a diffuse pattern, a necessity of the observed septal geometry, that is matched by variegated division component distribution. Synthesis continues after septal annulus completion, where the core division component FtsZ remains. The novel molecular level information requires re-evaluation of the growth and division processes leading to a new conceptual model, whereby the cell cycle is expedited by a set of functionally connected but not regularly distributed components.

https://doi.org/10.7554/eLife.32057.001

Introduction

In order to grow and divide, bacteria must make new cell wall, the major structural component of which is peptidoglycan (Turner et al., 2014). Bacteria generally have two groups of proteins that co-ordinate peptidoglycan insertion, one involved with elongation (elongasome), the other with division (divisome) (Cabeen and Jacobs-Wagner, 2005). S. aureus lacks an apparent elongasome machinery, but nonetheless new peptidoglycan is inserted all over the cell surface, throughout the cell cycle, not just during cell division (Monteiro et al., 2015; Zhou et al., 2015). Addition of peptidoglycan, along with its hydrolysis (Wheeler et al., 2015), is what enables S. aureus cells to get bigger – volume increases at a constant rate (Zhou et al., 2015).

The S. aureus divisome contains both enzymes that catalyse addition of new monomers to the peptidoglycan envelope (penicillin-binding proteins, PBPs), and proteins that co-ordinate this activity. Chief amongst these is FtsZ - an essential protein in almost all bacteria that directs cell division, which has recently been shown to form dynamic filaments that ‘treadmill’ in Escherichia coli and Bacillus subtilis, giving a framework to assemble other division proteins resulting in cell wall biosynthesis and septum formation (Yang et al., 2017; Bisson-Filho et al., 2017). FtsZ assembly into the Z-ring is regulated by other cell division components including EzrA (Levin et al., 1999; Adams and Errington, 2009), a membrane protein crucial for cell division in S. aureus (Steele et al., 2011). It has been shown to interact with both cytoplasmic proteins and those with periplasmic domains and it is therefore proposed to act as an interface between FtsZ and PBPs forming a scaffold for other cell division components (Steele et al., 2011).

Previously, FtsZ and EzrA in S. aureus have been imaged using fluorescent fusions (Strauss et al., 2012; Pereira et al., 2016) and sites of peptidoglycan insertion using fluorescent d-amino acids (Monteiro et al., 2015; Kuru et al., 2012). Here, we have applied single molecule localisation microscopy, a technique that provides unprecedented detail compared with other approaches. This has revealed an unexpected arrangement of division proteins and associated peptidoglycan insertion pattern. This defies the conventional view of division in S. aureus and has prompted a new model that encompasses the morphological idiosyncrasies of this important pathogen.

Results

Distribution of divisome components during septation

In order to visualise division machines, we localised the cytoplasmic initiator of division FtsZ and the crucial membrane protein EzrA (Steele et al., 2011). Four fusions of EzrA with different fluorophores were created. These had wild-type growth rates and the previously observed septal EzrA localization pattern (Steele et al., 2011; Jorge et al., 2011) by diffraction limited microscopy (Figure 1—figure supplement 1). Localisation microscopy and 3D structured illumination microscopy (3D-SIM) were used to address the distribution and juxtaposition of the cell division components at super-resolution.

3D-SIM revealed that EzrA exhibited punctate distribution at the division site (Figure 1—figure supplement 2a) (Strauss et al., 2012). Unfortunately, the ‘honeycomb’ artefact (which introduces foci in images due to incomplete noise filtering [Komis et al., 2015]), visible in our images, could not be removed by raising the Weiner filter parameter in reconstructions. Thus, localisation microscopy was employed as a superior approach.

eYFP was selected as a blinking fluorescent protein tag (Biteen et al., 2008). Multiple 2D images of septa in the plane of focus were obtained for EzrA-eYFP (Figure 1a), FtsZ-eYFP (Figure 1b) and EzrA-meYFP (Figure 1—figure supplement 2b). The mean localisation precision of eYFP was calculated using two different formulae: the ‘Thompson Equation’ (Thompson et al., 2002) by the ThunderSTORM ImageJ plugin yielded 24 (s.d. 8.5) nm while a using a modified version of this equation (Mortensen et al., 2010) yielded 27 (s.d. 8.7) nm. We also measured it experimentally using Nearest Neighbour in Adjacent Frames (NeNA) analysis (Endesfelder et al., 2014): NeNA analysis determines localisation precision based on spatial proximity of blinks that occur at similar times and is part of a family of clustering-based tools for assessing the quality of localisation microscopy data (Coltharp et al., 2012). This method gave us a mean localization precision of 16.2 nm. Many of the septa appeared to be somewhat elliptical. This is likely due to the cells being tilted relative to the plane of focus leading to circular septa appearing elliptical. We therefore fitted ellipses to the septal localisations and calculated the expected tilt of the cells. The results were that all of the localisations included in our analysis are within a 400 nm optical section, within a range to ensure good data (Palayret et al., 2015).

Figure 1 with 4 supplements see all
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Distribution of cell division components during septation.

(a) Examples of EzrA distributions obtained using localisation microscopy of SH4388 (ezrA-eyfp ΔezrA). Scale bars 200 nm. (b) Examples of FtsZ distributions obtained using localisation microscopy of SH4665 (pCQ11-FtsZ-eYFP) grown with 50 µM IPTG. Scale bars 200 nm. (c) Simulated distributions of localisations randomly distributed by angle with different radii (r), number of localisations (n) and random error from a normal distribution with standard deviation (σ) [i] r = 440 nm, n = 1118, σ = 20 nm, [ii] r = 440 nm, n = 1118, σ = 40 nm, [iii] r = 440 nm, n = 1118, σ = 80 nm, [iv] r = 440 nm, n = 145, σ = 20 nm, [v] r = 440 nm, n = 2010, σ = 20 nm. Scale bars 200 nm. (d) An enlarged example of EzrA-eYFP distribution. Scale bar 200 nm. (e) The distribution from ‘d’ plotted as a scatter graph, and as histograms of number of localisations with respect to angle and distance from centre. (f) Mean angular autocorrelations of 14 EzrA, 19 FtsZ and 15 simulated distributions. Autocorrelation drops less quickly for EzrA and FtsZ than for simulations where angle is randomised. This shows that neither EzrA or FtsZ are randomly distributed by angle. (g) Histograms of localisations with respect to distance from the centre of a fitted circle with varying localisation precision. Data for EzrA and FtsZ are spread more widely than simulated data with poor localisation precision.

https://doi.org/10.7554/eLife.32057.002

To analyse the distributions and address issues of sampling and resolution in our microscopy, a number of simple simulations were carried out where representative numbers of localisations were distributed at random in rings of similar radius to those observed, with a random error applied (Figure 1c). A circle was fitted to the data points and all the distributions (experimental and simulated) were parameterised with respect to angle and distance from the centre of the circle, generating histograms of localisations (Figure 1d,e). The autocorrelations of the angular distributions were then averaged to show that the localisations in the experimental data were neither completely randomly, or regularly, distributed around the ring (Figure 1f). Distributions of distance from the centre of the circle were compared with simulated distributions of a fixed circle radius where different levels of localisation precision error were applied (Figure 1g). Even with the most conservative assumptions (including simulated localisation precisions worse than we had calculated for our measured data), the localisations were spread out over a sufficiently wide range of distances to indicate that both FtsZ or EzrA do not form a very thin ring at the leading edge of the septum in S. aureus. Instead both proteins appear in a non-uniform distribution within the septal annulus. Within the annulus the proteins show no discernible pattern within or across cells. FtsZ distributions were consistent with FtsZ remaining in the division plane after septal fusion were also observed (Figure 2a).

Figure 2
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Relative locations of division components.

(a) Localisation microscopy images: of FtsZ-eYFP distributions in bacteria in the late stages of division. Scale bars 500 nm. Ellipses show approximate cell location and orientation. (b) Dual colour localisation microscopy image of FtsZ-eYFP and the cell wall (labelled with Alexa Fluor 647 NHS ester, NHS-647). Scale bars 500 nm. (c) Dual colour localisation microscopy image of EzrA-eYFP and the cell wall (labelled with NHS-647). Scale bars 500 nm.

https://doi.org/10.7554/eLife.32057.007

To further investigate whether the apparent elliptical shape of the rings had an influence on our interpretation, we also analysed the data using an elliptical, rather than a circular fit. Comparing our results to simulated data (Figure 1—figure supplement 3) corroborated our previous findings.

To place these findings in the context of cell wall shape, two colour localisation microscopy was performed where the cell wall was labelled with an Alexa Fluor 647 NHS ester (Figure 2b,c), which labels all amine groups in the cell wall (Zhou et al., 2015). This confirmed that EzrA and FtsZ were at the expected septal positions in the cell.

To analyse rapid molecular dynamics of EzrA, single-molecule Slimfield microscopy (Plank et al., 2009) was performed on EzrA-meYFP labelled S. aureus, SH4604 (ezrA-meyfp ΔezrA) optimized to enable blur-free tracking of single fluorescent protein fusion constructs in live cells over a millisecond timescale (Reyes-Lamothe et al., 2010; Badrinarayanan et al., 2012). Analysis of the mobility of tracked EzrA-meYFP foci enabled quantification of their microdiffusion coefficient (D), indicating a mixture of three different mobility components: an apparent immobile population in addition to an intermediate and a rapid mobility population (Figure 1—figure supplement 4a,b). In total, ~600 EzrA foci tracks were analysed in the septum region, whose overall mean D value, which captures both the immobile and two mobile populations, was 0.20 ± 0.01 μm2 s−1. Whereas, 140 foci tracks were detected outside the septum region, which showed an increased overall mean D of 0.28 ± 0.03 µm2 s−1. This greater average mobility was principally due to an increase in the proportion of EzrA foci present in the most mobile component (going from 33 ± 3% of the total to 42 ± 4%).

These relatively slow mobility values for EzrA, compared to many freely diffusing bacterial membrane integrated proteins (Leake et al., 2008), do not preclude putative rotational/treadmilling motions of EzrA (which have been observed in previous studies of FtsZ mobility in E. coli and B. subtilis [Yang et al., 2017; Bisson-Filho et al., 2017]) over a longer time scale. For example, the mean speed of putative FtsZ treadmilling estimated from B. subtilis recently (Bisson-Filho et al., 2017) is only ~30 nm/s, which we estimate would be sufficiently slow to appear predominantly in the immobile component over the typical time scales of our Slimfield tracking experiments here, and so putative treadmilling of EzrA at this equivalent mean speed, if present in S. aureus, would most likely appear in this apparent immobile fraction. However, in the three component mobility model, which fits the observed distribution of D values well, the intermediate mobility fraction has been interpreted previously in other cellular systems as indicating transient dynamic interactions (Stracy et al., 2015), and so we cannot entirely exclude the possibility that this may be due to transient association of EzrA with FtsZ. Deconvolution analysis (Wollman and Leake, 2015) of whole cell images obtained using Slimfield microscopy indicated a mean total copy number of 305 ± 23 EzrA molecules per cell measured across a population (Figure 1—figure supplement 4c). Estimating the proportion of the most mobile fraction of EzrA foci therefore indicates that at least ~100 EzrA molecules per cell are not likely to be treadmilling in tight association with FtsZ. In other words, we cannot account for the observed mobility of EzrA by a simple treadmilling model alone in which all EzrA is tightly associated with FtsZ, rather the real cellular behaviour is more complex than this.

Peptidoglycan synthesis in S. aureus does not occur in discrete foci

We used established metabolic labelling with fluorescent d-amino acids or dipeptides (Monteiro et al., 2015; Kuru et al., 2012) and adapted this for localisation microscopy to visualise peptidoglycan insertion with this higher resolution imaging technique. We confirmed that HADA (7-hydroxycoumarin-3-carboxylic acid-amino-d-alanine), ADA (azido d-alanine) and ADA-DA (azido-d-alanyl-d-alanine) mark regions of new peptidoglycan insertion by microscopy and liquid chromatography-mass spectrometry (LC-MS) (Figure 3—figure supplement 1).

Cells were pulse labelled with DAAs (d-amino acids) from 15 s to 5 min. Even at the very shortest labelling time (15 s) peptidoglycan synthesis was observed both at the septum and cell periphery but without discrete foci (Figure 3—figure supplement 2a,b). Localisation microscopy of 15 s ADA and ADA-DA labelled cells revealed labelling occurs dispersed across the whole septum as well as the off-septal cell wall (Figure 3a, Figure 3—figure supplement 2d). This was not due to non-specific labelling (Figure 3—figure supplement 2c). XY localisation precision (estimated by the Nikon N-STORM software) was 9.9 (s.d. 3.5) nm or 7.5 nm by NeNA (Endesfelder et al., 2014). A similar pattern of peptidoglycan synthesis was seen with up to 5 min labelling with ADA or ADA-DA as a zone across the developing septum as well as throughout the off-septal cell wall (Figure 3b,c,d). Previously PBP4 has been implicated in the presence of off-septal incorporation (Monteiro et al., 2015; Gautam et al., 2015), we therefore carried out DAA labelling and localisation microscopy in a PBP4 null background (SH4425) (Figure 3—figure supplement 3). Cell growth and GlcNAc incorporation were found to be the same as WT, however DAA labelling was reduced in SH4425 (Figure 3—figure supplement 3b–d). The proportion of off-septal labelling was calculated in both SH1000 and SH4425 when labelled with ADA-DA, however no significant difference was observed (Figure 3—figure supplement 3e). Localisation microscopy of both 15 s and 5 min labelled SH4425 showed peptidoglycan synthesis both at the septal and peripheral cell wall. Discrete foci of insertion were not observed (Figure 3—figure supplement 3f–g). Comparison of autocorrelations (as calculated for EzrA and FtsZ, using elliptical fits) for SH1000 and SH4425 revealed no substantial differences (Figure 3—figure supplement 3h).

Figure 3 with 3 supplements see all
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Peptidoglycan insertion.

Localisation microscopy images: (a) 15 s labelling of ADA clicked to Alexa Fluor 647. Scale bars 0.5 µm. (b) 5 min labelling of (i) ADA clicked to Alexa Fluor 647 and (ii) ADA-DA clicked to Alexa Fluor 647. Scale bars 1 µm. (c) 3D projections of S. aureus labelled for 5 min with ADA clicked to Alexa Fluor 647. (i) Cells with incomplete septum (yellow arrows show gaps in labelling), (ii) cell with annulus complete. Images in black boxes are z-projections while 3D representations show projections in all three planes. Scale bar 0.5 µm. (d) Cross sections of incomplete septa. The sketch graph (top row) hypothetically shows labelling exclusively at the leading edge of the septum. This is not the case for the data shown below - labelling is spread throughout the septum. The full width half maximum spread of labelling is ~230 nm. Data are plotted with blue dots, fits in red lines. (e) Two colour STORM, sample labelled for 5 min with ADA-DA clicked to Alexa Fluor 647 (yellow) and vancomycin linked to Amersham Cy3B (magenta). Images are z-projections and in merged images where localisations are in white show labelling by both ADA-DA and vancomycin. Boxed regions show slot in ADA-DA labelling but not vancomycin. Scale bars 1 µm.

https://doi.org/10.7554/eLife.32057.008

In cells with an incomplete septum, there was a ‘gap’ in peptidoglycan synthesis at the mother cell wall-septum interface (Figure 3c–i, arrows). In order to investigate the properties of the observed ‘gap’ we used a counter stain to determine if it is filled with peptidoglycan. Fluorescent vancomycin has been used extensively to label peptidoglycan (Daniel and Errington, 2003). Thus, we synthesised a version of this molecule with a Cy3B fluorophore so it could be used in two colour localisation microscopy with Alexa Fluor 647 click tagged amino acids. Vancomycin binds d-alanyl-d-alanine motifs in peptidoglycan and as these are highly prevalent in S. aureus the majority of peptidoglycan is fluorescently labelled. Our two colour images show that the ‘gap’ regions that do not contain ADA-DA (5 min labelling), are nonetheless bound by vancomycin and thus are filled with peptidoglycan (Figure 3e).

Also, cells with a filled septal annulus showed continued insertion that could be resolved into two distinct zones, one for each daughter (Figure 3c–ii). These features were not observable by SIM, being smaller than its theoretical resolution.

Inhibition of cell division leads to co-mislocalization of the cell division components and peptidoglycan synthesis

The FtsZ inhibitor PC190723 prevents depolymerisation of FtsZ and consequently inhibits cell division, also leading to swollen S. aureus cells (Haydon et al., 2008). It has previously been shown by diffraction limited fluorescence microscopy that PC190723 causes mislocalisations of FtsZ and PBP2 (Tan et al., 2012). We sought to determine the dynamics of this process, and the molecular pattern of associated peptidoglycan insertion. PC190723 treatment led to delocalization of peptidoglycan biosynthesis, EzrA and FtsZ even before substantial cell swelling (Figure 4—figure supplement 1). Incorporation of HADA does not cause mislocalisation of FtsZ or EzrA (data not shown). Peptidoglycan synthesis was observed around the cell periphery and in distinct foci in the same place as EzrA and FtsZ. This non-uniform peptidoglycan insertion results in misshapen cells with irregular thickening of the cell wall (Figure 4a). After 60 min treatment, patches of FtsZ, EzrA and peptidoglycan synthesis can be seen (Figure 4—figure supplement 1a). Localisation microscopy of peptidoglycan synthesis shows cell shape and the off-septal synthesis with patches of increased synthesis more clearly (Figure 4b). Thus, peptidoglycan synthesis follows localization of FtsZ and EzrA.

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Effect of FtsZ inhibitor PC190723 on S. aureus.

(a) TEM of S. aureus SH1000 grown in the presence of PC190723 (10 µg ml−1) for 60 min. Scale bars 200 nm. (b) STORM image of S. aureus SH1000 pre-treated with PC190723 (10 µg ml−1) for 60 min labelled with ADA clicked to Alexa Fluor 647 for 5 min. Scale bar 1 µm. (i) and (ii) zoomed images of the corresponding area, scale bars 0.25 µm.

https://doi.org/10.7554/eLife.32057.012

Morphology of the Staphylococcus aureus septum

It has been shown that the incomplete S. aureus septum is thinner at the leading than at the lagging edge (Giesbrecht et al., 1998; Matias and Beveridge, 2007). However, the significance of this has remained unknown. We observed sections of cells from different stages in the cell cycle and measured septal geometry using thin section Transmission Electron Microscopy (TEM). The septum of S. aureus is thinner at the leading edge and progressively thicker towards the lagging edge until it fuses, at which point it is thinner at the centre and progressively thicker towards the lagging edge until ultimately uniform thickness is established (Figure 5a,b). This dictates that peptidoglycan insertion cannot be confined to the leading edge of the septum and gives a morphological explanation for the observed peptidoglycan insertion pattern.

Figure 5
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Conceptual model of peptidoglycan insertion during the S. aureus cell cycle.

(a) Schematic of measurement used in (b) measurement of the angle (θ) between a line parallel to the surface of the septum (yellow) and a tangent to the surface of the bacterium in incomplete (blue) and complete (red) septa. (c) Surfaces available for peptidoglycan insertion for different septal geometries where d is half the thickness of the septum, r is the cell radius in the septal plane and s is the distance from the leading to the lagging edge of the septum (measured from the inner surface of the cell wall). (d) Conceptual model of peptidoglycan insertion in S. aureus. (i, ii) Cell size increases and aspect ratio changes prior to observation of the start of septum formation by 3D-SIM (Monteiro et al., 2015). (iii) The septum then starts to form, beginning with the ‘piecrust’ feature (red) observed by AFM (Turner et al., 2010). The septum is thinner at the leading edge (Matias and Beveridge, 2007). (iv) New peptidoglycan is inserted in a zone at the leading edge of the septum, as well as across the rest of the cell surface as visualised here by localisation microscopy. (v, vi) After the annulus has fused, peptidoglycan insertion continues in the septum, executed by cell division components, until it is of uniform thickness. (vii) ATL (a peptidoglycan hydrolase) is present at the outer surface of the cell in the plane of septation(Komatsuzawa et al., 1997). Cracks or splits begin to form at the outer surface in the plane of septation(Touhami et al., 2004), followed by rapid popping apart of the daughter cells (Zhou et al., 2015). (vii) ‘Scars’ or ‘ribs’ remain marking the site of division (Monteiro et al., 2015; Turner et al., 2010) and may provide spatial cues to subsequently enable correct sequentially orthogonal divisions.

https://doi.org/10.7554/eLife.32057.014

The surface area available for peptidoglycan insertion in the nascent septum was modelled resulting in the following expression for septal surface area prior to fusion (Figure 5c):

A=π(2r−s)s2+d2

Where d is half the thickness of the septum, r is the radius of the cell in the plane of septation and s is the distance from the leading to the lagging edge of the septum (measured from the inner surface of the cell wall).

The surface area of a septum with consistently uniform thickness is that of the leading edge of that septum:

A=4π(r−s)d

Not only is the available surface area always larger for the morphology we observe, but it increases as the septum closes (whereas with a uniformly thick septum, it decreases). This provides a framework for septal synthesis in an organism in which the septum comprises a substantial proportion of the cell wall.

Discussion

The non-standard cross section of the septum in S. aureus distinguishes it from other model organisms (Figure 5a,b,c) and indicates that not all peptidoglycan insertion occurs at the leading edge of the septum in this species prompting the development of a new model for how peptidoglycan is synthesised during the cell cycle (Figure 5d). This is likely advantageous to the bacteria, enabling more biosynthetic enzymes to work on the cell wall without steric hindrance. We sought to explain this phenomenon by analysing the distribution of peptidoglycan insertion and investigating key cell division components. Our novel application of localisation microscopy to DAAs revealed that even at the shortest timescales and with considerably more precision than previous studies (Monteiro et al., 2015; Zhou et al., 2015; Turner et al., 2010), there were no foci of peptidoglycan insertion – the diffuse pattern throughout the septum and periphery of the cell was ever-present. This surprising finding was corroborated by the distribution of core cell division components in S. aureus. Localisation microscopy of FtsZ and EzrA in the septal ring showed, like the distribution of peptidoglycan insertion, that they occurred in a zone, and were not limited to the leading edge of the septum. Also, FtsZ remained at the septum after the annulus had fused. When FtsZ depolymerisation was inhibited, peptidoglycan insertion was found to occur in areas with large amounts of FtsZ, resulting in local thickening of the cell wall, suggesting all synthesis may depend on FtsZ. This is a different scenario to E. coli and B. subtilis, where division-associated foci of peptidoglycan synthesis have been identified (albeit without the precision of localisation microscopy) and associated with cell division components driven by treadmilling FtsZ filaments (Yang et al., 2017; Bisson-Filho et al., 2017).

The divisome has been proposed to be a multi-component machine, present within a ring, based on diffraction-limited microscopy and interaction studies (Steele et al., 2011; Bottomley et al., 2014). Previous localisation microscopy studies have begun to reveal intricate structural and spatial relationships between division components (Holden et al., 2014; Buss et al., 2015; Jacq et al., 2015).

Our data show that divisome components are not placed exclusively at the leading edge of the septum, and that some individual proteins move more rapidly than others. There may, therefore, be a number of essentially identical machines executing peptidoglycan insertion within a region of the septum, with exchange of machine components with a more mobile population of molecules. It could also be the case that the machines are very non-uniform and can execute their tasks with a subset of the complete list of divisome proteins and with more or less of an individual protein. Alternatively, stable, stoichiometric complexes are not present and the interactions between proteins required to make new peptidoglycan are highly transient.

Materials and methods

Bacterial growth conditions

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Strains used in this study are listed in Appendix 1—table 1, while plasmids and oligonucleotide sequences are shown in Appendix 1—table 2 and Appendix 1—table 3. S. aureus was grown in Brain Heart Infusion (BHI) broth at 37°C with aeration at 250 rpm, except for Slimfield microscopy and14C-GlcNAc incorporation experiments (and associated growth curves) which were carried out using Chemically Defined Media (CDM) (Hussain et al., 1991). For solid media 1.5% (w/v) agar was added. Where required, antibiotics were added at the following concentrations; erythromycin (5 µg ml−1), lincomycin (25 µg ml−1), kanamycin (50 µg ml−1), and tetracycline (5 µg ml−1). To induce protein production strains carrying gene fusions under the control of the Pspac promoter were grown in the presence of 50 µM isopropyl β-D-thiogalactopyranoside (IPTG).

Construction of S. aureus mutants

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All vectors were constructed in E. coli NEB5α (New England Biolabs) following previously described methods (Sambrook and Russell, 2001; Gibson et al., 2009). The resulting constructs were passed through a restriction-deficient S. aureus RN4220 before being transduced into a final S. aureus SH1000 strain. Transformation and phage transduction of S. aureus were carried out as described previously (Schenk and Laddaga, 1992; Novick and Morse, 1967).

SH4388 (ezrA-eyfp ΔezrA): The EzrA-eYFP fusion was created by AscI and NotI digestion of pGM074 and insertion of eyfp amplified by PCR from SU492 (Monahan et al., 2014) using primer pair eYFP-F and eYFP-R. pGM074 is pKASBAR-kan (Bottomley et al., 2014) containing ezrA under the control of its own promoter with the C-terminal psmorange (flanked by AscI and NotI restriction sites). In the resulting plasmid pKASBAR-EzrA-eYFP the translational fusion of ezrA-eyfp is linked by linker A (see below). pKASBAR-EzrA-eYFP was electroporated into CYL316 (Lee et al., 1991) and its integration at the geh locus was confirmed by disruption of lipase production on Baird-Parker medium. The chromosomal fragment containing the integrated plasmid was moved into S. aureus SH1000 by phage transduction, creating SH4384 (ezrA-eyfp).

To delete native ezrA, an ezrA deletion vector was constructed. Fragments encompassing ~1.5 kb regions flanking ezrA were PCR amplified from S. aureus SH1000 genomic DNA using pOB-ezrA-up-F/-R and pOB-ezrA-down-F/-R. A 2.1 kb fragment encoding a tetracycline resistance cassette (tetR) was amplified from pAISH by PCR using pOB-TetR-F/-R primers. The three PCR products were ligated with HindIII and EcoRI cut pOB (Horsburgh et al., 2002a) by Gibson assembly, creating a deletion vector pOB-ΔezrA. The plasmid pOB-ΔezrA was electroporated into RN4220. The plasmid integrated into the chromosome through a single cross-over event and the DNA fragment containing the deletion cassette was transduced into SH4386 (ezrA-eyfp). Tetracycline-resistant/erythromycin-sensitive colonies were selected. In the resulting strain, SH4388 (ezrA-eyfp ΔezrA), ezrA-eyfp was the only copy of the ezrA gene. Replacement of ezrA for tetR was confirmed by PCR and Southern blot.

SH4640 (ezrA-gfp ΔezrA): To construct an EzrA-GFP translational fusion linked by linker A, gfp was PCR amplified from JGL227 (Steele et al., 2011) using GFP-F/-R primers and ligated into AscI and EcoRI cut pGM074, creating pKASBAR-EzrA-GFP. The resulting plasmid was electroporated into CYL316. pKASBAR-EzrA-GFP integration at the geh locus was confirmed by disruption of lipase production on Baird-Parker medium. The chromosomal region containing the plasmid integrated within geh was moved to SH1000 creating SH4639 (ezrA-gfp). To delete native ezrA, SH4639 was transduced with a phage lysate from SH4388 (ezrA-eyfp ΔezrA), creating SH4640 (ezrA-gfp ΔezrA). Replacement of ezrA for TetR was confirmed by PCR and Southern blot.

SH4642 (ezrA-snap ΔezrA): A translational fusion of EzrA linked by linker A to the SNAP tag was constructed by PCR amplification of snap from pSNAP-tag (T7)−2 (New England Biolabs) using SNAP-F/-R primers. The PCR product was ligated into pGM074 using AscI and NotI cut sites to create pKASBAR-EzrA-SNAP. The resulting plasmid was electroporated into CYL316, and its integration at the geh locus was confirmed by disruption of lipase production on Baird-Parker medium. The chromosomal fragment containing integrated pKASBAR-EzrA-SNAP was transduced into SH1000, resulting in SH4641 (ezrA-snap). Native ezrA was replaced by tetR by transducing SH4641 with the phage lysate from SH4388 (ezrA-eyfp ΔezrA), creating SH4642 (ezrA-snap ΔezrA). Replacement of ezrA for tetR was confirmed by PCR and Southern blot.

SH4604 (ezrA-meyfp ΔezrA): To create a C-terminal fusion of EzrA with monomeric eYFP (meYFP) the whole pKASBAR-EzrA-eYFP plasmid was PCR amplified using meYFP-F/-R primers. The meYFP-F primer introduced an A206K substitution (Zacharias et al., 2002) into the eyfp gene. The PCR product was digested with DpnI to remove methylated DNA, the 5’ ends of DNA were phosphorylated with T4 polynucleotide kinase (New England Biolabs) and DNA was circularized using Quick-Stick ligase (Bioline), resulting in pKASBAR-EzrA-meYFP. The resulting plasmid was electroporated into CYL316. The chromosomal fragment containing the integrated plasmid in the geh locus was moved into S. aureus SH1000 by phage transduction, creating SH4603 (ezrA-meyfp), To delete native ezrA, SH4603 was transduced with a phage lysate from SH4388 (ezrA-eyfp ΔezrA), creating SH4604 (ezrA-meyfp ΔezrA). Replacement of ezrA for tetR was confirmed by PCR and Southern blot.

SH4652 (ezrA-eyfp ΔezrA pCQ11-FtsZ-SNAP): In order to construct a strain simultaneously producing EzrA-eYFP and FtsZ-SNAP, a plasmid encoding a translational ftsZ-snap fusion placed under the control of the Pspac promoter was constructed. The ftsZ gene was PCR amplified from S. aureus N315 genomic DNA using FGFtsZXhoI-F and FGFtsZEcoRI-R primers and cloned into EcoRI and XhoI cut pSS26b (Covalys), resulting in pSS26bFtsZ-C. The fragment encoding ftsZ-snap was PCR amplified from pSS26bFtsZ-C using FGFtsZNheI-F and FGFtsZAscI-R and inserted into pCQ11 (Hardt et al., 2017) using NheI and AscI cut sites, creating pCQ11-FtsZ-SNAP. The plasmid was electroporated into RN4220 and moved to SH4388 (ezrA-eyfp ΔezrA) by phage transduction, resulting in SH4652 (ezrA-eyfp ΔezrA pCQ11-FtsZ-SNAP).

SH4665 (pCQ11-FtsZ-eYFP): To construct a translational fusion of FtsZ with eYFP, an insert containing a fragment of linker B (see below) followed by a full length eyfp gene was synthesized by the GeneArt Gene Synthesis service, PCR amplified using ftsZ-eyfp-F/-R primers and cloned into NcoI and AscI cut pCQ11-FtsZ-SNAP, creating pCQ11-FtsZ-eYFP. The plasmid was electroporated to RN4220 and moved to SH1000 by phage transduction, resulting in SH4665 (pCQ11-FtsZ-eYFP).

SH4425 (pbp4): NE679 (pbp4) containing a transposon insertion within the pbp4 gene was obtained from NARSA library (Fey et al., 2013). SH1000 was transduced with a phage lysate from NE679. Insertion of the transposon within pbp4 in resulting SH4425 (pbp4) was confirmed by PCR and sequencing.

Sequences of genes encoding fluorescent proteins, tags and linkers

eyfp in pKASBAR-EzrA-eYFP

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ATGGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCTTCGGCTACGGCCTGCAGTGCTTCGCCCGCTACCCCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTACGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGCGCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTACAACTACAACAGCCACAACGTCTATATCATGGCCGACAAGCAGAAGAACGGCATCAAGGTGAACTTCAAGATCCGCCACAACATCGAGGGCGGCAGCGTGCAGCTCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCTACCAGTCCGCCCTGAGCAAAGACCCCAACGAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCACTCTCGGCATGGACGAGCTGTACAAG

eyfp in pCQ11-FtsZ-eYFP

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ATGGTTTCAAAAGGTGAAGAATTATTCACAGGTGTTGTTCCAATTTTGGTTGAATTAGATGGTGATGTTAATGGTCATAAATTCTCAGTTTCAGGTGAAGGTGAAGGTGATGCAACATATGGTAAATTAACATTAAAATTTATTTGTACAACAGGTAAATTACCAGTTCCTTGGCCAACATTAGTTACAACATTCGGTTATGGTTTACAATGTTTTGCACGTTATCCAGATCATATGAAACAACATGATTTTTTCAAATCAGCAATGCCTGAAGGTTATGTTCAAGAACGTACAATTTTCTTTAAAGATGATGGTAATTACAAAACACGTGCTGAAGTGAAATTTGAAGGTGATACATTAGTTAATCGTATTGAATTAAAAGGTATTGATTTTAAAGAAGATGGAAATATTTTAGGTCATAAATTAGAATATAATTATAATTCACATAATGTTTATATTATGGCAGATAAACAAAAAAATGGTATTAAAGTTAATTTCAAAATTCGTCATAATATTGAAGGTGGTTCAGTTCAATTAGCAGATCATTATCAACAAAATACACCTATTGGTGATGGTCCAGTTTTATTACCAGATAATCATTATTTATCATATCAATCAGCATTATCAAAAGATCCAAATGAAAAACGTGATCATATGGTTTTATTAGAATTTGTTACAGCAGCAGGTATTACATTAGGTATGGATGAATTATATAAATAA

gfp in pKASBAR-EzrA-GFP

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ATGGCTAGCAAAGGAGAAGAACTTTTCACTGGAGTTGTCCCAATTCTTGTTGAATTAGATGGTGATGTTAATGGGCACAAATTTTCTGTCAGTGGAGAGGGTGAAGGTGATGCTACATACGGAAAGCTTACCCTTAAATTTATTTGCACTACTGGAAAACTACCTGTTCCATGGCCAACACTTGTCACTACTTTGACCTATGGTGTTCAATGCTTTTCCCGTTATCCGGATCATATGAAACGGCATGACTTTTTCAAGAGTGCCATGCCCGAAGGTTATGTACAGGAACGCACTATATCTTTCAAAGATGACGGGAACTACAAGACGCGTGCTGAAGTCAAGTTTGAAGGTGATACCCTTGTTAATCGTATCGAGTTAAAAGGTATTGATTTTAAAGAAGATGGAAACATTCTCGGACACAAACTCGAGTACAACTATAACTCACACAATGTATACATCACGGCAGACAAACAAAAGAATGGAATCAAAGCTAACTTCAAAATTCGCCACAACATTGAAGATGGATCCGTTCAACTAGCAGACCATTATCAACAAAATACTCCAATTGGCGATGGCCCTGTCCTTTTACCAGACAACCATTACCTGTCGACACAATCTGCCCTTTCGAAAGATCCCAACGAAAAGCGTGACCACATGGTCCTTCTTGAGTTTGTAACTGCTGCTGGGATTACACATGGCATGGATGAGCTCTACAAATAA

snap in pSNAP-tag (T7)−2 and pKASBAR-EzrA-SNAP

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ATGGACAAAGACTGCGAAATGAAGCGCACCACCCTGGATAGCCCTCTGGGCAAGCTGGAACTGTCTGGGTGCGAACAGGGCCTGCACCGTATCATCTTCCTGGGCAAAGGAACATCTGCCGCCGACGCCGTGGAAGTGCCTGCCCCAGCCGCCGTGCTGGGCGGACCAGAGCCACTGATGCAGGCCACCGCCTGGCTCAACGCCTACTTTCACCAGCCTGAGGCCATCGAGGAGTTCCCTGTGCCAGCCCTGCACCACCCAGTGTTCCAGCAGGAGAGCTTTACCCGCCAGGTGCTGTGGAAACTGCTGAAAGTGGTGAAGTTCGGAGAGGTCATCAGCTACAGCCACCTGGCCGCCCTGGCCGGCAATCCCGCCGCCACCGCCGCCGTGAAAACCGCCCTGAGCGGAAATCCCGTGCCCATTCTGATCCCCTGCCACCGGGTGGTGCAGGGCGACCTGGACGTGGGGGGCTACGAGGGCGGGCTCGCCGTGAAAGAGTGGCTGCTGGCCCACGAGGGCCACAGACTGGGCAAGCCTGGGCTGGGT

snap in pSS26b, pSS26bFtsZ-C and pCQ11-FtsZ-SNAP

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ATGGACAAAGATTGCGAAATGAAACGTACCACCCTGGATAGCCCGCTGGGCAAACTGGAACTGAGCGGCTGCGAACAGGGCCTGCATGAAATTAAACTGCTGGGTAAAGGCACCAGCGCGGCCGATGCGGTTGAAGTTCCGGCCCCGGCCGCCGTGCTGGGTGGTCCGGAACCGCTGATGCAGGCGACCGCGTGGCTGAACGCGTATTTTCATCAGCCGGAAGCGATTGAAGAATTTCCGGTTCCGGCGCTGCATCATCCGGTGTTTCAGCAGGAGAGCTTTACCCGTCAGGTGCTGTGGAAACTGCTGAAAGTGGTTAAATTTGGCGAAGTGATTAGCTATCAGCAGCTGGCGGCCCTGGCGGGTAATCCGGCGGCCACCGCCGCCGTTAAAACCGCGCTGAGCGGTAACCCGGTGCCGATTCTGATTCCGTGCCATCGTGTGGTTAGCTCTAGCGGTGCGGTTGGCGGTTATGAAGGTGGTCTGGCGGTGAAAGAGTGGCTGCTGGCCCATGAAGGTCATCGTCTGGGTAAACCGGGTCTGGGATGA

Linker A

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TCAGGTTCAGGTTCAGGTGGGCGCGCCTCAGGTTCAGGTTCAGGT

Linker B

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GAATTCCCCATGGGTTCAGGTGGTGGTGGTTCA

Labelling S. aureus with DAAs

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DAAs were prepared by published methods (Adams and Errington, 2009; Steele et al., 2011; Strauss et al., 2012) or by modified procedures described in Appendix. ADA was obtained from Iris Biotech. These were incubated with mid-exponential phase (OD600 ~0.3 to 0.4) S. aureus at 500 µM (1 mM for ADA-DA) and incubated on a rotary shaker at 37°C for the required labelling time. Samples were imaged using widefield microscopy, 3D-SIM or localisation microscopy as required. For 15 s labelling DAAs were used at 10 mM, 1 ml samples were mixed briefly by vortexing and fixed by addition of 500 µl 8% (w/v) ice-cold paraformaldehyde immediately after vortexing.

Click chemistry

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DAAs containing an azide functional group (ADA and ADA-DA) required chemical attachment of a fluorophore via the Click reaction (copper (I)-catalysed alkyne-azide cycloaddition). This was carried out using the Click-iT Cell Reaction Buffer Kit (ThermoFisher) as per the manufacturer's protocol. Alkyne dyes were added at 5 µg ml−1.

Labelling S. aureus with fluorescent vancomycin

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Fixed cells were resuspended in PBS containing fluorescent vancomycin at 2 µM (prepared using succinimidyl ester of Amersham Cy3B (GE Healthcare) as previously described (Daniel and Errington, 2003). Samples were protected from light and incubated at room temperature for 30 min then washed by centrifugation and resuspension in water. For dual labelled samples, cells were labelled with required DAA as described above and fixed with 4% (w/v) paraformaldehyde prior to labelling with fluorescent vancomycin.

Labelling S. aureus with NHS ester

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S. aureus grown to mid-exponential phase (OD600 ~0.5) were resuspended in PBS containing Alexa Fluor 647 NHS ester (Invitrogen) at 8 μg ml−1 and incubated at room temperature for 5 min. Cells were then washed by centrifugation and resuspension in PBS.

Labelling S. aureus with SNAP-Cell TMR-Star

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S. aureus grown to mid-exponential phase (OD600 ~0.5) were incubated with SNAP-Cell TMR-Star (New England Biolabs) at 500 nM for widefield microscopy or 3 μM for SIM at 37°C for 15 min. Cells were washed by centrifugation and resuspension in PBS.

Fixing

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With the exception of Slimfield microscopy which involved no fixation and 15 s DAA labelling which used 8% (w/v) ice-cold paraformaldehyde, all samples were fixed with 4% (w/v) paraformaldehyde prior to imaging.

Widefield epifluorescence microscopy

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Fixed cells were mounted on poly-l-Lysine coated slides and imaged on a Nikon Ti Inverted microscope fitted with a Lumencor Spectra X light engine. Images were taken using a 100x PlanApo (1.4 NA) oil objective using 1.518 RI oil and detected by an Andor Zyla sCMOS camera.

OMX microscopy

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Coverslips (High-precision, No.1.5H, 22 × 22 mm, 170 ± 5 µm, Marienfeld) were sonicated for 15 min in 1 M KOH, washed with water and incubated in poly-l-Lysine solution for 30 min. Coverslips were then further washed and dried with nitrogen. Fixed cells were then dried onto the coverslips with nitrogen and mounted on slides with ~5 µl Slow Fade Diamond (Invitrogen).

Structured Illumination Microscopy was carried out using a v4 DeltaVision OMX 3D-SIM system fitted with a Blaze module (Applied Precision, GE Healthcare, Issaquah, USA). Samples were illuminated using laser illumination. For each z slice, samples were imaged in five phase shifts and three angles, z-steps were 0.125 nm. Reconstructions were performed with the Softworx software (GE Healthcare) using OTFs optimised for the specific wavelength and oil used. The same software was used for deconvolution.

Sample preparation for localisation microscopy

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For all samples coverslips were prepared as for 3D-SIM Microscopy. All samples except for eYFP/meYFP and were mounted on slides with 5 µl GLOX buffer (0.5 mg ml−1 glucose oxidase, 40 µg ml−1 catalase, 10% (w/v) glucose in 50 mM Tris-HCl containing 10 mM NaCl (pH 8.0) containing either 10 or 100 mM mercaptoethylamine (MEA).

For eYFP/meYFP imaging (single colour) samples were mounted in 5 μl PLOX buffer (5 U ml−1 pyranose oxidase, 40 µg ml−1 catalase, 10% (w/v) glucose in 50 mM Tris-HCl, 10 mM NaCl (pH 8.0) prepared in heavy water (Ong et al., 2015).

For eYFP/Alexa Fluor 647 imaging (two-colour) samples were mounted in 5 μl PLOX containing 50 mM MEA. Where required, coverslips were sparsely coated with TetraSpeck beads (0.1 µm, Molecular Probes) prior to the application of cells.

Bespoke localisation microscope

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Localisation microscopy was carried out as previously described (Huang et al., 2010; Turner et al., 2013), but using OBIS 405 (50 mW) and OBIS 647 (120 mW) lasers, a 662 nm dichroic and a 676 (Daniel and Errington, 2003) nm emission filter. Calibration data for 3D reconstructions was obtained by recording images of fiducial particles while stepping the objective piezo.

Nikon N-STORM localisation microscope

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Localisation microscopy was carried out using a Nikon Ti-NS N-STORM version 1 with 3D capability in continuous mode. Objective used was a SR Apo TIRF 100x NA 1.49 and images detected using EMCCD camera (Andor DU-897) using the 17 MHz 16 bit mode with an EM Multiplier Gain of 300 and a conversion gain of 3. Calibration data for 3D reconstructions was obtained by recording images of fiducial particles using the calibration mode. Custom-made filter cubes were used for eYFP/meYFP (no excitation filter, 488 nm dichroic, 525/50 nm emission) and two-colour imaging (red/far red; no excitation filter, multi-band dichroic with transmission at 410–480 nm, 500–550 nm, 570–630 nm and above 650 nm, multi-band emission with transmission at 570–620 nm and above 660 nm) imaging and the N-STORM cube for single colour Alexa Fluor 647 imaging. Imaging was done under oblique illumination but not full TIRF. Two colour eYFP and Alexa Fluor 647 imaging was performed using separate filter cubes whereas two colour imaging using Cy3B and Alexa Fluor 647 was performed using a single cube, as specified.

Image reconstruction

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Images were reconstructed as previously described (Huang et al., 2008) using either custom Matlab scripts (available at this reference [Turner and Foster, 2018]), the ThunderSTORM ImageJ/Fiji plugin (Ovesný et al., 2014) or Nikon elements software. All of these methods identify the locations of molecules by fitting Gaussian functions to regions of source data, and all yielded similar results.

Two colour data (where using a single multi-band filter cube) was reconstructed and aligned (registered) using Nikon elements. In summary, alignment is achieved by obtaining calibration images of the same fluorescent beads in both channels. The software then determines the way in which localisations in one channel must be offset to align with the other, based on the offsets in the apparent positions of the beads.

For two colour eYFP/Alexa Fluor 647 NHS ester imaging, using two filter cubes, the average position of a TetraSpeck fiducial was determined in both channels and a translational offset calculated for each image. This was applied to the Alexa Fluor 647 channel to approximately align the data. Whilst more sophisticated co-alignment methods exist, this was sufficient for us to draw the qualitative conclusions necessary for this part of our study.

Image rendering

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Images were rendered as 2D histograms using the ThunderSTORM ImageJ/Fiji plugin (Ovesný et al., 2014). Unless otherwise stated images were projected onto a single plane and the reconstructed pixel size was 10 nm. Semi-quantitative Matlab contour plots were used in some instances for ease of visualisation of key features in 3D reconstructions both on screen and in print. eYFP and eYFP/Alexa Fluor 647 NHS ester dual colour images were reconstructed with a pixel size of 5 nm with a Gaussian blur of 20 nm applied to make them easier to see.

Analysis of localisation microscopy data

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Ring-like groups of localisations were manually selected from fields. The centre and radius of a circle that best fit the points was then determined allowing the localisations to be represented using polar co-ordinates. Histograms of localisations with respect to angle (2˚ bin size) and distance from the centre of the circle (10 nm bin size) were then generated. The angular histograms were auto-correlated to test for the presence of similarly sized large groups of molecules which would create peaks or a very slow decay from 0˚ in the resulting graph. The distance histograms were plotted and compared with those resulting from simulations.

An additional, similar, analysis was carried modelling the septal shape as an ellipse (Figure 1—figure supplement 3).

Simulation of localisation microscopy data

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We used the simplest possible methods to simulate data to compare with that acquired on the microscope. Localisations were randomly distributed by angle on circles of a fixed radius. Localisation error comes from several physical sources, but was simulated by adding offsets in x and y taken independently and at random from a normal distribution of a defined standard deviation.

Slimfield microscopy: Microscope setup

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A bespoke single-molecule microscope was used, constructed around the body of a Zeiss inverted microscope with a 100 × 1.49 numerical aperture oil immersion total internal reflection fluorescence (TIRF) objective lens (Olympus) and an xyz nano positioning stage (Nanodrive, Mad City Labs). A 20 mW Obis 514 nm laser expanded to 10 µm full width at half maximum was used to excite meYFP fluorescence combined with a dual pass CFP/YFP dichroic mirror with 20 nm transmission windows centred on 440 nm and 514 nm. A high-speed camera (Andor iXon DV860-BI) was used to image at 5 ms/frame with magnification at 50 nm/pixel. Data were acquired using custom LabView software.

Slimfield microscopy: Sample preparation and imaging

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S. aureus SH4604 (ezrA-meyfp ΔezrA) cells were imaged by immobilising them on an agarose pad suffused with media. These were constructed by placing a gene frame (Life Technologies) on a BK7 glass microscope slide (Fisher) and filling with ~500 µl 1% (w/v) agarose containing media. Once set, 5 µl of cell culture was spotted over the agarose and covered with a plasma cleaned coverslip.

Slimfield microscopy: Image analysis

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Cell bodies and apparent EzrA rings were segmented as outlined previously (Wollman et al., 2016). In brief, the cell body was found by segmenting both a five frame average EzrA-meYFP fluorescence and brightfield image using a threshold set by the background peak in the pixel intensity distribution. The brightfield segmentation was used as seeds for watershedding the segmented fluorescence image to identify individual cells. Further thresholding within cell pixels yields a mask for the EzrA ring.

Diffraction-limited fluorescent foci were tracked using custom Matlab software as described previously (Wollman et al., 2015). In brief, in each frame, candidate foci are identified by thresholding top-hat transformed images using Otsu’s method. The spot centre is determined to sub-pixel precision using iterative Gaussian masking (Leake et al., 2006) and accepted if its signal-to-noise ratio, as defined by the foci intensity, the background-corrected integrated pixel intensity within a five pixel radius circular region of interest centred of the foci intensity centroid, divided by the standard deviation of the background pixels, is greater than 0.4. Foci are linked into the same track between image frames if they are within a distance of 1 optical resolution width (approximately five pixels), generating single particle tracks to a typical localization precision of ~40 nm (Llorente-Garcia et al., 2014).

The mean squared displacement of each track over its first four time interval points was used to calculate its microdiffusion coefficient, D, using a linear fit (Kusumi et al., 1993). These were binned into 0.01 μm2 s−1 bins and fitted with 1–3 gamma functions (Stracy et al., 2015), with three gammas generating the lowest reduced chi2.

Copy number values were calculated using a deconvolution method called CoPro (Wollman and Leake, 2015) which utilised the symmetrical geometry of S. aureus cells and the in vivo characteristic intensity of single meYFP molecules (Leake, 2014). Detection of single meYFP was confirmed by observation of single, distinct photobleach steps. This characteristic brightness value corresponding to a single meYFP molecule was determined as the peak of the intensity distribution of fluorescent foci found after 200 ms of photobleaching, and was equivalent to 2000 ± 500 counts on our EMCCD camera detector.

Transmission electron microscopy

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Samples were prepared for electron microscopy as previously described (Bottomley et al., 2014).

Cell volume calculation

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Cell volume calculations were carried out as previously described (Zhou et al., 2015), specifically, the long and short axis of cells were measured using Fiji. The volume was then calculated based on a prolate spheroid shape with volume V=43πab2, where a and b are the dimensions of the long and short axis respectively.

Gel-based analysis of SNAP tagged proteins

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SNAP-Cell TMR-Star (New England Biolabs) was added to a 1 ml aliquot of mid-exponential phase (OD600 ~1) grown culture at a concentration of 500 nM and incubated at 37°C for 1 hr. Cells were washed three times by resuspension and centrifugation in PBS, resuspended in PBS supplemented with 200 μg ml−1 lysostaphin and 20 U ml−1 DNase I and lysed at 37°C for 30 min. Cell extracts were resolved in SDS-PAGE, the gel was rinsed with dH2O and scanned using ChemiDoc MP System (Bio-Rad).

Incorporation of 14C-GlcNAc into cell wall peptidoglycan

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S. aureus strains were grown overnight in CDM and used to inoculate fresh CDM to an OD600 of 0.05 and grown to OD600 ~ 0.2. At this point 5 µM 14C-GlcNAc was added to cultures. At 30 min intervals samples were collected and prepared for analysis of 14C-GlcNAc incorporation via Liquid Scintillation as previously described (Maki et al., 2001).

Fluorescence intensity measurements

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Fluorescence intensity was measured using Image J/Fiji and calculated as counts/pixel. To determine the % off-septal fluorescence the fluorescence intensity for both the septum and the whole cell was measured and the percentage of non-septal fluorescence calculated.

Peptidoglycan purification and mass-spectrometry analysis

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S. aureus peptidoglycan was purified as previously described (Turner et al., 2010). Specifically, 1L cultures of S. aureus SH1000 and S. aureus SH1000 containing 1 mM ADA were grown for 4 hr before peptidoglycan was extracted and purified. Peptidoglycan was solubilized by digestion with 50 µg Cellosyl per mg peptidoglycan (dry weight) overnight at 37°C. Samples were boiled to inactivate the Cellosyl and reduced using sodium borohydride (Bern et al., 2017). Reduced muropeptides were separated on an Agilent Technologies Accurate Mass Q-TOF LC/MS using a Hypersil Gold aQ column (200 × 42.1 µm, 1.9 µm particle size) with a gradient of 0–30% (v/v) water/ACN both containing 0.1% (v/v) formic acid over 60 mins.

Appendix 1

General synthetic methods

H-d-aza-OH.HCl, T3P in DMF, H-d-ala-OtBu, N-succinimidyl-7-hydroxycoumarin-3-carboxylate, Boc-d-Dap-OH, 6-[tetramethylrhodamine-5(6)-carboxamido]hexanoic acid succinimidyl ester are commercially available and were used as supplied.

Synthesis of 3-azido-N-[(1,1-dimethylethoxy)carbonyl]-d-alanine (Boc-d-aza-ala-OH) (Appendix 1—figure 1)

H-d-aza-OH.HCl (527 mg, 4.1 mmol) was added to a stirred solution of sodium carbonate (1.17 g, 11.1 mmol) in water (5 mL). The solution was cooled to 0°C, followed by drop-wise addition of a solution of di-t-butyldicarbonate (1.25 g, 5.3 mmol) in acetonitrile (5 mL). The mixture was allowed to warm to room temperature overnight, diluted with water (50 mL), and washed with diethyl ether (20 mL). The aqueous layer was acidified to pH 2 using 1M HCl and extracted with ethyl acetate (3 × 50 mL). The combined organic layers were washed with brine (20 mL), dried over magnesium sulfate, filtered and evaporated to give a clear, colourless oil (850 mg, 90%) that was used directly without further purification; [∝]D25−25.0 (c 1.0, MeOH); νmax/cm−1 (film) 3331, 2981, 2935, 2551, 2108, 1714, 1513; 1H NMR (400 MHz, MeOD) δH4.33 (1H, t J 5.1 Hz, CH), 3.70–3,63 (2H, m, CH2), 1.48 [9H, s, (CH3)3] (Appendix 1—figure 2); 13C NMR (100 MHz, MeOD) δC 171.5 (CO), 156.3 (CO), 79.5 (C), 53.5 (CH), 51.8 (CH2), 27.2 (CH3) (Appendix 1—figure 3); m/z (TOF MS ES+) 365 (30%), 297 (Komis et al., 2015), 229.0949 (100, M+-H. C8H13N4O4 requires 229.0942).

Appendix 1—figure 1
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Structure of 3-Azido-N-[(1,1-dimethylethoxy)carbonyl]-D-alanine (Boc-D-aza-ala-OH).
https://doi.org/10.7554/eLife.32057.017
Appendix 1—figure 2
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1D 1H NMR Spectrum of 3-Azido-N-[(1,1-dimethylethoxy)carbonyl]-D-alanine (Boc-D-aza-ala-OH.
https://doi.org/10.7554/eLife.32057.018
Appendix 1—figure 3
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1D 13C NMR Spectrum of 3-Azido-N-[(1,1-dimethylethoxy)carbonyl]-D-alanine (Boc-D-aza-ala-OH).
https://doi.org/10.7554/eLife.32057.019

This compound has previously been prepared in the literature by different methods. NMR data have been reported in DMSO – no specific rotation was recorded (Aggen et al., 1999).

Synthesis of 3-azido-N-[(1,1-dimethylethoxy)carbonyl] -d-alanyl-d-alanine 1,1-dimethylethyl ester (Boc-d-aza-ala-d-ala-OtBu) (Appendix 1—figure 4)

Appendix 1—figure 4
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Structure of 3-Azido-N-[(1,1-dimethylethoxy)carbonyl]-D-alanyl-D-alanine 1,1-dimethylethyl ester (Boc-D-aza-ala-D-ala-OtBu).
https://doi.org/10.7554/eLife.32057.020

Boc-d-aza-ala-OH (850 mg, 3.7 mmol), N-methylmorpholine (4.5 mL, 41 mmol) and H-d-ala-OtBu (710 mg, 3.7 mmol) were added to dry DCM (100 mL) under argon. A 50 wt% solution of T3P in DMF (2.4 g, 5.0 mL, 7.5 mmol) was added slowly and the mixture was stirred at room temperature for 18 hr. The solvent was removed under vacuum, the remaining solid purified by column chromatography on silica, eluting with a 1% to 5% gradient of methanol in dichloromethane, to give a colourless oil (1.07 g, 73%); [∝]D22+5.45 (c 1.0, CHCl3); νmax/cm−1 (ATR) 3020, 2986, 2930, 2104, 1721, 1661; 1H NMR (400 MHz, CDCl3) δH6.95 (1H, br d, J 6.2 Hz, NH), 5.41 (1H, br s, NH), 4.45 (1H, quint, J 7.1 Hz, CH3CH), 4.42 (1H, br s, CH) 3.86 (1H, br dd, J 12.2 Hz and 2.6 Hz, CHH), 3.54 (1H, dd, J 12.2 Hz and 5.4 Hz, CHH), 1.48 (9H, s, 3 × CH3), 1.40 (3H, d, J 7.1 Hz, CH3) (Appendix 1—figure 5); 13C NMR (100 MHz, CDCl3) δC 173.9 (C = O), 165.9 (C = O), 52.3 (CH), 50.8 (CH2), 48.5 (CH), 16.1 (CH3) (Appendix 1—figure 6); m/z (TOF MS ES+) 358.2093 (100%, MH+. C15H28N5O5 requires 358.2090).

Appendix 1—figure 5
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1D 1H NMR Spectrum of 3-Azido-N-[(1,1-dimethylethoxy)carbonyl]-D-alanyl-D-alanine 1,1-dimethylethyl ester (Boc-D-aza-ala-D-ala-OtBu).
https://doi.org/10.7554/eLife.32057.021
Appendix 1—figure 6
Download asset Open asset
1D 13C NMR Spectrum of 3-Azido-N-[(1,1-dimethylethoxy)carbonyl]-D-alanyl-D-alanine 1,1-dimethylethyl ester (Boc-D-aza-ala-D-ala-OtBu).
https://doi.org/10.7554/eLife.32057.022

Synthesis of 3-azido-d-alanyl-d-alanine, 2,2,2-trifluoroacetate [d-aza-ala-d-ala (ADA-DA) TFA salt] (Appendix 1—figure 7)

Appendix 1—figure 7
Download asset Open asset
Structure of 3-Azido-D-alanyl-D-alanine, 2,2,2-trifluoroacetate [D-aza-ala-D-ala (ADA-DA) TFA salt].
https://doi.org/10.7554/eLife.32057.023

Boc-d-aza-ala-d-ala-OtBu (112 mg, 0.28 mmol) stirred with TFA (1 mL) in methanol (5 mL) for 5 hr at room temperature. The solvent was removed in vacuo to give a colourless oil (83 mg, 100%); [∝]D23−2.00 (c 1.0, MeOH); νmax/cm−1 (film) 3081, 2120, 1670, 1563; 1H NMR (400 MHz, MeOD) δH4.46 (1H, q, J 7.3 Hz, CH3CH), 4.07 (1H, dd, J 4.0 Hz and 7.5 Hz, CH2CH) 3.96 (1H, dd, J 4.0 Hz and 13.5 Hz, CHH), 3.77 (1H, dd, J 7.5 Hz and 13.5 Hz, CHH), 1.46 (3H, d, J 7.3 Hz, CH3) (Appendix 1—figure 8); 13C NMR (100 MHz, MeOD) δC 173.9 (CO), 165.9 (CO), 52.3 (CH), 50.8 (CH2), 48.4 (CH), 16.1 (CH3) (Appendix 1—figure 9); m/z (TOF MS ES+) 224 (10%), 202.0940 (100, MH+. C6H12N5O3 requires 202.0935), 145 (Steele et al., 2011).

Appendix 1—figure 8
Download asset Open asset
1D 1H NMR Spectrum of 3-Azido-D-alanyl-D-alanine, 2,2,2-trifluoroacetate [D-aza-ala-D-ala (ADA-DA) TFA salt].
https://doi.org/10.7554/eLife.32057.024
Appendix 1—figure 9
Download asset Open asset
1D 13C NMR Spectrum of 3-Azido-D-alanyl-D-alanine, 2,2,2-trifluoroacetate [D-aza-ala-D-ala (ADA-DA) TFA salt].
https://doi.org/10.7554/eLife.32057.025

The compound has been previously reported as a white solid, although no specific rotation or IR data were reported. 1H and 13C literature date was recorded in DMSO, but is in broad agreement (Liechti et al., 2014).

Synthesis of N-[(1,1-Dimethylethoxy)carbonyl]−3-{[(7-hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} d-alanine (N-Boc-HADA) (Appendix 1—figure 10)

Appendix 1—figure 10
Download asset Open asset
Structure of N-[(1,1-Dimethylethoxy)carbonyl]−3-{[(7-hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} D-alanine (N-Boc-HADA).
https://doi.org/10.7554/eLife.32057.026

N-Succinimidyl-7-hydroxycoumarin-3-carboxylate (250 mg, 0.82 mmol) was dissolved in triethylamine (0.26 mL) and dry CH2Cl2 (15 mL) under an inert atmosphere at room temperature. Boc-d-Dap-OH (168 mg, 0.82 mmol) was added to the mixture and stirred for 18 hr at room temperature. The mixture was diluted with ethyl acetate (50 mL) and washed with 1M HCl (2 × 10 mL). The aqueous layer was extracted with ethyl acetate (3 × 10 ml) the combined organic layers dried over magnesium sulfate, then filtered. The solvent was removed in vacuo to yield white powder (317 mg, 99%) the powder could be further purified by recrystallization from methanol (186 mg, 58%); [∝]D25−54.0 (c 1.0, MeOH); νmax/cm−1 (film) 3411, 1692, 1600, 1543; 1H NMR (400 MHz, MeOD) δH8.79 (1H, s, ArCH), 7.69 (1H, d, J 8.6 Hz, ArCH), 6.91 (1H, dd, J 2.0 Hz and 8.6 Hz, ArCH), 6.78 (1H, d, J 2.0 Hz, ArCH), 4.42–4.39 (1H, m, CH), 3.99 (1H, dd, J 13.5 Hz and 4.6 Hz, CHH), 3.60 (1H, dd, J 13.5 and 8.2 Hz, CHH), 1.44 (9H, s, CH3) (Appendix 1—figure 11); 13C NMR (100 MHz, MeOD) δC 172.8 (CO), 164.4 (CO), 163.6 (CO), 161.6 (CO), 157.0 (C), 156.5 (C), 148.5 (CH), 131.6 (CH), 114.3 (CH), 112.9 (C), 111.4 (C), 101.7 (CH), 79.3 (C), 53.7 (CH), 40.7 (CH2), 27.3 (CH3) (Appendix 1—figure 12); m/z (TOF MS ES+) 391.1166 (100%, M+-H. C18H19N2O8 requires 391.1147).

Appendix 1—figure 11
Download asset Open asset
1D 1H NMR Spectrum of N-[(1,1-Dimethylethoxy)carbonyl]−3-{[(7-hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} D-alanine (N-Boc-HADA).
https://doi.org/10.7554/eLife.32057.027
Appendix 1—figure 12
Download asset Open asset
1D 13C NMR Spectrum of N-[(1,1-Dimethylethoxy)carbonyl]−3-{[(7-hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} D-alanine (N-Boc-HADA).
https://doi.org/10.7554/eLife.32057.028

Synthesis of 3-{[(7-Hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} d-alanine, 2,2,2-trifluoroacetate (HADA TFA salt) (Appendix 1—figure 13)

Appendix 1—figure 13
Download asset Open asset
Structure of 3-{[(7-Hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} D-alanine, 2,2,2-trifluoroacetate (HADA TFA salt).
https://doi.org/10.7554/eLife.32057.029

Trifluoroacetic acid (2 mL) was added to N-Boc-HADA (96 mg, 0.24 mmol) in DCM (2 mL) and stirred at room temperature for 3 hr. The solvent was removed in vacuo to yield dark orange oil that was used without further purification (72 mg, 99%); [∝]D23−1.5 (c 1.0, MeOH); νmax/cm−1 (film) 3388, 3315, 3143, 2947, 2838, 1701, 1626, 1539, 1430; 1H NMR (400 MHz, MeOD), δH8.81 (1H, s, ArCH), 7.69 (1H, d, J 8.6 Hz, ArCH), 6.91 (1H, dd, J 8.6 Hz and 2.0 Hz, ArCH), 6.78 (1H, d, J 2.0 Hz, ArCH), 4.29 (1H, dd, J 6.4 Hz and 3.8 Hz, CH), 3.99 (1H, dd, J 3.8 Hz and 14.5 Hz, CHH), 3.6 (1H, dd, J 6.4 Hz and 14.5 Hz, CHH) (Appendix 1—figure 14); 13C NMR (400 MHz, MeOD), δC 168.5 (CO), 164.7 (CO), 164.7 (CO), 161.7 (C), 157.1 (C), 148.9 (CH), 131.7 (CH), 114.4 (CH), 112.5 (C), 111.3 (C), 101.7 (CH), 53.2 (CH), 39.1 (CH2) (Appendix 1—figure 15); m/z (TOF MS ES+) 305 (100%), 293.0777 (65, MH+. C13H13N2O6 requires 292.0774).

Appendix 1—figure 14
Download asset Open asset
1D 1H NMR Spectrum of 3-{[(7-Hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} D-alanine, 2,2,2-trifluoroacetate (HADA TFA salt).
https://doi.org/10.7554/eLife.32057.030
Appendix 1—figure 15
Download asset Open asset
1D 13C NMR Spectrum of 3-{[(7-Hydroxy-2-oxo-2H-1-benzopyran-3-yl) carbonyl] amino} D-alanine, 2,2,2-trifluoroacetate (HADA TFA salt).
https://doi.org/10.7554/eLife.32057.031

The compound has been previously reported, but all analytical data have been recorded in DMSO, so is in broad agreement (Kuru et al., 2012).

Appendix 1—table 1
Strains used in this study.
https://doi.org/10.7554/eLife.32057.032
StrainRelevant Genotype/markersSource
SH1000Functional rsbU+ derivative of 8325–4(Horsburgh et al., 2002b)
RN4220Restriction deficient transformation recipient(Kreiswirth et al., 1983)
CYL316S. aureus RN4220 pCL112Δ19 (cm)(Lee et al., 1991)
JGL227S. aureus SH1000 ezrA-gfp+ (ery)(Steele et al., 2011)
SH4386S. aureus SH1000 ezrA-eyfp (kan)This study
SH4388S. aureus SH1000 ezrA-eyfp ΔezrA (kan, tet)This study
SH4603S. aureus SH1000 ezrA-meyfp (kan)This study
SH4604S. aureus SH1000 ezrA-meyfp ΔezrA (kan, tet)This study
SH4639S. aureus SH1000 ezrA-gfp (kan)This study
SH4640S. aureus SH1000 ezrA-gfp ΔezrA (kan, tet)This study
SH4641S. aureus SH1000 ezrA-snap (kan)This study
SH4642S. aureus SH1000 ezrA-snap ΔezrA (kan, tet)This study
SH4652S. aureus SH1000 ezrA-eyfp ΔezrA pCQ11-FtsZ-SNAP (kan, tet, ery)This study
SH4665S. aureus SH1000 pCQ11-FtsZ-eYFP (ery)This study
NE679S. aureus JE2 with transposon insertion in pbp4 (ery)(Fey et al., 2013)
SH4425S. aureus SH1000 pbp4 (ery)This study
N315Methicillin-resistant S. aureus(Kuroda et al., 2001)
SU492B. subtilis SU5 Pxyl-ftsZ-yfp (spec)(Monahan et al., 2014)
Appendix 1—table 2
Plasmids used in this study
https://doi.org/10.7554/eLife.32057.033
PlasmidRelevant genotype/markersSource
pGM074pKASBAR-kan (Bottomley et al., 2014) carrying ezra-psmorange under the putative ezrA promoter (amp, kan)G. McVicker
pSNAP-tag (T7)−2E. coli expression plasmid carrying the snap gene under the control of the T7 promoter (amp)New England
Biolabs
pOBpGEM3Zf(+) cloning vector containing the erythromycin resistance cassette (amp, ery)(Horsburgh et al., 2002a)
pAISHTetR derivative of pMUTIN4(Aish, 2003)
pKASBAR-EzrA-eYFPpKASBAR-kan containing ezrA-eyfp under the putative ezrA promoter (amp, kan)This study
pKASBAR-EzrA-meYFPpKASBAR-kan containing ezrA-meyfp under the putative ezrA promoter (amp, kan)This study
pKASBAR-EzrA-GFPpKASBAR-kan containing ezrA-gfp under the putative ezrA promoter (amp, kan)This study
pKASBAR-EzrA-SNAPpKASBAR-kan containing ezrA-snap under the putative ezrA promoter (amp, kan)This study
pOB-ΔezrApOB containing the ezrA deletion cassette consisting of a 1.5 kb fragment of the upstream region of S. aureus ezrA, the tetracycline resistance cassette from pAISH and a 1.5 kb fragment of the downstream region of S. aureus ezrA (amp, ery, tet)This study
pSS26bpUC19 encoding snap (amp)Covalys
pSS26bFtsZ-CpSS26b containing ftsZ-snap (amp)This study
pCQ11E. coli-S. aureus shuttle vector containing lacI, Pspac and gfp (amp, ery)(Hardt et al., 2017)
pCQ11-FtsZ-SNAPpCQ11 derivative containing ftsZ-snap under Pspac (amp, ery)This study
pCQ11-FtsZ-eYFPpCQ11-FtsZ-SNAP with eyfp replacement of snap (amp, ery)This study
Appendix 1—table 3
Oligonucleotides used in this study.
https://doi.org/10.7554/eLife.32057.034
Oligonucleotide nameSequence (5’ to 3’)
eYFP-FCGGCGCGCCTCAGGTTCAGGTTCAGGTATGGTGAGCAAGGGCGAG
eYFP-RCGCGGCCGCTTACTTGTACAGCTCGTCCATGCCGAGAGTGATCCCGGC
GFP-FCGGCGCGCCTCAGGTTCAGGTTCAGGTATGGCTAGCAAAGGAGAAGAA
CTTTTCACTGGAGTTGTCCC
GFP-RCGCGGCCGCTTATTTGTAGAGCTCATCCATGCCATGTGTAATCCCAGCAGC
SNAP-FGGGCGCGCCTCAGGTTCAGGTTCAGGTATGGACAAAGACTGCGAAATG
AAGCGCAC
SNAP-RCGAATTCTCATTAACCCAGCCCAGGCTTGCCCAGTCTG
meYFP-FCTACCAGTCCAAGCTGAGCAAAGAC
meYFP-RCTCAGGTAGTGGTTGTCG
pOB-ezrA-up-FTTTACGTACACTATCTGCAGATGCTTCTCCTCCTAATTTATCATT
pOB-ezrA-up-RATTCGAGCTCGGTACCCGGGTTTTAAATTAATAAAAAAAACAC
CCACAATT
pOB-ezrA-down-FCACTATAGAATACTCAAGCTTACTCCTTAATTTCCTCATAAATGATGA
pOB-ezrA-down-RGGATCAACTTTGGGAGAGAGAAACTAGTATGTAGTTATACTTAAA
TAATATGAGC
pOB-TetR-FTAAATTAGGAGGAGAAGCATCTGCAGATAGTGTACGTAAAAAGA
pOB-TetR-RGTATAACTACATACTAGTTTCTCTCTCCCAAAGTTGATCCC
ftsZ-eyfp-FACATGGCCATGTCAGGTTCAG
ftsZ-eyfp-RGGCGCGCCTTATTTATATAATTC
FGFtsZXhoI-FCTCGAGATGTTAGAATTTGAACAAGG
FGFtsZEcoRI-RTTAGAATTCACGTCTTGTTCTTCTTGAA
FGFtsZNheI-FGTTGCTAGCATGTTAGAATTTGAACAAGG
FGFtsZAscI-RGTTGGCGCGCCTTATCCCAGACCCGGTTTAC

References

    1. Aish JL
    (2003)
    Environmental regulation of virulence determinant expression in Staphylococcus aureus: University of Sheffield
    Environmental regulation of virulence determinant expression in Staphylococcus aureus: University of Sheffield.
    1. Giesbrecht P
    2. Kersten T
    3. Maidhof H
    4. Wecke J
    (1998)
    Staphylococcal cell wall: morphogenesis and fatal variations in the presence of penicillin
    Microbiology and Molecular Biology Reviews : MMBR 62:1371–1414.
  53. Book
    1. Sambrook J
    2. Russell DW
    (2001)
    Molecular Cloning: A Laboratory Manual
    CSHL Press.

Decision letter

  1. Jie Xiao
    Reviewing Editor; Johns Hopkins University, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Molecular coordination of Staphylococcus aureus cell division" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Gisela Storz as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to frame the key concerns we feel must be addressed before a binding decision may be made on your submission.

Overall, the referees are in agreement that the work potentially represents new insight into cell wall synthesis and division in S. aureus. However, both reviewers raised serious concerns regarding the experimental methods, quantification, and interpretations, and consequently the major claims of the work. There were also concerns regarding the necessity of some experiments. Before a decision can be made regarding the publication, we ask you to submit a revised application addressing the following comments:

Major comments:

1) Demonstrate the ability of ADA probes to label new septal cell wall synthesis.

The authors are assuming that FDAA incorporation equals PG synthesis, when it is well known that this is not the case, most especially for the ADAs, which are incorporated into the cell wall by transpeptidase reactions. The authors would be correct in stating that crosslinking occurs everywhere in the cell and throughout the septa, but this is not synthesis. Importantly it has been published by two different groups that PBP4 (a LMW transpeptidase) is responsible for the extra septal FDAA staining signal (Monteiro, 2015 doi:10.1038/ncomms9055), (Gautam, 2015 DOI: 10.1021/jacs.5b02972). These two papers reveal that a large amount of FDAA incorporation occurs via PBP4, but this is due to crosslinking of FDAA's into the existing material and does not reflect the insertion of new material.

Thus, if the authors want to claim that A) PG synthesis occurs throughout the septa, and B) there are no hotspots (or puncta) of PG synthesis in the ring, they need to Redo their FDAA labeling and STORM in PBP4 null cells at different pulse lengths, so that they can (as much as possible) remove this outside "noisy" signal, and truly focus on where synthesis occurs.

To prove this further, they should also conduct various ranges of pulses of ADA-DA (e.g., 5s, 15s, 30s, 1 min) as these are believed to be reflective of synthesis, and not simply crosslinking. These should be done in PBP4 null cells as well.

Finally, the authors went to great detail to make the HADA probe and then do not use it for any significant experiment. They also showed no evidence of incorporation of this probe into the PG (Figure 3—figure supplement 1 only has data for the ADA probe). The ADA-DA probe could be used throughout the manuscript – thereby allowing results to be accurately compared without having to worry about artifacts of the PG-probe. The HADA probe was not used in any significant experiment and could be removed without altering the conclusions of the manuscript.

2) Provide quantitative measurements specifying the localization precision and spatial resolution of PALM/STORM images.

The authors must determine (or at least estimate their precision of localization) in both XY and Z, for eYFP and Alexa 647. Many of the arguments made in figures 1 and 3 do indeed hinge on their precision of localization. They dismiss the need for this, stating – "It is challenging to determine resolution in localisation microscopy images. However, it is more important to determine which image features observed are representative of biological structures, then to determine absolute resolution." This reviewer adamantly disagrees with this statement, as the conclusions drawn from the widths of their distributions in Figures 1 and 3 rely on their precision of localization, and thus far it is not clear if they have achieved adequate precision to make claims about the widths of these distributions.

To overcome this issue, the authors have attempted to fit their data in Figure 1 with different precisions (up to 60 nm). However, several later statements made in the paper suggest that their precision may indeed not even be that accurate: First, they state their prevision for localization of 647 is <70nm. This is a troubling measure for multiple reasons, as eYFP is significantly dimmer than 647. This makes this reviewer fear they may not have enough photons even to approach 70nm, close to the fitting they used in Figure 1 to justify these measures. Second, while it appears they used flat septa in Figure 1 (in the XY plane), in later figures they used 3D-Storm, which then raises the issue that there is a much less precision in Z relative to XY. This is not raised nor acknowledged in the paper, and how it affects the data is not clear. For example, in Figures 1 and 3, what angle were these septa in that they analyzed? (This complicates their data). Were they flat in XY, which would give the best resolution or were they angled up in Z, or tilted in some combination of XYZ? This is a key issue, as the spread, noise, and asymmetry in these distributions may arise from their precisions of each different septa, and where it is in the Z plane.

So, overall, the authors must report their localization precision in XY, and Z, for both eYFP as well as 647. This is best done (amount other ways) with pure protein immobilized on coverslips, as well known. This should allow them to accurately determine the height of the peak of a single molecule and FWHM of each localization. On an NSTORM system, the precision in Z can also be easily determined, as these systems have piezo stages.

Alternatively, at the very least the authors should at minimally report the computed (localization) precision from frame by frame peak heights in their data, as detailed in doi:10.1038/nmeth.1447. While this does not account for other sources of noise (such as instrument vibrations), it will at least give an estimate. If their analyses are conducted on complexity flat rings in the XY plane, knowing the precision can help the authors gauge the accuracy of their claims. However, if they are drawing on data that involves rings that traverse the Z direction, they should do determine, or at least give a rough calculation of the loss of precision caused by the Z-direction, and use that "worse" precision estimate to determine if they can make the claims in the paper.

3) Re-examine the claim that FtsZ and EzrA are not localized in a thin ring around the division plane, but that these proteins are in much wider bands.

There are a few problems with their analysis. First, the method by which they analyzed these distributions appears to be erroneous. They stated that they looked at the localizations at distances from centers of the circles. While this would make sense if the septa were indeed perfect circles (as shown in their simulated data), but examining the examples they show it appears most of many of these septa are not perfect circles, rather they are extended ellipses. Thus, measuring the distance from the center is not a valid measure of thickness (or distance to the edge), and thus this assumption is likely distorting their conclusions. If they want to back up this claim, it would be better they fit an ellipse to the density each septal ring, and then plotted the distribution of localizations around that line around the septa.

Second, as described above, to make the claim that FtsZ and EzrA are not in a thin "ring" they must determine their localization precision (discussed more in 2 above). Currently, they attempt to get around this by estimating the spread with different localization precisions. This is not adequate, as they have not even demonstrated they have 60nm precision (the largest value they use).

Other concerns:

1) In Figure 4—figure supplement 1, septal peptidoglycan synthesis actually looks well-localized to my eyes for 15-20 minutes and even to some extent after 30-35 minutes of PC190723 treatment, well beyond the time when FtsZ and EzrA have delocalized. Do the authors have an explanation for why this might be? It seems contrary to their claim in the manuscript that gross PG synthesis follows FtsZ and EzrA localization, although this is certainly true at later time points.

2) The Slimfield analysis, while nice, adds nothing to this paper or story. All this gives them is the diffusion coefficient of EzrA. First, how is this measure pertinent to the story? That is not made clear. Second, there also appears to be an immobile fraction, but the authors report the characteristics of the diffusive form. Is this the biologically relevant form? If EzrA is treadmilling with FtsZ, it should be immobile, and the diffusive molecules not associated with FtsZ.

Furthermore, the authors should rethink their statement about EzrA moving around the cell with FtsZ – that would not be expected for molecules associate with treadmilling filaments.

3) Figure 3—figure supplement 2B and C should present the same brightness and contrast range for comparison.

4) In the last paragraph of the Discussion, it is difficult to follow how the data presented support or do not support a model of "machines moving through the cell depositing peptidoglycan." The article argues persuasively that septal PG synthesis is not confined to the inner edge of the invaginating septum in Staph, but doesn't include much detail about the molecular mechanism of insertion (which is fine, and not necessary for this article). Given that FtsZ and EzrA are colocalized with PG incorporation in this system as well, it is a question of how the authors rule out a model where these hypothetical molecular machines do exist, but are distributed across the entire septum rather than localized in one region- such a system seems consistent with the results presented here. It is also confusing that these proteins are not always physically adjacent- it seems like most of the article has been arguing the contrary, that a wider FtsZ/EzrA localization band gives rise to more broadly distributed septal PG insertion.

5) The use of the Alexa Fluor 647 NHS ester should be clarified. This reagent non-specifically labels proteins in the cell wall.

6) As unnatural amino acids are used, could the authors comment on how they think these additions could change the structure of the cell wall? Could the probes possibly effect the placement of the machines?

7) Could the authors explain what "gross peptidoglycan synthesis" is? How does this play into their model?

8) The treatment with labeled vancomycin is not standard – usually unlabeled vancomycin is added with the labeled vancomycin. It is not certain if vancomycin is capable of labeling a D-Ala-D-Ala terminus that has been labeled with a fluorophore – this is very close to the binding pocket. That could be a reason why they do not see labeling there? What do these experiments add to their conclusions?

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Molecular coordination of Staphylococcus aureus cell division" for further consideration at eLife. Your revised article has been favorably evaluated by Gisela Storz (Senior Editor) and a Reviewing Editor.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

1) Calculation of the spatial resolution of EzrA and FtsZ imaging:

Localization precision only tells how accurately one can determine the position of one localization. It is usually much better than the actual spatial resolution one can achieve, which is compounded by the Nyquist resolution (calculated by labeling density), and experimental resolution (calculated by the spread of repeat localization). Of these two, the experimental resolution often is the limiting factor, and should be reported. The authors cited reasons for not doing the latter calculation coltharp, but this measurement can be done by using the fixed bacteria instead of purified, in-vitro samples. See Endesfelder et al., 2014, Churchman LS et al., 2006, Biophysical J, 90(2):668-671, and Coltharp et al., 2012.

2) PBP4 null experiment:

There was one image (Figure 3—figure supplement 3) qualitatively suggesting that 15s ADA-DA and ADA incorporation in the absence of PBP4 is homogeneous. Please provide enough statistics to support this conclusion, i.e., number of cells, autocorrelation function and labeling density.

https://doi.org/10.7554/eLife.32057.040

Author response

Overall, the referees are in agreement that the work potentially represents new insight into cell wall synthesis and division in S. aureus. However, both reviewers raised serious concerns regarding the experimental methods, quantification, and interpretations, and consequently the major claims of the work. There were also concerns regarding the necessity of some experiments. Before a decision can be made regarding the publication, we ask you to submit a revised application addressing the following comments:

Major comments:

1) Demonstrate the ability of ADA probes to label new septal cell wall synthesis.

The authors are assuming that FDAA incorporation equals PG synthesis, when it is well known that this is not the case, most especially for the ADAs, which are incorporated into the cell wall by transpeptidase reactions. The authors would be correct in stating that crosslinking occurs everywhere in the cell and throughout the septa, but this is not synthesis. Importantly it has been published by two different groups that PBP4 (a LMW transpeptidase) is responsible for the extra septal FDAA staining signal (Monteiro, 2015 doi:10.1038/ncomms9055), (Gautam, 2015 DOI: 10.1021/jacs.5b02972). These two papers reveal that a large amount of FDAA incorporation occurs via PBP4, but this is due to crosslinking of FDAA's into the existing material and does not reflect the insertion of new material.

Thus, if the authors want to claim that A) PG synthesis occurs throughout the septa, and B) there are no hotspots (or puncta) of PG synthesis in the ring, they need to Redo their FDAA labeling and STORM in PBP4 null cells at different pulse lengths, so that they can (as much as possible) remove this outside "noisy" signal, and truly focus on where synthesis occurs.

To prove this further, they should also conduct various ranges of pulses of ADA-DA (e.g., 5s, 15s, 30s, 1 min) as these are believed to be reflective of synthesis, and not simply crosslinking. These should be done in PBP4 null cells as well.

Finally, the authors went to great detail to make the HADA probe and then do not use it for any significant experiment. They also showed no evidence of incorporation of this probe into the PG (Figure 3—figure supplement 1 only has data for the ADA probe). The ADA-DA probe could be used throughout the manuscript – thereby allowing results to be accurately compared without having to worry about artifacts of the PG-probe. The HADA probe was not used in any significant experiment and could be removed without altering the conclusions of the manuscript.

We agree that PBP4 activity could have an effect on the observed FDAA incorporation patterns. Thus, a pbp4 mutant was used. Strain SH4425 (SH1000 pbp4) has no growth defect compared to its parent and shows identical, overall peptidoglycan synthesis as measured by 14C GlcNAc incorporation (Figure 3—figure supplement 3). Using both HADA and ADA-DA labelling for 5 min, SH4425 shows decreased incorporation of either probe compared to SH1000. However, this reduction is across the entire cell and not as a result of loss of off septal labelling per se. This is contrary to published data (1) that can be explained by low level labelling in the previous study. To further map synthesis in SH4425 (pbp4), STORM was carried out using cells labelled with ADA and ADA-DA for 5min and 15s (Figure 3—figure supplement 3). This clearly demonstrates that there is labelling across the septum, around the cell periphery and not limited to apparent foci of insertion. This is corroborated by STORM analysis of SH1000 labelled for 15s with ADA-DA (Figure 3—figure supplement 2).

The HADA probe has been used extensively in previous FDAA studies (2, 3) and now here in the further analysis of SH4425 (pbp4) (Figure 3—figure supplement 3).

2) Provide quantitative measurements specifying the localization precision and spatial resolution of PALM/STORM images.

The authors must determine (or at least estimate their precision of localization) in both XY and Z, for eYFP and Alexa 647. Many of the arguments made in figures 1 and 3 do indeed hinge on their precision of localization. They dismiss the need for this, stating – "It is challenging to determine resolution in localisation microscopy images. However, it is more important to determine which image features observed are representative of biological structures, then to determine absolute resolution." This reviewer adamantly disagrees with this statement, as the conclusions drawn from the widths of their distributions in Figures 1 and 3 rely on their precision of localization, and thus far it is not clear if they have achieved adequate precision to make claims about the widths of these distributions.

To overcome this issue, the authors have attempted to fit their data in Figure 1 with different precisions (up to 60 nm). However, several later statements made in the paper suggest that their precision may indeed not even be that accurate: First, they state their prevision for localization of 647 is <70nm. This is a troubling measure for multiple reasons, as eYFP is significantly dimmer than 647. This makes this reviewer fear they may not have enough photons even to approach 70nm, close to the fitting they used in Figure 1 to justify these measures. Second, while it appears they used flat septa in Figure 1 (in the XY plane), in later figures they used 3D-Storm, which then raises the issue that there is a much less precision in Z relative to XY. This is not raised nor acknowledged in the paper, and how it affects the data is not clear. For example, in Figures 1 and 3, what angle were these septa in that they analyzed? (This complicates their data). Were they flat in XY, which would give the best resolution or were they angled up in Z, or tilted in some combination of XYZ? This is a key issue, as the spread, noise, and asymmetry in these distributions may arise from their precisions of each different septa, and where it is in the Z plane.

So, overall, the authors must report their localization precision in XY, and Z, for both eYFP as well as 647. This is best done (amount other ways) with pure protein immobilized on coverslips, as well known. This should allow them to accurately determine the height of the peak of a single molecule and FWHM of each localization. On an NSTORM system, the precision in Z can also be easily determined, as these systems have piezo stages.

Alternatively, at the very least the authors should at minimally report the computed (localization) precision from frame by frame peak heights in their data, as detailed in doi:10.1038/nmeth.1447. While this does not account for other sources of noise (such as instrument vibrations), it will at least give an estimate.

Computed localisation precisions can be obtained using the widely adopted “Thompson” Equation (4) which is the basis of localisation precision determination in both the N-STORM software (used for our 3D imaging) and in ThunderSTORM (used for our 2D imaging). We reproduce this here for reference:

⟨(∆x)2⟩=s2+a2/12N+8πs4b2a2N2

This equation has attracted criticism for potentially underestimating localisation precision and a modified version of the equation is presented in the paper referenced by the reviewers (5).

We can use these to summarise calculated localisation precision for example datasets. We fully acknowledge that this information would have been very useful to those reviewing the manuscript and have included it in our amended version (subsection “Distribution of divisome components during septation”, fourth paragraph and subsection “Peptidoglycan synthesis in S. aureus does not occur in discrete foci”, second paragraph). Determining localisation precision by repeatedly imaging immobilised fluorophores would probably lead to an underestimate of localisation precision as background will be higher in our bacteria than on a clean surface (where there would also be near field coupling to the glass, boosting the detection efficiency) – we have therefore not done this.

FluorophoreLocalisation Precision (nm)
(Quoted as a standard deviation)		Method
Alexa Fluor 647
(3D with cylindrical lens)		9.8 (s.d. 3.5)N-STORM Software (Thompson)
YFP24 (s.d. 8.5)ThunderSTORM
(Thompson)
27 (s.d. 8.7)Matlab Function
(Mortensen’s modified Thompson)

Substantial variability over individual blinks is to be expected (due to variability in photon counts/blink). Although experience tells us that YFP is dimmer than Alexa Fluor 647 for confocal and epifluorescence imaging, we have found than an improved buffer results in an acceptable number of photons per blink for single molecule imaging (as have others (6)). We used D2O to make the buffer to boost photon counts (7). Alexa Fluor 647 still performed much better than YFP.

The critical point here is that the features we see in our images, on which we base our conclusions, are on a much larger scale than our localisation precision. Our approach of comparing simulations and microscope data, and exploring pessimistic scenarios regarding performance, was rooted in concerns that good localisation precision alone does not always equate to reliable data.

If their analyses are conducted on complexity flat rings in the XY plane, knowing the precision can help the authors gauge the accuracy of their claims. However, if they are drawing on data that involves rings that traverse the Z direction, they should do determine, or at least give a rough calculation of the loss of precision caused by the Z-direction, and use that "worse" precision estimate to determine if they can make the claims in the paper.

We have adapted our methods to use ellipse fitting as an alternative to circle fitting (see our response to point 3 below, subsection “Distribution of divisome components during septation”, fourth paragraph and Figure 1—figure supplement 3). If the elliptical shape of the rings is the result of the effective projection of a circular ring onto the imaging plane, we can calculate how much the circle would have to be tilted and thus the range of distances perpendicular to the imaging plane in which we would expect to find molecules.

Author response image 1
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In localisation microscopy, blinks a long way from the “absolute” plane of focus are more poorly localised than those “in focus”. A justified concern is that this would mean that for some molecules localisation precision would be very poor, and falsely lead us to our conclusion that the molecules are not in a very thin ring. However, the spread of localisations in z is within the range for which we expect our objective to deliver good data (see (8) for a typical z range). Furthermore, we have now provided calculated localisation precisions which take into account potential reduced precision (due to fewer photons or a broader Gaussian). We demonstrate in our response to comment 3, below, that our localisation precision is good enough to support our conclusions.

3) Re-examine the claim that FtsZ and Ezra are not localized in a thin ring around the division plane, but that these proteins are in much wider bands.

There are a few problems with their analysis. First, the method by which they analyzed these distributions appears to be erroneous. They stated that they looked at the localizations at distances from centers of the circles. While this would make sense if the septa were indeed perfect circles (as shown in their simulated data), but examining the examples they show it appears most of many of these septa are not perfect circles, rather they are extended ellipses. Thus, measuring the distance from the center is not a valid measure of thickness (or distance to the edge), and thus this assumption is likely distorting their conclusions. If they want to back up this claim, it would be better they fit an ellipse to the density each septal ring, and then plotted the distribution of localizations around that line around the septa.

Carrying out this analysis based on an ellipse rather than a circle complicates it as the circle fit was a primarily means of shifting to a polar co-ordinate system, not to analyse the distribution of molecules relative to a circle of a specific radius – the analysis is independent of the fitted radius. However, we have adapted the analysis method to a fitted ellipse to test whether the approach proposed by the reviewers is advantageous or impacts on our conclusions.

We used an elliptical ring divided into blocks to inform us of the way in which the number of localisations changes as we go around the ring. In the illustration below we have used different shades of blue to show which blocks contain more or less molecules. Yellow localisations are included in the analysis, whilst grey ones are not.

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Second, as described above, to make the claim that FtsZ and Ezra are not in a thin "ring" they must determine their localization precision (discussed more in 2 above).

Please see our response to point 2 regarding localisation precision.

Currently, they attempt to get around this by estimating the spread with different localization precisions. This is not adequate, as they have not even demonstrated they have 60nm precision (the largest value they use).

We determined how close each molecule was to the closest point on the fitted ellipse and compared this distribution of distances to a simulation (applying localisation precision from a normal distribution with a mean of 27 nm and a standard deviation of 8.7 nm). We again concluded that the variation in molecule position cannot be accounted for by localisation error alone, and that the molecules were not in a very thin ring, even if this ring is an ellipse (Figure 1—figure supplement 3E).

Other concerns:

1) In Figure 4—figure supplement 1, septal peptidoglycan synthesis actually looks well-localized to my eyes for 15-20 minutes and even to some extent after 30-35 minutes of PC190723 treatment, well beyond the time when FtsZ and EzrA have delocalized. Do the authors have an explanation for why this might be? It seems contrary to their claim in the manuscript that gross PG synthesis follows FtsZ and EzrA localization, although this is certainly true at later time points.

We agree that the image of HADA cells at 15-20 minutes looks more similar to that at 0-5 minutes than do the images of FtsZ-SNAP or EzrA-YFP. However, there are at 15-20 minutes some cells within the field shown that have clear defects in peptidoglycan insertion pattern, showing asymmetric or punctate insertion. Figure 4—figure supplement has been amended to highlight these features. In all cases the images for the three channels are similar enough to support the conclusion that EzrA, FtsZ and peptidoglycan insertion are all in the same places.

2) The Slimfield analysis, while nice, adds nothing to this paper or story. All this gives them is the diffusion coefficient of EzrA. First, how is this measure pertinent to the story? That is not made clear. Second, there also appears to be an immobile fraction, but the authors report the characteristics of the diffusive form. Is this the biologically relevant form? If EzrA is treadmilling with FtsZ, it should be immobile, and the diffusive molecules not associated with FtsZ.

Furthermore, they authors should rethink their statement about Ezra moving around the cell with FtsZ – that would not be expected for molecules associate with treadmilling filaments.

The Slimfield microscopy and associated analysis enable precise quantitation of molecular mobility over a rapid millisecond time scale comparable to molecular diffusion rates inside live cells i.e. it allows us to quantify mobile and immobile populations of EzrA molecules directly inside live cells. However, it also enables direct quantitation of the EzrA copy number on a cell-by-cell basis – this is achieved through a process of determining the characteristic brightness of single YFP molecules inside each cell from Slimfield images and then deconvolving these images directly. Slimfield gives us an unprecedented insight into precisely how much EzrA is present in live cells and how its molecular mobility changes depending on whether or not it is incorporated into a septum structure; this level of robust quantification indicates a more complex behavior than simple treadmilling alone, and we believe it has value in supporting a more heterogeneous model for the cell growth and division processes than has been suggested previously, when viewed in the context of the other data presented in our paper.

Specifically, the full distribution of measured microscopic diffusion coefficients (D) for all tracked EzrA-YFP foci are given in the plots of Figure 1—figure supplement 4; these indicate a mixture of both a relatively immobile and two relatively mobile populations. The characteristic D values reported in the main text are overall mean values which are calculated over the full range of measured D, either inside or outside the septum, and so include contributions from both the immobile and two mobile components. We have now reworded this section to clarify this specifically to avoid confusion (subsection “Distribution of divisome components during septation”, last paragraph). The text in this section has also been amended to indicate clearly that copy number was determined directly by Slimfield.

Our measured tracking localization precision of a few tens of nm (9) indicates that rates of diffusion values below ~0.02 µm2/s will appear predominantly in the apparent immobile component of the distribution of D values, even if they have a real underlying mobility. The mean speed of putative FtsZ treadmilling estimated from B. subtilis (3) is only ~30nm/s, which we estimate would be sufficiently slow to appear predominantly in the immobile component over the typical time scales of our Slimfield tracking experiments here, and so we agree with the reviewer that any putative treadmilling of EzrA at this equivalent mean speed, if present in S. aureus, would appear in this apparent immobile fraction. Conversely, however, this measurement for the proportion of the apparent immobile fraction sets an upper bound for the proportion of EzrA which may be treadmilling in association with FtsZ. EzrA present in the most mobile fraction is unlikely to be associated with FstZ, we agree with the reviewer. However, in the 3-γ function mobility model, which fits the observed distribution of D values well, the intermediate mobility fraction has been interpreted previously in other cellular systems as indicating transient dynamic interactions(10), so we cannot entirely exclude the possibility that this may be due to transient association of EzrA with FtsZ i.e. that it is also the ‘biologically relevant form’ as that present in the apparent immobile fraction. However, we can then derive an important result that at least ~1/3 of EzrA is not treadmilling with FtsZ. In other words, we cannot account for the observed mobility of EzrA by a simple treadmilling model alone in which all EzrA is tightly associated to FtsZ, but rather the real cellular behavior is more nuanced than this. We have now amended the text to include discussion of this (see the aforementioned subsection).

3) Figure 3—figure supplement 2B and C should present the same brightness and contrast range for comparison.

Different FDAAs have different brightness in the microscope, therefore using numerically the same brightness and contrast settings will not be optimal for all fluorophores. Images were re-processed to be optimal for individual channels where the background was not black and the highest pixel values were not saturated, therefore no details will be hidden. Since STORM data is a plot of localisations rather than an image the same contrast cannot be applied. We hope that the revised figure will make it easier for readers to compare the images (Figure 3—figure supplement 2).

4) In the last paragraph of the Discussion, it is difficult to follow how the data presented support or do not support a model of "machines moving through the cell depositing peptidoglycan." The article argues persuasively that septal PG synthesis is not confined to the inner edge of the invaginating septum in Staph, but doesn't include much detail about the molecular mechanism of insertion (which is fine, and not necessary for this article). Given that FtsZ and EzrA are colocalized with PG incorporation in this system as well, it is a question of how the authors rule out a model where these hypothetical molecular machines do exist, but are distributed across the entire septum rather than localized in one region- such a system seems consistent with the results presented here. It is also confusing that these proteins are not always physically adjacent- it seems like most of the article has been arguing the contrary, that a wider FtsZ/EzrA localization band gives rise to more broadly distributed septal PG insertion.

We have revised the last paragraph of the Discussion to incorporate the reviewers points so as not to over-interpret our data. We agree that our data does not rule our molecular machines, but that it provokes questions as to the nature, stability and dynamism of these conceptual groupings of proteins.

5) The use of the Alexa Fluor 647 NHS ester should be clarified. This reagent non-specifically labels proteins in the cell wall.

The NHS ester labels amine groups which are present in both peptidoglycan and proteins. The reagent was used to indicate the location of the entirety of the cell wall relative to other cell components – we make no claim that it is a peptidoglycan specific label. This has been clarified in the text (subsection “Distribution of divisome components during septation”, seventh paragraph).

6) As unnatural amino acids are used, could the authors comment on how they think these additions could change the structure of the cell wall? Could the probes possibly effect the placement of the machines?

We have combined unnatural amino acid labelling with imaging of EzrA-YFP and FtsZ-YFP using diffraction limited microscopy and see no difference in the distribution of the proteins. The reviewer is right to point out that localised alterations to peptidoglycan chemistry may affect the localisation of proteins involved with growth and division, however if this occurs, it does so at scales we cannot currently access. We assume the number of peptides altered by our amino acid (or dipeptide) labelling is a very small proportion of the total.

Author response image 3
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Effect of HADA labelling on EzrA-eYFP and FtsZ-SNAP TMR-Star localisation.

S. aureus strain producing EzrA-eYFP grown without (a) and with HADA for 5 min (b). SNAP-Cell TMR-Starlabelled S. aureus strain producing FtsZ-SNAP grow without (c) and with HADA for 5 min (d). Images are average intensity fluorescence projections of z stacks. Scale bars 5 𝝻m.

https://doi.org/10.7554/eLife.32057.038

7) Could the authors explain what "gross peptidoglycan synthesis" is? How does this play into their model?

This was not a great choice of words – we have amended the text for clarity (throughout).

8) The treatment with labeled vancomycin is not standard – usually unlabeled vancomycin is added with the labeled vancomycin.

This is only the case if fluorescent vancomycin is purchased ready labelled. For “home-made” fluorescent vancomycin, no unlabelled drug needs to be added (11).

It is not certain if vancomycin is capable of labeling a D-Ala-D-Ala terminus that has been labeled with a fluorophore – this is very close to the binding pocket. That could be a reason why they do not see labeling there?

It is likely that vancomycin cannot bind side chains that are not D-ala-D-ala terminated – incorporation of D-serine (for example) instead of D-alanine in the positions blocks vancomycin binding. However, many D-ala-D-ala terminated peptides remain in ADA labelled bacteria, resulting in a strong vancomycin fluorescence signal over the entire cell surface.

What do these experiments add to their conclusions?

Combining vancomycin and ADA labelling reveals regions where there is vancomycin binding, but no ADA incorporation. This confirms that the regions within partially formed septa that are not labelled with ADA, are in fact filled with peptidoglycan, to which the vancomycin can bind (subsection “Peptidoglycan synthesis in S. aureus does not occur in discrete foci”, third paragraph).

References

1) Monteiro JM, Fernandes PB, Vaz F, Pereira AR, Tavares AC, Ferreira MT, et al. Cell shape dynamics during the staphylococcal cell cycle. Nature Communications. 2015;6:8055.

2) Kuru E, Hughes HV, Brown PJ, Hall E, Tekkam S, Cava F, et al. In Situ probing of newly synthesized peptidoglycan in live bacteria with fluorescent ᴅ-amino acids. Angewandte Chemie Int Ed. 2012;51(50):12519-23.

3) Bisson Filho AW, Hsu YP, Squyres GR, Kuru E, Wu F, Jukes C, et al. Treadmilling by FtsZ filaments drives peptidoglycan synthesis and bacterial cell division. Science. 2017;355:739-43.

4) Thompson RE, Larson DR, Webb WW. Precise Nanometer Localization Analysis for Individual Fluorescent Probes. Biophysical Journal. 2002;82(5):2775-83.

5) Mortensen KI, Churchman LS, Spudich JA, Flyvbjerg H. Optimized localization analysis for single-molecule tracking and super-resolution microscopy. Nature Methods. 2010;7:377.

6) Jusuk I, Vietz C, Raab M, Dammeyer T, Tinnefeld P. Super-Resolution Imaging Conditions for enhanced Yellow Fluorescent Protein (eYFP) Demonstrated on DNA Origami Nanorulers. Scientific Reports. 2015;5:14075.

7) Ong WQ, Citron YR, Schnitzbauer J, Kamiyama D, Huang B. Heavy water: a simple solution to increasing the brightness of fluorescent proteins in super-resolution imaging. Chemical Communications. 2015;51(70):13451-3.

8) Palayret M, Armes H, Basu S, Watson AT, Herbert A, Lando D, et al. Virtual-'Light-Sheet' Single-Molecule Localisation Microscopy Enables Quantitative Optical Sectioning for Super-Resolution Imaging. PLOS ONE. 2015;10(4):e0125438.

9) Wollman AJM, Leake MC. Millisecond single-molecule localization microscopy combined with convolution analysis and automated image segmentation to determine protein concentrations in complexly structured, functional cells, one cell at a time. Faraday Discussions. 2015;184(0):401-24.

10) Stracy M, Lesterlin C, Garza de Leon F, Uphoff S, Zawadzki P, Kapanidis AN. Live-cell superresolution microscopy reveals the organization of RNA polymerase in the bacterial nucleoid. Proceedings of the National Academy of Sciences. 2015;112(32):E4390-E9.

11) Daniel RA, Errington J. Control of Cell Morphogenesis in Bacteria: Two Distinct Ways to Make a Rod-Shaped Cell. Cell. 2003;113:767-76.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

1) Calculation of the spatial resolution of EzrA and FtsZ imaging:

Localization precision only tells how accurately one can determine the position of one localization. It is usually much better than the actual spatial resolution one can achieve, which is compounded by the Nyquist resolution (calculated by labeling density), and experimental resolution (calculated by the spread of repeat localization). Of these two, the experimental resolution often is the limiting factor, and should be reported. The authors cited reasons for not doing the latter calculation coltharp, but this measurement can be done by using the fixed bacteria instead of purified, in-vitro samples. See Endesfelder et al., 2014, Churchman LS et al., 2006, Biophysical J, 90(2):668-671, and Coltharp et al., 2012.

We fully acknowledge the importance of understanding localisation precision and resolution in this type of study. We have used the NeNA method (executed in the Lama software package) to extract mean values from our experimental data on fixed cells. These are 7.5 nm for Alexa Fluor 647 and 16.23 nm for YFP, in both cases smaller than the “theoretical precision”. (subsection “Distribution of divisome components during septation”, fourth paragraph and subsection “Peptidoglycan synthesis in S. aureus does not occur in discrete foci”, second paragraph).

We are cautious about localisation precision estimates and would like to re-emphasise that our analysis methods take localisation precision into account, and we have demonstrated that our conclusions would hold even if we were to have vastly underestimated localisation precision.

2) PBP4 null experiment:

There was one image (Figure 3—figure supplement 3) qualitatively suggesting that 15s ADA-DA and ADA incorporation in the absence of PBP4 is homogeneous. Please provide enough statistics to support this conclusion, i.e., number of cells, autocorrelation function and labeling density.

We have applied the elliptical analysis method suggested in the previous review to S. aureus labelled for 15 seconds with ADA or ADA-DA, both wild-type (SH1000) and lacking the gene encoding PBP4 (SH1000 pbp4). There were 10 cells in each group. We saw no substantial difference in autocorrelation function. This supports the qualitative observation that there is no major difference in incorporation distribution of ADA or ADA-DA, in the presence or absence of PBP4 (subsection “Peptidoglycan synthesis in S. aureus does not occur in discrete foci”, second paragraph and Figure 3—figure supplement 3).

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Mean labelling densities (to the nearest one localisation) were:

SampleLabellingDensity
Wild TypeADA436 localisations per cell
pbp4ADA881 localisations per cell
Wild TypeADA-DA683 localisations per cell
pbp4ADA-DA641 localisations per cell

It is risky to compare numbers of localisations (and thus any kind of density) when using Alexa Fluor 647 (or a similar fluorophore) as each molecule may blink multiple times within the duration an experiment. This comparison is not advisable for our data set as we terminated imaging when no more blinks could be collected, rather than after a fixed time period, and adjusted laser power for maximum blinks.

https://doi.org/10.7554/eLife.32057.041

Article and author information

Author details

  1. Victoria A Lund

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Contributed equally with
    Katarzyna Wacnik and Robert D Turner
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1637-2023

  2. Katarzyna Wacnik

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Contributed equally with
    Victoria A Lund and Robert D Turner
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9921-6746

  3. Robert D Turner

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    3. Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Contributed equally with
    Victoria A Lund and Katarzyna Wacnik
    Competing interests
    No competing interests declared

  4. Bryony E Cotterell

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    3. Department of Chemistry, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared

  5. Christa G Walther

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Resources, Formal analysis, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8962-3102

  6. Samuel J Fenn

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Formal analysis, Investigation, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared

  7. Fabian Grein

    Institute for Pharmaceutical Microbiology, German Center for Infection Research (DZIF), University of Bonn, Bonn, Germany
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared

  8. Adam JM Wollman

    Biological Physical Sciences Institute, University of York, York, United Kingdom
    Contribution
    Resources, Data curation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5501-8131

  9. Mark C Leake

    Biological Physical Sciences Institute, University of York, York, United Kingdom
    Contribution
    Formal analysis, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1715-1249

  10. Nicolas Olivier

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared

  11. Ashley Cadby

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Physics and Astronomy, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Methodology, Writing—review and editing
    Competing interests
    No competing interests declared

  12. Stéphane Mesnage

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Supervision, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared

  13. Simon Jones

    Department of Chemistry, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Formal analysis, Methodology, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8043-7998

  14. Simon J Foster

    1. Krebs Institute, University of Sheffield, Sheffield, United Kingdom
    2. Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield, United Kingdom
    Contribution
    Formal analysis, Supervision, Funding acquisition, Project administration, Writing—review and editing
    For correspondence
    s.foster@sheffield.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7432-7805

Funding

Medical Research Council (MR/N002679/1)

  • Simon J Foster

Biotechnology and Biological Sciences Research Council (BB/L006162/1)

  • Simon J Foster

Medical Research Council (MR/K015753/1)

  • Simon J Foster

Medical Research Council (G1100127)

  • Simon J Foster

Medical Research Council (MR/K01580X/1)

  • Mark C Leake

Biotechnology and Biological Sciences Research Council (BB/N006453/1)

  • Mark C Leake

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work was funded by the Medical Research Council (MR/N002679/1, MR/K015753/1, G1100127, MR/K01580X/1) and the Biotechnology and Biological Science Research Council UK (BB/L006162/1, BB/N006453/1). We are grateful to Gareth McVicker, Simon Thorpe, Chris Hill, Irene Johnson and Joe Kirk for their assistance.

Reviewing Editor

  1. Jie Xiao, Johns Hopkins University, United States

Publication history

  1. Received: September 18, 2017
  2. Accepted: January 26, 2018
  3. Version of Record published: February 21, 2018 (version 1)

Copyright

© 2018, Lund et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Victoria A Lund
  2. Katarzyna Wacnik
  3. Robert D Turner
  4. Bryony E Cotterell
  5. Christa G Walther
  6. Samuel J Fenn
  7. Fabian Grein
  8. Adam JM Wollman
  9. Mark C Leake
  10. Nicolas Olivier
  11. Ashley Cadby
  12. Stéphane Mesnage
  13. Simon Jones
  14. Simon J Foster
(2018)
Molecular coordination of Staphylococcus aureus cell division
eLife 7:e32057.
https://doi.org/10.7554/eLife.32057

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    Russell P Swift, Rubayet Elahi ... Sean T Prigge
    Research Article Updated

    Iron-sulfur clusters (FeS) are ancient and ubiquitous protein cofactors that play fundamental roles in many aspects of cell biology. These cofactors cannot be scavenged or trafficked within a cell and thus must be synthesized in any subcellular compartment where they are required. We examined the FeS synthesis proteins found in the relict plastid organelle, called the apicoplast, of the human malaria parasite Plasmodium falciparum. Using a chemical bypass method, we deleted four of the FeS pathway proteins involved in sulfur acquisition and cluster assembly and demonstrated that they are all essential for parasite survival. However, the effect that these deletions had on the apicoplast organelle differed. Deletion of the cysteine desulfurase SufS led to disruption of the apicoplast organelle and loss of the organellar genome, whereas the other deletions did not affect organelle maintenance. Ultimately, we discovered that the requirement of SufS for organelle maintenance is not driven by its role in FeS biosynthesis, but rather, by its function in generating sulfur for use by MnmA, a tRNA modifying enzyme that we localized to the apicoplast. Complementation of MnmA and SufS activity with a bacterial MnmA and its cognate cysteine desulfurase strongly suggests that the parasite SufS provides sulfur for both FeS biosynthesis and tRNA modification in the apicoplast. The dual role of parasite SufS is likely to be found in other plastid-containing organisms and highlights the central role of this enzyme in plastid biology.

    1. Cell Biology
    2. Microbiology and Infectious Disease

    Profiling the bloodstream form and procyclic form Trypanosoma brucei cell cycle using single-cell transcriptomics

    Emma M Briggs, Catarina A Marques ... Keith R Matthews
    Research Article Updated

    African trypanosomes proliferate as bloodstream forms (BSFs) and procyclic forms in the mammal and tsetse fly midgut, respectively. This allows them to colonise the host environment upon infection and ensure life cycle progression. Yet, understanding of the mechanisms that regulate and drive the cell replication cycle of these forms is limited. Using single-cell transcriptomics on unsynchronised cell populations, we have obtained high resolution cell cycle regulated (CCR) transcriptomes of both procyclic and slender BSF Trypanosoma brucei without prior cell sorting or synchronisation. Additionally, we describe an efficient freeze–thawing protocol that allows single-cell transcriptomic analysis of cryopreserved T. brucei. Computational reconstruction of the cell cycle using periodic pseudotime inference allowed the dynamic expression patterns of cycling genes to be profiled for both life cycle forms. Comparative analyses identify a core cycling transcriptome highly conserved between forms, as well as several genes where transcript levels dynamics are form specific. Comparing transcript expression patterns with protein abundance revealed that the majority of genes with periodic cycling transcript and protein levels exhibit a relative delay between peak transcript and protein expression. This work reveals novel detail of the CCR transcriptomes of both forms, which are available for further interrogation via an interactive webtool.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease

    Integrating contact tracing and whole-genome sequencing to track the elimination of dog-mediated rabies: an observational and genomic study

    Kennedy Lushasi, Kirstyn Brunker ... Katie Hampson
    Research Article

    Background:

    Dog-mediated rabies is endemic across Africa causing thousands of human deaths annually. A One Health approach to rabies is advocated, comprising emergency post-exposure vaccination of bite victims and mass dog vaccination to break the transmission cycle. However, the impacts and cost-effectiveness of these components are difficult to disentangle.

    Methods:

    We combined contact tracing with whole-genome sequencing to track rabies transmission in the animal reservoir and spillover risk to humans from 2010-2020, investigating how the components of a One Health approach reduced the disease burden and eliminated rabies from Pemba Island, Tanzania. With the resulting high-resolution spatiotemporal and genomic data we inferred transmission chains and estimated case detection. Using a decision tree model we quantified the public health burden and evaluated the impact and cost-effectiveness of interventions over a ten-year time horizon.

    Results:

    We resolved five transmission chains co-circulating on Pemba from 2010 that were all eliminated by May 2014. During this period, rabid dogs, human rabies exposures and deaths all progressively declined following initiation and improved implementation of annual islandwide dog vaccination. We identified two introductions to Pemba in late 2016 that seeded re-emergence after dog vaccination had lapsed. The ensuing outbreak was eliminated in October 2018 through reinstated islandwide dog vaccination. While post-exposure vaccines were projected to be highly cost-effective ($256 per death averted), only dog vaccination interrupts transmission. A combined One Health approach of routine annual dog vaccination together with free post-exposure vaccines for bite victims, rapidly eliminates rabies, is highly cost-effective ($1657 per death averted) and by maintaining rabies freedom prevents over 30 families from suffering traumatic rabid dog bites annually on Pemba island.

    Conclusions:

    A One Health approach underpinned by dog vaccination is an efficient, cost-effective, equitable and feasible approach to rabies elimination, but needs scaling up across connected populations to sustain the benefits of elimination, as seen on Pemba, and for similar progress to be achieved elsewhere.

    Funding:

    Wellcome [207569/Z/17/Z, 095787/Z/11/Z, 103270/Z/13/Z], the UBS Optimus Foundation, the Department of Health and Human Services of the National Institutes of Health [R01AI141712] and the DELTAS Africa Initiative [Afrique One-ASPIRE/DEL-15-008] comprising a donor consortium of the African Academy of Sciences (AAS), Alliance for Accelerating Excellence in Science in Africa (AESA), the New Partnership for Africa's Development Planning and Coordinating (NEPAD) Agency, Wellcome [107753/A/15/Z], Royal Society of Tropical Medicine and Hygiene Small Grant 2017 [GR000892] and the UK government. The rabies elimination demonstration project from 2010-2015 was supported by the Bill & Melinda Gates Foundation [OPP49679]. Whole-genome sequencing was partially supported from APHA by funding from the UK Department for Environment, Food and Rural Affairs (Defra), Scottish government and Welsh government under projects SEV3500 & SE0421.