1. Microbiology and Infectious Disease
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Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in Helicobacter pylori

  1. Jennifer A Taylor
  2. Benjamin P Bratton
  3. Sophie R Sichel
  4. Kris M Blair
  5. Holly M Jacobs
  6. Kristen E DeMeester
  7. Erkin Kuru
  8. Joe Gray
  9. Jacob Biboy
  10. Michael S VanNieuwenhze
  11. Waldemar Vollmer
  12. Catherine L Grimes
  13. Joshua W Shaevitz
  14. Nina R Salama  Is a corresponding author
  1. University of Washington, United States
  2. Fred Hutchinson Cancer Research Center, United States
  3. Princeton University, United States
  4. University of Delaware, United States
  5. Harvard Medical School, United States
  6. Newcastle University, United Kingdom
  7. Indiana University, United States
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Cite this article as: eLife 2020;9:e52482 doi: 10.7554/eLife.52482

Abstract

Helical cell shape is necessary for efficient stomach colonization by Helicobacter pylori, but the molecular mechanisms for generating helical shape remain unclear. The helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod H. pylori. Quantitative 3D microscopy analysis of short pulses with either N-acetylmuramic acid or D-alanine metabolic probes showed that cell wall growth is enhanced at both sidewall curvature extremes. Immunofluorescence revealed MreB is most abundant at negative Gaussian curvature, while the bactofilin CcmA is most abundant at positive Gaussian curvature. Strains expressing CcmA variants with altered polymerization properties lose helical shape and associated positive Gaussian curvatures. We thus propose a model where CcmA and MreB promote PG synthesis at positive and negative Gaussian curvatures, respectively, and that this patterning is one mechanism necessary for maintaining helical shape.

eLife digest

Round spheres, straight rods, and twisting corkscrews, bacteria come in many different shapes. The shape of bacteria is dictated by their cell wall, the strong outer barrier of the cell. As bacteria grow and multiply, they must add to their cell wall while keeping the same basic shape. The cells walls are made from long chain-like molecules via processes that are guided by protein scaffolds within the cell. Many common antibiotics, including penicillin, stop bacterial infections by interrupting the growth of cell walls.

Helicobacter pylori is a common bacterium that lives in the gut and, after many years, can cause stomach ulcers and stomach cancer. H. pylori are shaped in a twisting helix, much like a corkscrew. This shape helps H. pylori to take hold and colonize the stomach.

It remains unclear how H. pylori creates and maintains its helical shape. The helix is much more curved than other bacteria, and H. pylori does not have the same helpful proteins that other curved bacteria do. If H. pylori grows asymmetrically, adding more material to the cell wall on its long outer side to create a twisting helix, what controls the process?

To find out, Taylor et al. grew H. pylori cells and watched how the cell walls took shape. First, a fluorescent dye was attached to the building blocks of the cell wall or to underlying proteins that were thought to help direct its growth. The cells were then imaged in 3D, and images from hundreds of cells were reconstructed to analyze the growth patterns of the bacteria’s cell wall.

A protein called CcmA was found most often on the long side of the twisting H. pylori. When the CcmA protein was isolated in a dish, it spontaneously formed sheets and helical bundles, confirming its role as a structural scaffold for the cell wall. When CcmA was absent from the cell of H. pylori, Taylor et al. observed that the pattern of cell growth changed substantially.

This work identifies a key component directing the growth of the cell wall of H. pylori and therefore, a new target for antibiotics. Its helical shape is essential for H. pylori to infect the gut, so blocking the action of the CcmA protein may interrupt cell wall growth and prevent stomach infections.

Introduction

Helicobacter pylori is a helical Gram-negative bacterium that colonizes the human stomach and can cause stomach ulcers and gastric cancers (Correa, 1988). Helical cell shape is necessary for efficient stomach colonization (Bonis et al., 2010; Sycuro et al., 2012; Sycuro et al., 2010), underscoring its importance. H. pylori is a main model organism for studying helical cell shape, in part because it is a genetically tractable organism with a compact genome that minimizes redundancy (Tomb et al., 1997). Key non-redundant, non-essential contributors to cell shape have been identified, but the question of how they enable H. pylori to be helical remains largely unsolved.

As is the case for most bacteria (Höltje, 1998), the structure of the H. pylori peptidoglycan (PG) cell wall (sacculus) is ultimately responsible for the shape of the cell; purified cell walls maintain helical shape (Sycuro et al., 2010). PG is a polymer of alternating N-acetylglucosamine (GlcNAc) and N-acetylmuramic acid (MurNAc) with an attached peptide stem that can be crosslinked to a peptide stem of an adjacent PG strand. Crosslinked PG strands form the cell wall, a large mesh-like macromolecule that surrounds the cell and counteracts the cell’s turgor pressure (Höltje, 1998; Typas et al., 2012). The PG monomer is synthesized in the cytoplasm and subsequently flipped across the inner membrane and incorporated into the existing PG by the glycosyltransferase activities of penicillin binding proteins (PBPs) and shape, elongation, division, and sporulation (SEDS) proteins, and the transpeptidation activities of PBPs (Meeske et al., 2016; Sauvage et al., 2008).

Helical cell shape maintenance in H. pylori requires a suite of both PG-modifying enzymes (Csd1, Csd3/HdpA, Csd4, and Csd6) to remodel the cell wall and non-enzymatic proteins (Csd2, Csd5, CcmA, and Csd7) that may act as scaffolds or play other structural roles (Bonis et al., 2010; Sycuro et al., 2013; Sycuro et al., 2012; Sycuro et al., 2010; Yang et al., 2019). One of the non-enzymatic proteins is the putative bactofilin CcmA. Bactofilins are bacteria-specific cytoskeletal proteins with diverse functions, including playing a role in stalk elongation in Caulobacter crescentus (Kühn et al., 2010) and helical pitch modulation in Leptospira biflexa (Jackson et al., 2018). CcmA loss in H. pylori results in rod-shaped cells with minimal sidewall curvature (Sycuro et al., 2010). As with other organisms, H. pylori CcmA has been shown to self-oligomerize (Holtrup et al., 2019). Recently CcmA was shown to co-purify with Csd5 and the PG biosynthetic enzyme MurF (Blair et al., 2018), suggesting CcmA may influence cell wall growth.

Patterning PG synthesis has been shown to be an important mechanism for cell shape maintenance in several model organisms. In the rod shaped Escherichia coli, MreB helps direct synthesis preferentially to sites at or below zero Gaussian curvature. One working model is that this growth pattern promotes rod shape by accelerating growth at dents and restricting growth at bulges along the sidewall, thereby enforcing diameter control (Bratton et al., 2018; Ursell et al., 2014). In the Gram-positive Bacillus subtilis, MreB filaments have been shown to move in paths oriented approximately perpendicular to the long axis of rod shaped cells. The relative organization of path orientations decreases with an increase in rod diameter, suggesting that filament orientation is sensitive to changes in cell surface curvatures (Hussain et al., 2018).

Here, we demonstrate that the surface of helical H. pylori cells is characterized by large regions of both positive and negative Gaussian curvature. To investigate how H. pylori achieves diameter control while simultaneously maintaining sidewall curvature, we employed two metabolic probes to investigate PG synthesis patterning in H. pylori. Using superresolution microscopy and 3D quantitative image analysis, we show that synthesis is enhanced at negative Gaussian curvature as well as at a limited range of positive Gaussian curvatures. We furthermore investigate the localization of cytoskeletal proteins MreB and CcmA. We demonstrate that, as in straight-rod shaped E. coli cells, MreB is enriched at negative curvature. CcmA is enriched at the window of positive Gaussian curvatures where enhanced synthesis is observed. We propose that both MreB and CcmA help maintain PG synthesis activity locally and that PG synthesis patterning is one mechanism that plays a fundamental role in helical cell shape maintenance.

Results

Helical cells maintain areas of positive and negative Gaussian curvature on the sidewall

Unlike straight-rod shaped bacteria, helical H. pylori cells maintain distinct and diverse cell surface curvatures along the sidewall (Figure 1 and Figure 2). To characterize the cell surface curvature features of H. pylori in detail, we stained permeabilized cells with fluorescent wheat germ agglutinin (WGA), which binds GlcNAc and thus labels the cell wall. Since the dimensions of H. pylori cells (1.5–3.5 µm in length and 0.45 µm in diameter Figure 3) are near the limit of light microscopy resolution, we employed 3D structured illumination microscopy (SIM) to more clearly resolve cells in three dimensions (Figure 1A). We adapted previous image processing software (Bartlett et al., 2017; Morgenstein et al., 2015) to accommodate characteristic SIM artifacts and enhanced resolution in order to generate a 3D triangular meshwork surface with roughly 30 nm precision from the SIM z-stack images (Figure 1A and B, matched SIM image volumes and surface reconstructions). Display of the Gaussian curvature, which is the product of the two principal curvatures, at each point on the meshwork shows the distinct curvatures on opposite sides of helical cells (Figure 1B). Using Gaussian curvature allows us to focus on local curvature geometry. We operationally define the minor helical axis as the shortest helical path along the sidewall within the zone of moderate negative curvature (minor helical axis area, −15 to −5 µm−2, blue), and define the major axis as the path opposite the minor helical axis, which resides within the zone of moderate positive curvature (major helical axis area, 5 to 15 µm−2, red) (Figure 1C). The cell poles are characterized by high positive curvature (>15 µm−2, gray).

Helical cell surfaces feature areas of distinct curvatures.

(A) 3D SIM images of individual H. pylori cells stained with fluorescent wheat germ agglutinin (WGA). Top-down view (left) and 90-degree rotation about the long axis (right). Scale bar = 0.5 µm; images from one experiment. (B) Corresponding views of computational surface reconstructions of cells in (A). with Gaussian curvature plotted (scale at right - blue: moderate negative; white: zero; red: moderate positive; gray: high positive). Computationally-defined polar regions are delineated by the thin black line. Polar regions correspond to regions whose centerline points are within 0.75 of a cell diameter to the terminal pole positions. (C) Schematic of minor (blue line) and major (red line) helical axes.

The distribution of surface Gaussian curvature for helical cells is distinct from that of curved- and straight-rod cells.

Smooth histograms of the distribution of surface Gaussian curvatures for a population of cells (wild-type helical, yellow; curved-rod Δcsd2, teal; straight-rod Δcsd6, indigo) with poles included (A) or sidewall only (B, poles excluded). The region to the right of the dotted vertical lines corresponds to curvatures contributed almost exclusively by the poles. Histograms are derived using a bin size of 0.2 µm−2. Example computational surface reconstructions (top right of each histogram) of a wild-type helical, curved-rod Δcsd2, and straight-rod Δcsd6 cell with Gaussian curvatures displayed as in Figure 1. The data represented are from one replicate.

Figure 3 with 4 supplements see all
Three-dimensional shape properties of a wild-type helical population.

Analysis of the wild-type population in Figure 2 from the 231 wild-type cells for which the cell centerline was well-fit by a helix. (A) Schematic of helical-rod shape parameters (cell centerline length, gray; cell diameter, purple; helical pitch, pink; and helical diameter, green). (B) Example cell with helical coordinate system and the major (red line, 0°) and minor (blue line, 180°) helical axes shown on the cell sidewall. Population distributions of (C) cell centerline lengths, (D) average cell diameters, (E) helical pitch, (F) helical diameter, (G) major to minor axis length ratio, and (H) the average Gaussian curvature for a given helical coordinate system unwrap angle. Colored dotted lines in (C–G) indicate the mean ±1.5 standard deviations in 0.5 standard deviation steps. Shaded line in (H) indicates ±1 standard deviation about the mean. Distributions of parameters (C–D) are from real cells, parameters (E–F) are from helical centerline fits, and properties (G–H) are measured from the matched synthetic cell sidewalls.

Our image reconstruction method performs faithful reconstructions of straight- and curved-rod cells (Figure 2, inset). To compare the surface curvatures maintained by helical (wild-type), curved-rod (Δcsd2), and straight-rod (Δcsd6) cells, we pooled reconstructions of hundreds of non-septating cells for each genotype and plotted a histogram of the proportion of surface curvature points with a given Gaussian curvature value (Figure 2). All three cell shapes share a tail of high positive curvatures from the cell poles (Figure 2A, right of the dotted line). In order to study the sidewall alone, we developed an algorithm to computationally define and exclude poles (Figure 1B, black lines). With the poles removed, the extended tail disappears for each cell shape. In contrast to the other shapes, helical cells have a large proportion of sidewall area with curvatures less than −5 µm−2 and an even larger proportion with curvatures greater than 5 µm−2 (Figure 2B). Rather than having a unimodal distribution, helical cells have a multimodal distribution that includes an apparent peak at negative curvature and another at positive curvature.

The sidewall curvature distribution informed us about the overall types of surface curvature wild-type cells need to achieve, but was not sufficient to let us directly compare the surface properties of the major and minor axes, specifically the relative lengths of the major and minor axes and the average Gaussian curvature along both axes. Furthermore, prior shape parameter characterizations of H. pylori have been performed using 2D images (Martínez et al., 2016; Sycuro et al., 2013; Sycuro et al., 2012; Sycuro et al., 2010; Yang et al., 2019); measurement of pitch and helical radius from 2D images is subject to systematic errors for short cells (approximately <1.5 helical turns) depending on their orientation on the coverslip. Therefore, we also wished to determine H. pylori population shape parameters from our 3D dataset. To characterize the major and minor axes, we needed to find these axes on each reconstructed cell surface. While cells in our experiments appear helical, in reality they have surface imperfections and centerlines with kinks, bends, or variation in pitch along the centerline (Sycuro et al., 2010). We therefore limited ourselves to considering the relative length of the major and minor helical axes of a population of simulated, idealized cells, each of which mimics a cell from the wild-type population described in Figure 2 (for full details see Appendix 1). In brief, to both derive the cell shape parameters necessary to generate the simulated cells and to further characterize the 3D shape parameters of the wild-type population, we measured the cell lengths from one pole to the other along the curved centerlines (Figure 3A and C, gray); the diameters of the cells (Figure 3A and D, purple); the helical pitches of the centerlines (Figure 3A and E, pink); and the helical diameters of the centerlines (Figure 3A and F, green).

Wild-type cells are 2.5 ± 0.5 µm long and 0.45 ± 0.02 µm in diameter, have a helical pitch of 1.7 ± 1 µm, and have a helical diameter of 0.3 ± 0.1 µm (mean ± standard deviation, Figure 3C–F). These parameters are derived from a subset of the wild-type population that can be modeled as a uniform helix (Figure 3—figure supplement 1 and Figure 3—video 1). The distribution of cell lengths, diameters, and surface curvatures of the subset closely match that of the whole population (Figure 3—figure supplement 1C–E). Using the simulated counterparts to these cells, we determined that the average major to minor length ratio is 1.69 ± 0.16, meaning that the major axis is on average 70% longer than the minor axis (Figure 3G). We also determined from the simulated cells that the average Gaussian curvature at the major axis is 5 ± 1 µm−2, and the average Gaussian curvature at the minor axis is −11 ± 4 µm−2 (Figure 3H).

We next used our simulation framework to explore how the four helical-rod shape parameters affect the length ratio of the major to minor helical axes. Changes in cell length and cell diameter had almost no effect, whereas increasing the helical diameter or decreasing the helical pitch increased the relative length of the major axis (Figure 3—figure supplement 2, right column), consistent with the idea that a helix is formed by differential expansion of the major and minor axes. We then investigated how each of these parameters influences the distribution of surface curvatures along the sidewall. We began with a cell simulated from the population average of all four parameters (cell length, cell diameter, helical pitch, and helical diameter), and changed each property individually within the range of variation represented in the wild-type population (±1.5 standard deviations) while holding the other three constant (Figure 3—figure supplements 2 and 3). Each of the dashed colored lines in Figure 3C–F correspond to the parameters used to simulate these altered cell shapes. Changing cell length had a negligible impact on the distribution of surface curvatures along the sidewall (Figure 3—figure supplement 2A). Decreasing the cell diameter had a relatively small effect given the narrow distribution of cell diameters observed in the wild-type population (Figure 3—figure supplement 2B). Changing the two parameters describing the properties of the helix had a larger impact on the distribution of Gaussian curvatures. Decreasing the pitch resulted in a helix with tighter coils and a greater distance between the peak of negative and positive Gaussian surface curvatures (Figure 3—figure supplement 2C). Increasing the helical diameter resulted in cells that looked less like straight-rod cells and had a greater distance between the peak of negative and positive Gaussian surface curvatures (Figure 3—figure supplement 2D). In holding with the Gauss-Bonnet theorem, cells had a greater proportion of sidewall area with positive Gaussian curvature than with negative, and the magnitude of the positive Gaussian curvature was less than that of the negative Gaussian curvature.

Having established the substantial difference in the length of the major and minor axes, we wondered if differential synthesis at these cellular landmarks might help explain helical shape maintenance. Although it is not currently possible to computationally define the helical axes on surface reconstructions of actual cells due to their imperfections, our data indicate that we can use Gaussian curvatures of 5 µm−2 and −11 µm−2 as a proxy for the major and minor axes, respectively, in population level data.

H. pylori can incorporate modified D-alanine and modified MurNAc into peptidoglycan

Since a helical cell must maintain large regions of positive and of negative curvatures, we hypothesized that H. pylori may have a different growth pattern than that of E. coli, where the majority of the sidewall regions have Gaussian curvature near zero. To determine where new PG is preferentially inserted, we used two metabolic probes of PG incorporation. First, we attempted labeling wild-type cells with MurNAc-alkyne (MurNAc-alk), but H. pylori is unable to readily use exogenous MurNAc. We then engineered a strain, HJH1, containing recycling enzymes AmgK and MurU from Pseudomonas putida (Gisin et al., 2013) at the rdxA locus, a neutral locus routinely used for expression of genes in H. pylori (Goodwin et al., 1998; Smeets et al., 2000). These enzymes convert MurNAc into UDP-MurNAc, which can then be used to form PG subunit precursors (Figure 4—figure supplement 1). To verify that HJH1 can indeed use exogenous MurNAc, we assayed rescue from fosfomycin treatment. Fosfomycin blocks the first committed step in PG precursor synthesis by preventing the conversion of UDP-GlcNAc into UDP-MurNAc (Figure 4—figure supplement 1). We determined the minimum inhibitory concentration (MIC) of fosfomycin of our strain to be 25 µg/ml (Figure 4—figure supplement 2). Supplementation with 4 mg/ml MurNAc partially rescued growth of HJH1 in the presence of 50 µg/ml fosfomycin, but not the parental strain (LSH108) (Figure 4A).

Figure 4 with 4 supplements see all
Validation of PG metabolic probes.

(A) 10-fold dilutions showing LSH108 (rdxA::catsacB) or HJH1 (rdxA::amgKmurU) treated with 50 µg/ml fosfomycin or untreated and with or without 4 mg/ml MurNAc supplementation, from one representative of three biological replicates. (B and C) Verification of MurNAc-alk incorporation into pentapeptides (left column) and tetra-pentapeptides (right column) by HPLC/MS/MS. (B) Extracted ion chromatograms (EICs) for the ion masses over the HPLC elution for unlabeled (lower EIC) and labeled (top EIC) sacculi. (C) Spectra of the ions observed during LC-MS for the MurNAc-alk pentapeptide (left, non-reduced, predicted [M+H]+ ion m/z = 1049.452) and MurNAc-alk tetra-pentapeptide dimer (right, non-reduced, predicted [M+2H]2+ ion m/z = 985.920). (D) Verification of D-Ala-alk incorporation into pentapeptides and tetra-pentapeptides. HPLC chromatograms of labeled (top) and unlabeled (bottom) sacculi. The main monomeric and dimeric muropeptides are labeled (4, disaccharide tetrapeptide; 5, disaccharide pentapeptide; 44, bis-disacccharide tetratetrapeptide; 45, bis-disacharide tetrapentapeptide). D-Ala-alk-modified muropeptides (top, 5a and 45a) are present only in the sample from labeled cells and were confirmed by MS analysis of the collected peak fractions. 5a, alk-labeled disaccharide pentapeptide (neutral mass: 1036.448); 45a, alk-labelled bis-disaccharide tetrapentapeptide (neutral mass: 1959.852). Data (B, C, and D) are from one replicate.

To verify that clickable MurNAc-alk is indeed incorporated into the cell wall, we purified sacculi from HJH1 labeled with MurNAc-alk for six doublings for MS/MS analysis. We positively identified MurNAc-alk-pentapeptide and MurNAc-alk-tetra-pentapeptide, the most abundant monomeric and dimeric species in the H. pylori cell wall, (Figure 4B,C and Figure 4—figure supplement 3), as well as less-abundant species (Table 1), confirming incorporation. Cells were labeled without the addition of fosfomycin, indicating the HJH1 strain can use MurNAc-alk even when unmodified MurNAc is available in the cell.

Table 1
MurNAc-alk incorporation into PG
Muropeptide
(non-reduced)
Theoretical neutral massMurNAc-alk labeled H. pyloriControl H. pylori
Observed ion (charge)Rt*
(min)
Calculated neutral
mass
Observed ion (charge)Rt*
(min)
Calculated neutral
mass
Di696.270697.289 (1+)20.3696.282697.290 (1+)20.4696.283
Alk-Di734.286735.307 (1+)30.5734.300---
Tri868.355869.375 (1+)15.8868.368869.374 (1+)15.8868.367
Alk-Tri906.371907.392 (1+)25.8906.385---
Tetra939.392940.411 (1+)20.4939.404940.412 (1+)20.4939.405
Alk-Tetra977.408978.428 (1+)30.4977.421---
Penta1010.4291011.449 (1+)22.91010.4421011.449 (1+)22.81010.442
Alk-Penta1048.4451049.464 (1+)32.91048.457---
TetraTri1789.736895.889 (2+)33.41789.762895.888 (2+)33.31789.761
Alk-TetraTri1827.752914.898 (2+)39.21827.781---
TetraTetra1860.774931.407 (2+)35.01860.799931.407 (2+)34.91860.799
Alk-TetraTetra1898.789950.416 (2+)39.71898.817---
TetraPenta1931.811966.926 (2+)35.81931.837966.925 (2+)35.71931.835
Alk-TetraPenta1969.826985.934 (2+)39.91969.853---
  1. * Rt, retention time.

    -, not detected. Muropeptides detected (confirming incorporation) via LC-MS analysis of MurNAc-alk labeled versus control PG digests. The control cells displayed no evidence of any MurNAc-alk incorporation.

As a second strategy for labeling new PG incorporation, we used D-alanine-alkyne (D-Ala-alk) (Kuru et al., 2012; Siegrist et al., 2013). This probe can be incorporated through the activity of PG transpeptidases (Figure 4—figure supplement 1). To verify that D-Ala-alk is incorporated into the cell wall and to determine the position(s) at which it is incorporated, we purified sacculi from wild-type (LSH100) cells labeled for six doublings for analysis. D-Ala-alk was detected in only pentapeptide monomers and tetra-pentapeptide dimers, indicating that D-Ala-alk is exclusively incorporated at the pentapeptide position (Figure 4D and Figure 4—figure supplement 4).

PG synthesis is enriched at both negative Gaussian curvature and the major helical axis area

To visualize new PG incorporation, we labeled HJH1 with either MurNAc-alk or D-Ala-alk for 18 min (approximately 12% of the doubling time). AF555-azide was conjugated to the alkyne groups using click chemistry and cells were counterstained with WGA-AF488. Cells were imaged using 3D SIM (Figure 5 and Figure 5—video 1). As expected, labeling was seen on the boundary of the cell but not in the cytoplasmic area (Figure 5D and H). For both metabolic probes, PG synthesis appeared to be excluded from the poles, dispersed along the sidewall, and present at septa. However, D-Ala-alk septal labeling appeared much brighter compared to MurNAc-alk septal labeling, indicating at least some difference between incorporation and/or turnover of the two probes. To discover if this labeling difference is due to curvature-biased transpeptidation rates, we also attempted labeling with dimers D-alanine-D-alanine-alkyne and D-alanine-alkyne-D-alanine, which is presumably incorporated predominantly through PG precursor biosynthesis in the cytoplasm, but no signal was detected (data not shown) (Liechti et al., 2014).

Figure 5 with 1 supplement see all
New cell wall growth appears dispersed along the sidewall, excluded from poles, and present at septa.

3D SIM imaging of wild-type cells labeled with an 18 min pulse of MurNAc-alk (A–D, yellow) or 18 min pulse of D-Ala-alk (E–H, yellow) counterstained with fluorescent WGA (blue). Color merged maximum projection of 18 min MurNAc-alk (A), D-Ala-alk (E), or mock (B, F) labeling with fluorescent WGA counterstain. (C, G) Top-down (left) and 90-degree rotation (right) 3D views of three individual cells, including a dividing cell at the right. Top: color merge; middle: 18 min MurNAc-alk (C) or D-Ala-alk (G); bottom: fluorescent WGA. (D, H) Color merged z-stack views of the three cells in (C, G), respectively (left to right = top to bottom of the cell). Numbering indicates matching cells. Scale bar = 0.5 µm. The representative images are selected from one of three biological replicates.

To quantify any curvature-based enrichment (expressed throughout as relative concentration vs. Gaussian curvature) of new cell wall synthesis, we used the fluorescent WGA signal to generate 3D cell surface reconstructions of hundreds of individual, non-septating cells labeled with MurNAc-alk, D-Ala-alk, or cells that were mock-labeled as a control. The Gaussian curvature was calculated at every location on the reconstructed 3D surface of the cell. Because the absolute amount of synthesis (or other signals of interest) can vary between cells, and because the level of illumination throughout the field of view is non-uniform, we set the average PG synthesis signal for each individual cell to one. We measured each cell’s curvature-dependent PG synthesis signal intensity relative to that average value, normalized by the amount of that curvature present on the surface, since there is more surface area associated with positive Gaussian curvature than negative (Figure 6A).

Figure 6 with 2 supplements see all
New cell wall growth is excluded from the poles and enriched at negative Gaussian curvature and the major axis area.

(A) The calculation of relative concentration for a specific probe involves two steps of normalization. First, the raw signal is summed up in bins defined by the Gaussian curvature at the surface. Then, this raw signal is normalized by dividing by the sum of the raw signal at all Gaussian curvatures (total signal). This normalizes for changes in total signal, fluorophore brightness, imaging conditions, etc. The second step is to divide by the fractional surface area, or amount of surface area contributed by each Gaussian curvature bin. This distribution is dependent on the observed shape of the cell. Following these two normalization steps, one has the concentration of the probe of interest relative to a uniformly distributed null model. For illustration, we have shown this graphical equation for three noise-free cells that have the same geometry, but different relative signal abundances. In the experimental data presented in the main text, the single cell relative concentration profile is averaged over hundreds of cells, each with their own unique geometry. Whole surface (B) and sidewall only (C) surface Gaussian curvature enrichment of relative concentration of new cell wall growth (y-axis) vs. Gaussian curvature (x-axis) derived from a population of computational cell surface reconstructions of MurNAc-alk (green), D-Ala-alk (blue) 18 min pulse-labeled, and mock-labeled (gray) cells. 90% bootstrap confidence intervals are displayed as a shaded region about each line. The represented data are pooled from three biological replicates.

As a tool to facilitate understanding and interpretation of these relative enrichment plots, we generated a synthetic cell surface with the same geometric properties as the average wild-type cell (Figure 3), applied a variety of example intensity distributions, and generated curvature enrichment plots. We began with a uniform baseline signal (Figure 6—figure supplement 1, ‘uniform - low’) and in each case added 25% extra signal intensity to specific geometries. In the enrichment profiles, a relative concentration value of one indicates that the average signal intensity at that curvature is the same as the average across the cell surface. Values greater than one indicate curvatures where normalized signal is enriched compared to average and values less than one indicate curvatures where normalized signal is depleted compared to average. These simulations illustrate the interrelated nature of the relative enrichment plots. Because there is more cell surface area with positive Gaussian curvature, adding 25% signal to this region (Figure 6—figure supplement 1, ‘enriched at major axis’) increases the average signal more than adding 25% signal at zero or negative Gaussian curvature. Thus by increasing the signal at positive curvature, the relative concentration decreases at the rest of the cell surface even though the absolute signal at these geometries remains the same. A similar change in relative concentration occurs with an increase in signal at zero or negative curvature (Figure 6—figure supplement 1, ‘enriched at zero’ and ‘enriched at minor axis’, respectively), but because there is less surface area with these curvatures, the magnitude of this change is lower. To further illustrate the implications of the interrelated nature of these plots, we added both signal with a monotonic decline profile (Figure 6—figure supplement 1, ‘monotonic decline’) and signal enriched at the major axis (Figure 6—figure supplement 1, ‘enriched at major axis’) to one cell surface (Figure 6—figure supplement 1, ‘monotonic decline and major axis’). By adding extra signal at the major axis area, the average concentration increases significantly, causing the rest of the relative concentrations to decrease compared to the monotonic decline profile alone. As these simulations demonstrate, relative enrichment plots must be considered holistically. The key features of interest are the overall increases, decreases, and peaks in the curves, along with the curvatures at which these occur.

We performed relative concentration enrichment analysis separately with the entire cell surface and with the sidewall only (poles removed) from the PG synthesis data. We then averaged the single cell measurements across more than 100 cells pooled from three biological replicates to obtain a profile of enrichment or depletion as a function of surface curvature. Curvature enrichment analysis of whole cell surfaces revealed that for both metabolic probes, signal was largely absent from the poles, as seen by the drop-off of relative enrichment at curvatures above 10 µm−2 (Figure 6B). To focus on the curvature enrichment pattern along the sidewall, we repeated the analysis after first computationally removing the poles. Looking at sidewall curvature alone, MurNAc-alk was enriched at two sites. At negative curvature, enrichment increases as curvature becomes more negative. At positive curvature, enrichment peaks near 6 µm−2 and then begins to decrease at higher curvatures (Figure 6C, green). D-Ala-alk showed peaks of enrichment aligning with those of MurNAc-alk (Figure 6C, blue), but the magnitude of the peak at positive curvature was reduced. The mock labeling control showed minimal curvature bias and is on average 3.6% of the D-Ala-alk signal and 4.5% of the MurNAc-alk signal (Figure 6B and C, gray and Figure 6—figure supplement 2B). This demonstrates that the fluorescent signal in the mock labeling is independent of geometry. Thus the nonspecific signal should contribute negligibly to the PG synthesis enrichment profiles. Biological replicates are shown in Figure 6—figure supplement 2A.

MreB is enriched at negative Gaussian curvature

The cytoskeletal protein MreB has been shown in rod-shaped organisms to preferentially localize to negative Gaussian curvatures near to and below zero and help direct PG synthesis (Bratton et al., 2018; Ursell et al., 2014). It has been reported that MreB is not essential in H. pylori and that treatment with the MreB inhibitor A22 does not alter cell shape (Waidner et al., 2009), though growth inhibition only occurred at concentrations well above those used to select for A22 resistance in other organisms (Gitai et al., 2005; Ouzounov et al., 2016; Srivastava et al., 2007; Wu et al., 2011). Since multiple attempts to knock out mreB in wild-type LSH100 were unsuccessful, we generated IM4, a merodiploid strain with a second copy of mreB at a neutral intergenic locus (McGee locus Langford et al., 2006) (Figure 7—figure supplement 1A) for comparative transformation experiments. To verify that both LSH100 and IM4 are readily transformable, we performed parallel transformations with a ccmA::CAT deletion cassette. LSH100 and IM4 showed similar transformation efficiencies (2.4 × 10−4 and 1.2 × 10−4, respectively) (Figure 7A). We transformed LSH100 and IM4 with an mreB::CAT deletion cassette (Figure 7A and Figure 7—figure supplement 1A) and obtained mreB targeting transformants in strain IM4 at a frequency of 2.3 × 10−4. The CAT resistance cassette integrated into mreB at either the native locus or the McGee locus (19 and 5 of 24 clones tested, respectively) (Figure 7—figure supplement 1B). In contrast, we obtained two colonies after transformation of LSH100 (frequency of 6.7 × 10−7). Sequencing revealed that an amplification event at the mreB locus occurred for each of these clones, such that an uninterrupted copy of mreB was present in addition to a copy of mreB::CAT (Figure 7—figure supplement 1D). Western blotting revealed that MreB was produced at wild-type levels in clone #2, but only a faint band was observed for clone #1 (Figure 7—figure supplement 1C). In clone #1, the terminal four amino acids were replaced due to the recombination event (GFSE to FLAN). One of the four epitopes used to generate the anti-MreB antibody includes the four terminal amino acids (Nakano et al., 2012), likely explaining the discrepancy between the sequencing results and western blot detection. While we requested the previously published mreB mutant strains (Waidner et al., 2009), they could not be revived from frozen stocks. We thus conclude that MreB is essential in LSH100 and perhaps all H. pylori strains.

Figure 7 with 4 supplements see all
MreB is essential in LSH100 and is present as small foci enriched at negative Gaussian curvature.

(A) Schematic of transformation experiment testing MreB essentiality in LSH100 (WT) and IM4 (2XmreB) (left) and corresponding transformation frequencies (right). *=two recombinant clones with mreB duplication (see Figure 7—figure supplement 1 for details). 3D SIM imaging of wild-type cells immunostained with anti-MreB (B, D, E, yellow) or preimmune serum (C, yellow) and counterstained with fluorescent WGA (blue). (B, C) Color merged maximum projections (D) Top-down (left) and 90-degree rotation (right) 3D views of three individual cells. Top: color merge; middle: anti-MreB; bottom: fluorescent WGA. (E) Color merged z-stack views of the three cells in (A). (left to right = top to bottom of the cell). Numbering indicates matching cells. Scale bar = 0.5 µm. (F) Sidewall only surface Gaussian curvature enrichment plots for a population of cells immunostained with anti-MreB (pink), or preimmune serum (gray). Smooth line plot (solid line) of relative MreB concentration (y-axis) vs. Gaussian curvature (x-axis) derived from a population of computational cell surface reconstructions with poles excluded. 90% bootstrap confidence intervals are displayed as a shaded region about each line. The representative images are selected from one of three biological replicates and the data shown in (F) are pooled from the three biological replicates.

We investigated MreB localization to determine if an altered curvature preference might account for the PG synthesis pattern we observed. Immunofluorescence labeling with 3D SIM imaging revealed that MreB is present at the cell periphery as many individual foci and some short arcs that appear to be oriented approximately circumferentially and excluded from the poles (Figure 7B,D and E and Figure 7—video 1). Only sparse foci were seen with immunofluorescence using the preimmune serum (Figure 7C). Curvature enrichment analysis of non-dividing cells confirmed that MreB localization is depleted at the poles (Figure 7—figure supplement 2). Regardless of whether the poles were included in the analysis, we observed that as Gaussian curvature became more negative, relative MreB concentration increased monotonically (Figure 7F and Figure 7—figure supplement 2). Biological replicates are shown in Figure 7—figure supplement 3A. This echoes the enrichment of PG synthesis at negative Gaussian curvature; as Gaussian curvature became more negative (below −2 µm−2), relative PG synthesis increased monotonically. Preimmune serum signal was 36.4% of the MreB signal (Figure 7—figure supplement 3B), but did not show a curvature preference (Figure 7E, gray). Thus, MreB may promote the enhanced PG synthesis observed at negative curvature.

The bactofilin CcmA forms filaments, bundles, and lattices in vitro

We reasoned that another cytoskeletal element might promote the higher relative PG synthesis observed at the major axis area. While both coiled-coil rich proteins (Ccrp) and the bactofilin homolog CcmA have been implicated in H. pylori cell shape (Specht et al., 2011; Sycuro et al., 2010; Waidner et al., 2009), only loss of CcmA, and not individual Ccrps, results in a drastic cell shape defect in our strain background (Yang et al., 2019); ΔccmA cells are nearly straight. To verify CcmA’s status as a cytoskeletal filament, we tested its ability to form higher-order structures in vitro. Negative staining of recombinant wild-type CcmA purified from E. coli revealed filaments of varying length, long helical bundles of filaments, and lattice structures (Figure 8A–B and Figure 8—figure supplement 1A). Fourier transform analysis of the lattice structures revealed a filament spacing of 5.5 nm (Figure 8—figure supplement 2), similar to that previously observed for C. crescentus BacA lattices (5.6 nm) (Vasa et al., 2015). While BacA forms orthogonal lattices, the CcmA lattices are skewed (acute angle = 71.5°; obtuse angle = 106.2°).

Figure 8 with 2 supplements see all
Amino acid substitution mutations in CcmA cause altered polymerization in vitro and alter cell shape in vivo.

(A–D) Negatively stained TEM images of purified CcmA. Scale bars = 100 nm, with representative images from one of three biological replicates. Wild-type CcmA lattices (A) (blue arrows) and helical bundles (B) (pink arrows), which are comprised of individual filaments (lime green arrows). (C) The I55A variant does not form ordered structures in vitro. (D) CcmAL110S filament bundles (pink arrows) and individual filaments (lime green arrows). (E) Immunoblot detection of CcmA expression (top) in H. pylori lysates using Cag3 as loading control (bottom); representative of four experiments. (F) Scatterplot displaying axis length (x-axis) and side curvature (y-axis) of wild-type (gold), ∆ccmA (gray), ccmAI55A (red), and ccmAL110S (dark red) strains. Data are representative of two biological replicates. Wild-type, n = 346; ∆ccmA, n = 279; ccmAI55A, n = 328; and ccmAL110S, n = 303.

To begin to assess the importance of higher-order structures and localization for CcmA cell shape functions, we constructed two point mutant variant proteins, located in the predicted hydrophobic core of the protein (I55A and L110S) (Shi et al., 2015). Homologous residues (75 and 130, respectively) were shown to be important for polar localization of the bactofilin BacA in C. crescentus (Vasa et al., 2015). While both proteins could be expressed and purified from E. coli (Figure 8—figure supplement 1D), the recombinant proteins either fail to form any higher order structures under any buffer condition tested (I55A; Figure 8C) or form no lattice structures and many individual filaments in addition to bundles that are straighter, narrower, and shorter than those of wild-type CcmA in vitro (L110S; Figure 8D and Figure 8—figure supplement 1B). When expressed as the sole copy of ccmA in H. pylori, both mutant proteins could be detected in whole cell extracts (Figure 8E). The I55A variant showed lower steady-state protein levels than wild-type, while the L110S variant consistently showed higher steady-state protein levels than wild-type. In both cases, the mutant strains displayed a morphology indistinguishable from a ccmA null strain (Figure 8F and Figure 8—figure supplement 1C), suggesting that formation of higher-order structures by CcmA may be necessary for cell shape-determining functions.

CcmA localization to positive curvature correlates with cell wall synthesis, CcmA polymerization, and helical cell shape

To determine the subcellular localization of CcmA, we performed immunofluorescence of HJH1 cells expressing a 2X-FLAG epitope tag at the native locus under endogenous control as the sole copy of CcmA (Figure 9A,C and D and Figure 9—video 1). As shown previously (Blair et al., 2018), helical morphology is retained upon addition of the 2X-FLAG tag to the wild-type protein. Wild-type CcmA was observed at the cell boundary as puncta and short arcs and was largely absent from the center of the cell, indicating an association with the cell membrane (Figure 9D and Figure 9—video 1). Puncta were in some cases present as lines of dots roughly parallel to the helical (long) axis of the cell, but were also found distributed along the cell surface. Immunofluorescence was also performed on cells expressing wild-type or polymerization defective CcmA (CcmAI55A and CcmAL110S) using antisera raised against H. pylori CcmA (Figure 9B,E–J and Figure 9—video 1). Immunostaining with CcmA preimmune serum showed background signal in the interior of wild-type and mutant cells (Figure 9—figure supplement 1). In contrast to cells expressing the wild-type version of CcmA, the mutant CcmA proteins localized as puncta at the center with minimal signal at the cell boundary (Figure 9G–J).

Figure 9 with 2 supplements see all
Wild-type CcmA appears as short foci on the side of the cell, but CcmA mutants I55A and L110S appear as foci in the interior of the cell.

3D SIM imaging of CcmA-FLAG cells immunostained with M2 anti-FLAG (A, C, D, yellow) or wild-type or CcmA amino acid substitution mutant cells immunostained with anti-CcmA (B, E–J, yellow); cells counterstained with fluorescent WGA (blue). (A) Color merged maximum projection of CcmA-FLAG immunostained with anti-FLAG and counterstained with fluorescent WGA. (B) Color merged field of view of wild-type cells immunostained with anti-CcmA and counterstained with fluorescent WGA. (C) Top-down (left) and 90-degree rotation (right) 3D views of three individual CcmA-FLAG cells. Top: color merge; middle: anti-FLAG; bottom: fluorescent WGA. (D) Color merged z-stack views of the three CcmA-FLAG cells in (C). (left to right = top to bottom of the cell). Numbering indicates matching cells. (E, F) Color merged field of view of I55A or L110S CcmA, respectively, immunostained with anti-CcmA and counterstained with fluorescent WGA. Top-down (left) and 90-degree rotation (right) 3D views of three individual I55A (G) or L110S (I) cells. (H, J) Color merged z-stack views of the three I55A cells in (G) or L110S cells in (I), respectively (Left to right = top to bottom of the cell). Scale bar = 0.5 µm. The representative images are selected from one of three biological replicates.

To determine if wild-type CcmA localization corresponds to the peak of higher relative PG synthesis at the major axis area, we performed curvature enrichment analysis of CcmA-2X-FLAG immunofluorescence images of non-dividing cells. CcmA was depleted at the poles (Figure 10—figure supplement 1, gold). With or without the poles, we saw a marked preference for the positive helical axis area (Figure 10 and Figure 10—figure supplement 1, red line and shaded box) that overlapped with the positive curvature enrichment peaks of MurNAc-alk and D-Ala-alk (Figure 10). The wild-type (no FLAG) negative control was 28.9% of the CcmA-FLAG signal (Figure 10—figure supplement 2B). While the negative control showed a small peak at 5 µm−2, the magnitude of the CcmA-FLAG peak was far greater (Figure 10A and Figure 10—figure supplement 1). Biological replicates are shown in Figure 10—figure supplement 2A. We also performed curvature enrichment analysis on cells expressing wild-type, I55A, and L110S CcmA immunostained with anti-CcmA. Wild-type had a similar major axis area peak as CcmA-2X-FLAG (Figure 10—figure supplement 3A, gold), with a lower magnitude due to a lower signal to noise ratio and an enrichment of background (preimmune) staining at negative Gaussian curvature (Figure 10—figure supplement 3A, dotted gray). Preimmune signal was 33.0% of the anti-CcmA signal in wild-type (Figure 10—figure supplement 3B). There was no distinguishable curvature preference for I55A or L110S CcmA compared to preimmune serum (Figure 10—figure supplement 3A, red and dark red vs. dotted light pink and dotted mauve, respectively), indicating that these proteins are unable to localize preferentially to positive Gaussian surface curvature. Preimmune signal was 50.6% and 26.7% of the anti-CcmA signal in I55A and L110S, respectively (Figure 10—figure supplement 3B).

Figure 10 with 3 supplements see all
CcmA curvature preference correlates with the peak of new PG incorporation at the major axis area and MreB curvature preference correlates with new PG enrichment at negative Gaussian curvature.

Overlay of sidewall only surface Gaussian curvature enrichment of relative concentration (y-axis) vs. Gaussian curvature (x-axis) from a population of computational cell surface reconstructions with poles excluded of CcmA-FLAG (gold), no-FLAG control (gray), MreB (pink, from Figure 7F), MurNAc-alk (green, from Figure 6C), and D-Ala-alk (blue, from Figure 6C). The represented data are pooled from three biological replicates. Blue and red vertical lines and shaded regions indicate the average ±1 standard deviation Gaussian curvature at the minor and major helical axis, respectively.

To ascertain the impact of deleting ccmA on MreB localization and cell wall synthesis patterning, we performed immunostaining for MreB and 18 min MurNAc-alk and D-Ala-alk pulse labeling on ΔccmA cells (JTH6, amgK murU ΔccmA, Figure 11A and B and Figure 11—figure supplement 1A and B; dark pink, dark green, and dark blue, respectively). In ΔccmA cells, MreB is present as small foci (Figure 11—figure supplement 2 and Figure 11—video 1). New cell wall labeling with MurNAc-alk is present as dispersed sidewall labeling with some subtle circumferential banding, while labeling with D-Ala-alk is present as clear circumferential bands along the length of the sidewall (Figure 11—figure supplement 3 and Figure 11—video 1). MreB curvature preference appears largely similar in both wild-type (HJH1, amgK murU, light pink) and ΔccmA with poles excluded (JTH6, amgK murU ΔccmA, dark pink) (Figure 11A). When poles are included in the analysis, MreB curvature preference differs more between wild-type and ΔccmA, though the general pattern of enrichment at negative Gaussian curvature remains (Figure 11—figure supplement 1A). In contrast, MurNAc-alk and D-Ala-alk patterning change with loss of CcmA; there is greater relative enrichment at low magnitude negative Gaussian curvature in ΔccmA cells (dark green and dark blue) compared to wild-type cells (light green and light blue). Additionally, in ΔccmA cells the enrichment at positive Gaussian curvature is both less pronounced and shifted to lower Gaussian curvature than that of wild-type (Figure 11B and Figure 11—figure supplement 1B). There is a small peak for MreB at approximately 3 µm−2, however interpretation of the MreB peak is complicated by the presence of a peak at the same curvature range for the preimmune signal. For ΔccmA, mock signal was 2.8% of the D-Ala-alk signal, 0.6% of the MurNAc-alk signal, and preimmune signal was 34.6% of anti-MreB signal (Figure 11—figure supplement 1C, dotted and solid dark blue and dotted and solid dark pink, respectively). These data suggest that proper localization of CcmA to the major helical axis may be required for promoting extra cell wall synthesis at the major axis area and patterning helical cell shape.

Figure 11 with 4 supplements see all
MreB and CcmA contribute to cell wall synthesis patterning.

(A, B) Sidewall only Gaussian curvature enrichment of relative concentration (y-axis) vs. Gaussian curvature (x-axis) from a population of computational cell surface reconstructions of HJH1 (amgK murU) and JTH6 (amgK murU ΔccmA) cells immunostained with (A) anti-MreB (HJH1, light pink; JTH6, dark pink) or preimmune serum (HJH1, light gray; JTH6, dark gray) or (B) 18 min MurNAc-alk (HJH1, light green; JTH6, dark green) or D-Ala-alk (HJH1, light blue; JTH6, dark blue) pulse-labeled or mock-labeled (HJH1, light gray; JTH6, dark gray) cells. 90% bootstrap confidence intervals are displayed as a shaded region about each line. The represented data are pooled from three biological replicates. (C) Model of the contribution of synthesis patterning to rod and helical shape maintenance. Dots indicate different densities of cell wall synthesis that can decrease or propagate non-zero Gaussian curvature. Colored shading indicates local regions of positive (red) and negative (blue) Gaussian curvature.

Discussion

Bacterial cell shape is driven by patterning the cell wall. Maintenance of a cylindrical rod form in a variety of bacteria relies on the action of the actin-like protein MreB, which helps to pattern PG synthesis along the sidewall (Typas et al., 2012; Zhao et al., 2017). Detailed analysis of MreB localization in the Gram-negative straight-rod E. coli indicates that centerline straightness and diameter uniformity rely on MreB curvature enrichment (Bratton et al., 2018; Ursell et al., 2014), which may result from circumferential motion about the cell (Wong et al., 2019). One working model is that MreB localization and cell wall synthesis are enhanced at cell wall dimples (negative Gaussian curvature), cylindrical regions (zero Gaussian curvature), and limited at cell wall bulges (positive Gaussian curvature). This pattern minimizes local curvature as growth progresses (Figure 11C, left). While such a growth pattern is at odds with maintaining areas of negative and positive Gaussian curvature required for curved- and helical-rod shapes, MreB is present in many bacteria with these shapes. To be able to maintain curvature in the presence of MreB, the curved-rod shaped Gram-negative Proteobacteria Caulobacter crescentus and Vibrio cholerae appear to limit relative levels of PG synthesis at negative curvatures through the action of long, cell-spanning cytoskeletal filaments (CreS and CrvA) that preferentially localize to the minor axis (negative Gaussian curvature) and enable cells to increase relative synthesis rates on the opposite side of the wall (positive Gaussian curvature) (Bartlett et al., 2017; Cabeen et al., 2009). We propose that the helical Proteobacterium H. pylori uses different mechanisms than C. crescentus and V. cholerae to maintain the even higher levels of negative and positive Gaussian curvature required for its helical cell shape; H. pylori leverages the bactofilin CcmA, which localizes preferentially to the major helical axis area, to promote synthesis at positive Gaussian curvatures on the sidewall and supplements the MreB-associated enhanced synthesis that is enriched at negative Gaussian curvatures (the minor helical axis) (Figure 11C, right). Adding the contribution of CcmA to the PG synthesis patterning allows H. pylori to maintain curvatures in the presence of MreB-associated PG synthesis.

To probe cell wall synthesis patterns in H. pylori, we used distinct metabolic probes to label the sugar (MurNAc-alk) and peptide (D-Ala-alk) portions of the polymer. While both probes indicate enhanced synthesis at the major and minor helical axes relative to the rest of the sidewall, there were considerable differences in enrichment peak magnitudes between the MurNAc-alk and D-Ala-alk probes. Modified D-alanine is thought to be incorporated into the cell wall through the action of synthesis-associated D-D-transpeptidases and cell wall-modifying L-D-transpeptidases, potentially complicating interpretation of this label. H. pylori does not have any known functional L-D-transpeptidases and no detectable 3–3 crosslinks, a hallmark of L-D-transpeptidase activity (Costa et al., 1999; Sycuro et al., 2010). Thus, signal from D-Ala-alk likely reports on D-D-transpeptidase activity. It is possible that D-D-transpeptidation may also occur separately from synthesis to promote cell wall remodeling or that the rates of synthesis-associated transpeptidation activity may vary on different sides of the cell. We only observed D-Ala-alk incorporation at the penta position (Figure 4D and Figure 4—figure supplement 4). H. pylori has a pentapeptide-rich cell wall and it is unclear if H pylori actively regulates pentapeptide trimming. However, the cell shape determining protein Csd3/HdpA has been shown to have weak pentapeptide carboxypeptidase activity in vitro (Bonis et al., 2010). Pentapeptides can also be trimmed via transpeptidase-mediated hydrolysis (Ghuysen, 1991). Curvature-biased trimming by either mechanism could also contribute to the difference between the D-Ala-alk and MurNAc-alk curvature enrichment profiles. The MurNAc probes have none of these complications as they are embedded in the glycan.

We provide the first example of MreB curvature enrichment analysis in a curved- or helical-rod bacterium and show that enrichment at negative Gaussian curvature is retained, even across the broad range of curvatures represented on the H. pylori sidewall. While there has been a report of MreB being non-essential in H. pylori (Waidner et al., 2009), the mutated strains could not be revived from frozen stocks when requested. In our strain, we could only knock out mreB if we first supplied the cells with a second copy of mreB at separate locus, indicating that MreB is functional and important. We propose that MreB promotes the peak of PG synthesis we observed at negative Gaussian curvature given its preference for this curvature in H. pylori and its role in localizing PG synthesis activity in other organisms. To enable maintenance of high sidewall curvature in the presence of the MreB-driven straight-rod cell growth pattern, we suggest that H. pylori augments the default rod pattern by means of enhanced growth at the major axis area that is independent of MreB (Figure 11C).

A major outstanding question is how H. pylori enhances PG synthesis activity at the major axis area. Our 3D analysis establishes that the average Gaussian curvature along the major axis is distinct from that along the minor axis (5 vs. −11 µm−2, respectively) and that the major axis is on average 70% longer than the minor axis in the strain used here. Cytoskeletal elements can form higher-order structures that reach a sufficient size scale to be able to sense surface curvature, providing a potential mechanism for targeting synthesis to a specific range of positive Gaussian curvature. The bactofilin CcmA is the only non-essential cytoskeletal protein we have identified in our strain background that makes an indispensable and non-redundant contribution to helical shape maintenance. In contrast to the cell spanning filaments CreS in C. crescentus and CrvA in V. cholerae, which reside at the minor axis, we show that CcmA is present in cells as numerous puncta that have a preference for the major axis area. We propose that CcmA acts to enhance synthesis on its preferred cell face by promoting PG synthesis locally (at positive Gaussian curvature). In support of this hypothesis, the bactofilins BacA and BacB in C. crescentus recruit the PG synthase PBPC to assist in stalk elongation, indicating that they help recruit PG synthesis (Kühn et al., 2010). Additionally, our group recently showed that CcmA co-purifies with Csd5 and MurF, an enzyme involved in PG precursor synthesis (Blair et al., 2018), and separately that both CcmA and MurF are within the top 20 mass spec hits of a Csd7 immunoprecipitation (Yang et al., 2019). Furthermore, we demonstrate that in the absence of CcmA, similarly to in wild-type, MreB is still enriched at negative Gaussian curvature, but that MurNAc-alk and D-Ala-alk synthesis patterning shift to more closely resemble the MreB curvature enrichment profile. In ΔccmA, synthesis at negative Gaussian curvature makes a much more significant contribution to the overall synthesis pattern than does synthesis at positive curvature, as seen by the greater relative concentration at Gaussian curvature values below 0 µm−2. The MurNAc-alk and D-Ala-alk signals do show a subtle peak at low magnitude positive Gaussian curvature (approximately 3 µm−2), however the peak is far less prominent (greatly reduced peak to trough distance). Given that there is still some curvature in ΔccmA cells, it is not necessarily surprising that there is still some enrichment at positive Gaussian curvature. CcmA is one of a suite of proteins required for helical cell shape maintenance; it is possible that other cell shape proteins can influence PG synthesis to promote some limited curvature in the absence of CcmA, consistent with multiple complementary mechanisms being required for helical shape maintenance.

It is possible that CcmA may also help promote localized crosslink trimming, as loss of CcmA results in an increased degree of crosslinking in the sacculus (Sycuro et al., 2010). Crosslink trimming may help promote synthesis but could also play some other role in helical shape maintenance. CcmA dynamics could also influence its ability to promote cell shape. While CcmA does not require a nucleotide cofactor for polymerization, it may be mobile through coupling with the motion of PG synthesis machinery. In other organisms, MreB filaments travel in a roughly circumferential path around the cell and we expect MreB to behave similarly in H. pylori.

Loss of CcmA results in cells with highly diminished cell curvature and without significant helical twist. Beyond helping promote curvature by localized PG synthesis, it is possible that CcmA also helps generate twist. We observed helical bundles of filaments in vitro by TEM. These bundles are far longer than the foci we see by immunofluorescence, but foci within the cell may consist of short twisted filament bundles and/or skewed lattices. While it remains unclear how filament or lattice twist would be coupled to cell wall twist, the bactofilin LbbD modulates helical pitch in the spirochete Leptospira biflexa (Jackson et al., 2018). Both CcmA point mutant variants show altered or no polymerized structures under a variety of buffer conditions in vitro and fail to localize to the cell envelope in vivo. It is still unclear which structures are relevant and if altering higher-order structures abolishes CcmA function by disrupting protein-protein interactions and/or CcmA localization.

Overall, our results are consistent with a model in which MreB-patterned straight-rod shape is the default pattern for H. pylori cells and helical shape is facilitated by adding major axis area PG synthesis via CcmA to augment straight-rod cell wall patterning. The enrichment of new cell wall synthesis to both negative Gaussian curvature, as expected for straight-rod shape, and to the major axis area indicates one mechanism for achieving helical shape, but it is not apparent how this growth pattern on its own could be sufficient for helical shape maintenance. The lower relative amount of synthesis at Gaussian curvatures corresponding to the sides of the cell body in comparison to the major and minor axis areas is both unexpected and counterintuitive; it suggests additional mechanisms may be required to maintain helical shape. Indeed, the noted difference between enrichment of D-Ala-alk and MurNAc-alk suggests that spatially-coordinated cell wall modification occurs. Curvature-dependent differences in crosslinking could alter cell wall mechanical properties and PG density; perhaps the PG at the side of the cell is less dense, thus requiring less PG synthesis during growth. Furthermore, our labeling strategy allowed us to determine the curvature bias of new PG insertion, but spatially-regulated turnover of old PG may also contribute to cell wall homeostasis. We also do not know if super-twisting of the cell wall occurs during growth: does PG on the major axis remain at the major axis as the cell grows?

We employed sophisticated computational tools to demonstrate that H. pylori must achieve a much broader distribution of sidewall Gaussian curvature than the curved-rod bacteria C. crescentus and V. cholerae and that it uses distinct mechanisms to achieve these curvatures. In elucidating the spatial patterning of new cell wall synthesis, we have revealed one of the downstream mechanisms of H. pylori’s cell shape-determining program.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
AntibodyMonoclonal ANTI-FLAG M2 antibody produced in mouseSigmaCat# F1804,
RRID:AB_262044
IF(1:200)
AntibodyGoat anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 488InvitrogenCat# A-11029, RRID:AB_2534088IF(1:200)
AntibodyGoat anti-Rabbit IgG (H+L)
Cross-Adsorbed Secondary Antibody, Alexa Fluor 488
InvitrogenCat#: A-11008; RRID: AB_143165IF(1:200)
AntibodyPolyclonal rabbit αCcmA(Blair et al., 2018)IF (1:200); WB (1:10,000)
AntibodyPolyclonal rabbit αMreB (H. pylori)(Nakano et al., 2012)IF (1:500); WB (1:25,000)
Commercial assay, kitClick-iT Cell
Reaction Buffer Kit
InvitrogenCat# C10269
Chemical compound, drugAlexa Fluor 555 Azide,
Triethylammonium Salt
InvitrogenCat# A20012
Chemical compound, drugD-Ala-alk ((R)−2-Amino-4-pentynoic acid)BoaopharmaCat# B60090
Chemical compound, drugMurNAc-alk(Liang et al., 2017)
Chemical compound, drugMurNAcSigmaCat# A3007
Chemical compound, drugWheat Germ Agglutinin, Alexa Fluor 488 ConjugateInvitrogenCat# W11261
Chemical compound, drugWheat Germ Agglutinin, Alexa Fluor 555 ConjugateInvitrogenCat# W32464
OtherProLong Diamond Antifade MountantInvitrogenP36961

Cultures and growth

Request a detailed protocol

H. pylori (LSH100 and derivatives, Table 2) was grown on horse blood (HB) agar plates (Humbert and Salama, 2008) incubated at 37°C under micro-aerobic conditions in either 90% air, 10% CO2 (dual-gas) or in 10% CO2, 10% O2, 80% N2 (tri-gas). For resistance marker selection, HB agar plates were supplemented with 15 µg/ml chloramphenicol, 25 µg/ml kanamycin, or 30 mg/ml sucrose, as appropriate. Liquid H. pylori cultures were grown shaking in Brucella broth (BD Biosciences, Sparks, MD) supplemented with 10% heat-inactivated fetal bovine serum (Gemini Bio-Products, West Sacramento, CA) (BB10) at 37°C in tri-gas conditions. For plasmid selection and maintenance, E. coli cultures were grown in lysogeny broth (LB) or agar supplemented with 100 µg/ml ampicillin or as described at 37°C.

Table 2
Strains used in this study.
StrainGenotype/descriptionConstructionReference
LSH100Wild-type: mouse-adapted G27 derivative-Lowenthal et al., 2009
LSH141 (Δcsd2)LSH100 csd2::cat-Sycuro et al., 2010
TSH17 (Δcsd6)LSH100 csd6::cat-Sycuro et al., 2013
LSH108LSH100 rdxA::aphA3sacB-Sycuro et al., 2010
HMJ_Ec_pLC292-KUE. coli TOP10 pLC292-KUTransformation of TOP10 with pLC292-KUThis study
HJH1LSH100 rdxA::amgKmurUIntegration of pLC292-KU into LSH108This study
IM4LSH100 mcGee:mreBIntegration of pIM04into LSH100This study
JTH3LSH100 ccmA:2X-FLAG:aphA3-Blair et al., 2018
JTH5LSH100 ccmA:2X-FLAG:aphA3 rdxA::amgKmurUNatural transformation of HJH1 with JTH3 genomic DNAThis study
KGH10NSH57 ccmA::catsacB-Sycuro et al., 2010
LSH117LSH100 ccmA::catsacBNatural transformation of LSH100 with KGH10 genomic DNAThis study
SSH1LSH100 ccmAI55ANatural transformation with ccmA I55A PCR productThis study
SSH2LSH100 ccmAL110SNatural transformation with ccmA L110S PCR productThis study
LSH142 (ΔccmA)LSH100 ccmA::cat-Sycuro et al., 2010
JTH6LSH100 rdxA::amgKmurU ccmA::catNatural transformation of HJH1 with LSH142 genomic DNAThis study

AmgK MurU strain constuction

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AmgK and MurU-encoding sequences were PCR amplified from expression plasmid pBBR-KU (Liang et al., 2017) using primers AmgK_BamHI_F and MurU_HindIII_R (Table 3). The amgK murU amplification product and plasmid pLC292 (Terry et al., 2005) were digested with BamHI-HF and HindIII-HF (New England BioLabs, Ipswich, MA) at 37°C for 1 hr and cleaned up with the QIAquick PCR Purification Kit (Qiagen, Valencia, CA) according to manufacturer instructions. Insert and vector were then ligated with T4 ligase (New England BioLabs) for 10 min at room temperature, inactivated at 65°C for 20 min, and stored at −20°C. 1 µl of the ligation mixture was transformed into OneShot TOP10 competent cells (Invitrogen, Carlsbad, CA) according to manufacturer instructions. Cells were plated on LB-ampicillin plates and incubated overnight at 37°C. Colonies were screened by colony PCR using primers AmgK_BamHI_F and MurU_HindIII_R. Plasmid pLC292-KU was purified from the resulting clone, HMJ_Ec_pLC292-KU, using the QIAprep Spin Miniprep Kit (Qiagen) according to manufacturer instructions. Recipient H. pylori containing a aphA3sacB cassette at the rdxA locus (LSH108 Sycuro et al., 2010) were transformed with the purified plasmid. Transformants were selected on sucrose plates and kanamycin sensitivity was verified. Genomic DNA was purified using the Wizard Genomic DNA Purification Kit (Promega, Fitchburg, WI) and insertion of amgK murU at rdxA was verified by PCR amplifying and sequencing the locus using primers RdxA_F1P1 and RdxA_dnstm_RP2. The resulting confirmed strain was named HJH1. JTH6, ΔccmA with amgK murU was generated by natural transformation of HJH1 with genomic DNA from LSH142 and selection on chloramphenicol plates. Deletion of ccmA was confirmed by PCR.

Table 3
Primers used in this study.
Primer nameSequence (5’ to 3’)
AmgK_BamHI_FGATAGGATCCTGACCCGCTTGACGGCTA
MurU_HindIII_RGTATAAGCTTTCAGGCGCGCTCGC
RdxA_F1P1CAATTGCGTTATCCCAGC
RdxA_dnstm_RP2AAGGTCGCTTGCTCAATC
O#9 ProMreB (KpnI_5’)TATTGGTACCCGCTTGATGTATTCATCAAAG
O#10 ProMreB_RGATTAATTTGCTAAAAATCATAAAATAAACTCCTTGTTTTG
O#11 ProMreB_FCAAAACAAGGAGTTTATTTTATGATTTTTAGCAAATTAATC
O#12 ProMreB (XhoI_3’)TATTCTCGAGTTATTCACTAAAACCCACAC
O#36 pMcGee-Insert-FCTGCCTCCTCATCCTCTTCATCCTC
O#45 MreBC-seq-F2GCACCTATTTTGGGGTTTGAAACC
O#47 MreB-seq-F2CATTGAGCGCTGGTTTTAAGGCGGTC
O#28 MreBseq-F3CGATCGTGTTAGTCAAAGGGCAGGGC
O#37 pMcGee-Insert-RGGTGTACAAACATTTAAAGGTAGAG
O#68 McGee-1FCATTTCCCCGAAAAGTGCCACGAGCTCGAAGGAGTATTGATGAAAAAGG
O#69 McGee-1RCTAGAGCGGCCCCACCGCGGCCATCATTAACATCATTATCG
O#70 MCS-kan-FCTCGAGGGGGGGCCCGGTACCCACAGAATTACTCTATGAAGC
O#71 MCS-kan-RCCATTCTAGGCACTTATCCCCTAAAACAATTCATCCAGTAA
O#72 McGee-2FTTACTGGATGAATTGTTTTAGGGGATAAGTGCCTAGAATGG
O#73 McGee-2RCGGATATTATCGTGAGATCGCTGCAGACTGGGGGGAAACTCATGGG
O#74 McGee-R6K-FCCCATGAGTTTCCCCCCAGTCTGCAGCGATCTCACGATAATATCCG
O#75 McGee-R6K-RGTAACTGTCAGACCAAGTTTACTGCGGCCGCGCAAGATCCGGCCACGATGCG
O#76 R6K-amp-FCGCATCGTGGCCGGATCTTGCGCGGCCGCAGTAAACTTGGTCTGACAGTTAC
O#77 R6K-amp-RCCTTTTTCATCAATACTCCTTCGAGCTCGTGGCACTTTTCGGGGAAATG
O#78 MCS fragmentCCGCGGTGGGGCCGCTCTAGAACTAGTGGATCCCCCGGGCTGCGGAATTCGCTTATCG
O#79 McGee-MCS-FCGATAATGATGTTAATGATGGCCGCGGTGGGGCCGCTCTAG
O#80 McGee-MCS-RGCTTCATAGAGTAATTCTGTGGGTACCGGGCCCCCCCTCGAG
Csd1FGAGTCGTTACATTAATGTGCATATCT
G1480_DnStrmP2AAGGGTGCAATAACGCGCTAA
MreB_start_FATGATTTTTAGCAAATTAATCGG
MreB_cat_up_RCACTTTTCAATCTATATCCGTGCCTCCGCCAATATC
C1GATATAGATTGAAAAGTGGAT
C2TTATCAGTGCGACAAACTGGG
Cat_mreB_dn_FAGTTTGTCGCACTGATAAACTGAAATTGGCG
MreB_end_RTTATTCACTAAAACCCACACGGCTGA
FabZ_up_FGCTATCCCATGCTATTGATAGAC
Cat_mid_RGTCGATTGATGATCGTTGTAACTCC
MreB_mid_dn_FGATCAAAGCATCGTGGAATACATCC
Supp2_junc1_R_midAATTTGCTAAAAATCACTAA
MreB_upAATACCAGCAACTTTTCAAAA
Supp1_Junction1_RATTTGCTAAAAACACACGGC
CatoutCCTCCGTAAATTCCGATTTGT
McGee_187GCGAGTATTACCACAAGTTTTC
CcmA SDM mi RAGACTAGATTGGATCATTCCCTATTTATTTTCAATTTTCT
CcmA SDM mi FATAAAGAAAGGAGCATCAGATGGCAATCTTTGATAACAAT
CcmA SDM up RATTGTTATCAAAGATTGCCATCTGATGCTCCTTTCTTTAT
CcmA SDM dn FAGAAAATTGAAAATAAATAGGGAATGATCCAATCTAGTCT
CcmA SDM dn RGCTCATTTGAGTGGTGGGAT
SDM 155A FATTCTAAAAGCACGGTGGTGgcCGGACAAACCGGCTCGGTAG
SDM 155A RCTACCGAGCCGGTTTGTCCGgcCACCACCGTGCTTTTAGAAT
SDM L110S FTGGTGGAAAGGAAGGGGATTtcGATTGGGGAAACTCGCCCTA
SDM L110S RTAGGGCGAGTTTCCCCAATCgaAATCCCCTTCCTTTCCACCA

mreB merodiploid strain construction and quantitative transformation assays

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To generate the mreB merodiploid strain IM4, the promoter of the operon containing mreB and a 5’ KpnI site was amplified from LSH100 genomic DNA using primers O#9 ProMreB (KpnI_5') and O#10 ProMreB_R. The mreB coding sequence with a 3’ XhoI site was PCR amplified using primers O#11 ProMreB_F and O#12 ProMreB (Xhol_3'). These products were joined using PCR SOEing (Horton, 1995). A modified Bluescript SK vector, pDCY40, containing the RK6 origin and aphA3 flanked by two 550 bp segments of DNA from a previously characterized neutral locus (McGee locus) located between HPG27_186 and HPG27_187 (Langford et al., 2006). pDCY40 was constructed using isothermal assembly (Gibson et al., 2009) of six pieces amplified using primers O#68 McGee-1F, O#69 McGee-1, O#70 MCS-kan-F, O#71 MCS-kan-R, O#72 McGee-2, O#73 McGee-2R, O#74 McGee-R6K-F, O#75 McGee-R6K-R, O#76 R6K-amp-F, O#77 R6K-amp-R, O#78 MCS fragment, O#79 McGee-MCS-F, and O#80 McGee-MCS-R. The PCR SOEing product and pDCY40 were digested with KpnI and XhoI and ligated to generate vector pIM04DY containing the promotor-mreB fusion with flanking McGee locus sequences. pIM04DY was transformed into Chung competent DH5αλpir cells and selected on LB plates with 50 µg/ml ampicillin and 0.2% glucose. The pIM04DY insert was sequence confirmed using primers O#36 pMcGee-Insert-F, O#45 MreBC-seq-F2, O#47 MreB-seq-F2, O#28 MreBseq-F3, and O#37 pMcGee-Insert-R. Linear DNA was PCR amplified from pIM04DY using primers O#73 McGee-2R and O#68 McGee-1F. LSH100 was transformed with this PCR product and kanamycin resistant clones were verified by Sanger sequencing. IM4 was generated by back-crossing LSH100 with genomic DNA from one of these verified clones.

ccmA::CAT linear DNA was PCR amplified from LSH142 (ΔccmA) genomic DNA (Sycuro et al., 2010) using primers csd1F and G1480_DnStrmP2. mreB::CAT linear DNA was generated using previously published methods (Sycuro et al., 2010). Briefly, PCR products were amplified from LSH100 genomic DNA using primers MreB_start_F and MreB_cat_up_R for the upstream fragment and Cat_mreB_dn_F and MreB_end_R for the downstream fragment. The CAT cassette was amplified from LSH123 (Δcsd5) genomic DNA (Sycuro et al., 2012) using primers C1 and C2. These products were annealed using PCR SOEing (Horton, 1995). For transformations, LSH100 and IM4 were grown up to mid-log phase in liquid. 4.5 × 105 cells in liquid were spotted onto plates, allowed to dry, and were incubated three hours prior to transformation. Each transformation was performed in triplicate. 300 ng of either mreB::CAT or ccmA::CAT linear DNA was mixed with each cell patch. Transformations were incubated overnight and then each cell patch was resuspended in BB10, serially diluted, and spread on non-selective plates for colony counts and chloramphenicol plates for selection of transformants. Colonies were counted after six days. Plates without colonies after six days were incubated for three weeks to allow any slowly growing colonies to arise. Genomic DNA was purified from the two transformants of LSH100 (clone 1 and 2) with mreB::CAT. Sanger sequencing was performed on recombinant clone 1 and 2. For sequencing clone1, sequencing template was PCR amplified from genomic DNA using primers FabZ_up_F and Cat_mid_R and sequenced using primers Supp1_Junction1_R and MreB_up. Additional sequencing template for clone 1 was PCR amplified using primers MreB_mid_dn_F and Cat_mid_R and sequenced using primer MreB_mid_dn_F. For sequencing clone 2, template was PCR amplified from genomic DNA using primers Supp2_junc1_R_mid and MreB_up and sequenced using primers Supp2_junc1_R_mid and MreB_up. Additional sequencing template was PCR amplified using primers MreB_mid_dn_F and Cat_mid_R and sequenced using primers MreB_mid_dn_F and Cat_mid_R. Genomic DNA was purified from eight transformants per transformation of IM4 with mreB::CAT. PCR with primers Catout, MreB_up, and McGee_187 was used to determine which copy of mreB in each clone was disrupted.

ccmA point mutation strain construction 

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Strains containing CcmA amino acid substitution mutations were created based on previously published methods (Sycuro et al., 2010). Briefly, PCR products were amplified from pKB69H (I55A) or pKB72D (L110S) using primers CcmA SDM mi F and CcmA SDM mi R (Table 3). Those products were annealed using PCR SOEing (Horton, 1995) to fragments amplified from WT H. pylori flanking the CcmA locus using primers Csd1F and CcmA SDM up R (upstream fragment, 810 bp flanking) and CcmA SDM dn F and CcmA SDM dn R (downstream fragment, 540 bp flanking). PCR product was transformed into a catsacB ccmA knockout strain LSH117 (LSH100 naturally transformed with KGH10 [Sycuro et al., 2010] genomic DNA) and colonies resistant to sucrose and susceptible to chloramphenicol were validated using PCR and Sanger sequencing. Single clones of colonies containing correct mutations were used for all experiments.

Fosfomycin rescue with MurNAc

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Overnight liquid cultures of HJH1 and parent strain LSH108 grown to an optical density at 600 nm (OD600) of 0.3–0.5 OD600/ml were diluted in BB10, BB10 containing fosfomycin, or BB10 containing fosfomycin and MurNAc to yield cultures at 0.002 OD600/ml, with 50 µg/ml fosfomycin, or 50 µg/ml fosfomycin and 4 mg/ml MurNAc, as appropriate. Cultures were grown shaking in 5 ml polystyrene tubes. Samples were taken initially and after 12 hr. 10 µl of culture was diluted into 30 µl of BB10 and a 10-fold dilution series was performed from this initial dilution. 4 µl of each dilution for each experimental condition was spotted on plates and plates were incubated 5–6 days. One biological replicate is defined as beginning with a new overnight liquid culture.

Synthesis and characterization of MurNAc-alk

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MurNAc-alk was synthesized and characterized as previously described (Liang et al., 2017) and underwent multiple rounds of purification using our previously-described autopur preparatory HPLC purification strategy until no more than 5% N-hydroxysuccinimide (NHS) remained in the product as judged by H NMR, chemical shift 2.6 ppm. The final MurNAc-alk product was then solubilized in DMSO or water (200 mg/ml) for subsequent bacterial PG labeling experiments.

PG preps and analysis for D-Ala-alk and MurNAc-alk

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330 ml of liquid cultures were grown for six doublings to 1 OD600/ml with 100 µg/ml D-alanine-alk ((R)−2-Amino-4-pentynoic acid, Boaopharma, Woburn, MA), 62.5 µg/ml MurNAc-alk, or no additions. Cells were harvested and sacculi were purified as previously described (Blair et al., 2018). Briefly, cells were harvested by centrifugation at 4°C, resuspended in PBS, and added dropwise to boiling 8% SDS. SDS was then removed by ultracentrifugation and washing. Then sacculi were resuspended in 900 µl of 10 mM Tris HCl with 10 mM NaCl pH 7.0 and 100 µl of 3.2 M imidazole pH 7.0 and incubated with 15 µl α-amylase (10 mg/ml) (Sigma, St. Louis, MA) for 2 hr at 37°C and 20 µl Pronase E (10 mg/ml) (Fisher Scientific, Pittsburgh, PA) for 1 hr at 60°C. 500 µl of 8% SDS was added and samples were boiled for 15 min. SDS was again removed by ultracentrifugation and washes with water. The purified PG was suspended in 20 mM sodium phosphate pH 4.8 (D-Ala-alk samples) or 20 mM ammonium formate pH 4.8 (MurNAc-alk samples) and incubated overnight with 10 μg of cellosyl (kind gift from Hoechst, Frankfurt am Main, Germany) at 37°C on a Thermomixer at 900 rpm. Following this incubation, the samples were placed in a dry heat block at 100°C for 10 min and centrifuged at room temperature for 15 min at 16,000 × g. The supernatant was retrieved. D-Ala-alk labeled digests were reduced with sodium borohydride (Merck KGaA, Darmstadt, Germany) and separated by RP-HPLC, peaks collected and analyzed using offline electrospray mass spectrometry as previously described (Bui et al., 2009).

MurNAc-alk labeled digests (non-reduced) were analyzed via injection onto a capillary (0.5 × 150 mm) ACE Ultracore 2.5 super C18 column (Hichrom, Lutterworth, UK). The LC-MS instrument configuration comprised a NanoAcquity HPLC system (Waters, Milford, MA) and QTOF mass spectrometer (Impact II, Bruker, Billerica, MA). Buffer A was 0.1% formic acid (VWR, Lutterworth, UK) in water (VWR). Buffer B was 0.1% formic acid in acetonitrile (VWR). RP-HPLC conditions were as follows: 0% buffer B for 3 min, 1.5% B at 20 min, 3.0% B at 35 min, 15% B at 45 min, 45% B at 50 min, followed by 2 min at 85% B and finally 15 min re-equilibration at 0% B. The flow rate was 0.02 ml/min and the capillary column temperature was set at 35°C.

MS data was collected in positive ion mode, 50–2000 m/z, with capillary voltage and temperature settings of 3200 V and 150°C respectively, together with a drying gas flow of 5 L/min and nebulizer pressure of 0.6 Bar. The resulting MS spectral data was analyzed using Compass DataAnalysis software (Bruker).

18 min pulses with D-Ala-alk and MurNAc-alk

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400 µl of HJH1 overnight liquid cultures in BB10 grown to 0.3–0.5 OD600/ml was added to a 5 ml polystyrene round bottom tube and equilibrated in the 37°C Trigas incubator for 15 min before addition of the metabolic probe. 8 µl of a 200 mg/ml MurNAc-alk stock in DMSO or water (final concentration = 4 mg/ml) or 4 µl of a 100 mM stock of D-Ala-alk ((R)−2-Amino-4-pentynoic acid, Boaopharma) in water was added to the culture. The culture was incubated for 18 min and growth was arrested by adding 4 µl of 10% sodium azide and placing cultures on ice for 5 min. Cells were transferred to a 1.5 ml microcentrifuge tube, pelleted in a microcentrifuge for 5 min at 5000 rpm, and resuspended in 1 ml Brucella broth. Paraformaldehyde was added to a final concentration of 4%. Cells were fixed at room temperature for 45 min, pelleted, and resuspended in 70% ethanol. Cells were permeabilized on ice for 30 min, pelleted, and resuspended in PBS. Cell suspension density was normalized between samples using a hemocytometer and cells were spun onto clean glass coverslips at 500 rpm for 5 min in a Hettich Rotana 460R swinging bucket centrifuge. Click chemistry was performed on coverslips using the Click-iT Cell Reaction Buffer Kit (Invitrogen) according to manufacturer instructions (without BSA washes) with 8 µg/ml Alexa Fluor 555 Azide (Invitrogen). Coverslips were washed two times with 0.05% Tween-20 in PBS (PBST) for 10 min each and were then stained with 30 µg/ml WGA-Alexa Fluor 488 (Invitrogen) in PBS for 30 min at room temperature. Coverslips were washed an additional four times in PBST and mounted on slides with Prolong Diamond antifade (Invitrogen). Slides were cured for a week before imaging. One biological replicate is defined as beginning with a new overnight liquid culture.

Immunofluorescence (CcmA-FLAG, CcmA, MreB)

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Overnight liquid cultures in BB10 grown to 0.3–0.5 OD600/ml were fixed at room temperature for 45 min with 4% paraformaldehyde. Cells were pelleted in a TOMY TX-160 micro centrifuge for 5 min at 5000 rpm and resuspended in 0.1% Triton X-100 in PBS for one hour at room temperature to permeabilize the cells. Cells were then pelleted in an Eppendorf microfuge at 2400 rpm for 10 min and resuspended in PBS. Cell suspension density was normalized using a hemocytometer and cells were spun onto clean glass coverslips at 500 rpm for 5 min in a Hettich Rotana 460R swinging bucket centrifuge. Coverslips were stained with 30 µg/ml WGA-Alexa Fluor 555 (Invitrogen) in PBS for 30 min at room temperature, washed four times with 0.05% Tween-20 in PBS (PBST) for 10 min each, blocked for two hours with 5% goat serum (Sigma) in PBST at room temperature, and then incubated overnight at 4°C in primary antibody in 5% goat serum PBST. Mouse anti-FLAG M2 (Sigma, RRID:AB_262044), rabbit anti-CcmA (Blair et al., 2018), and CcmA preimmune serum were used at a 1:200 dilution. Rabbit anti-MreB and MreB preimmune serum (a gift from Dr. Hong Wu and Dr. Kouichi Sano Nakano et al., 2012) were used at a 1:500 dilution. After primary antibody incubation, coverslips were washed four times in PBST and incubated with 1:200 Alexa Fluor 488 anti-mouse (A-11029, Invitrogen, RRID:AB_2534088) or 1:200 Alexa Fluor 488 anti-rabbit (A-11008, Invitrogen, RRID:AB_143165) in PBST for 45 min at room temperature. After secondary antibody incubation, coverslips were washed four times in PBST and mounted on slides with Prolong Diamond antifade (Invitrogen). Slides were cured for a week before imaging. For CcmA-FLAG immunofluorescence, strain JTH5 was used. JTH5 was generated by natural transformation of HJH1 with genomic DNA from JTH3 (Blair et al., 2018) and selection on kanamycin blood plates. HJH1 was used as the corresponding no-FLAG control, as well as for the anti-MreB and MreB preimmune immunofluorescence. Wild-type LSH100 (Lowenthal et al., 2009) was used for anti-CcmA and CcmA preimmune immunofluorescence. One biological replicate is defined as beginning with a new overnight liquid culture.

3D structured illumination imaging

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Slides for cell surface curvature profiles for LSH100, Δcsd2, and Δcsd6 were imaged on a DeltaVision OMX V4 BLAZE 3D microscope (GE Healthcare Life Sciences, Chicago, IL) equipped with Photometrics Evolve 512 emCCD cameras and an Olympus UPlanApo 100x/1.42 oil objective with oil matched for the sample refractive index. 512 × 512 pixel images were collected with three msec exposure and 170 EMCCD gain using a 100 mW 488 nm laser with 10% transmission. Z-plane images were acquired with 125 nm spacing. The remaining SIM microscopy was performed on a DeltaVision OMX-SR equipped with PCO scientific CMOS cameras, 488 nm and 568 nm lasers, and an Olympus 60x/1.42 U PLAN APO oil objective with oil matched for the sample refractive index. 512 × 512 pixel Z-plane images with 125 nm spacing and 3 µm thickness were collected. For HJH1 D-Ala-alk samples, images were collected with 5% 488 and 15% 568 laser power for 20 msec and 100 msec exposures, respectively. For JTH6 D-Ala-alk samples, images were collected with 5% 488 and 30% 568 laser power for 20 msec and 100 msec exposures, respectively. For MurNAc-alk samples, images were collected with 10% 488 and 15% or 2% 568 laser power for 2 msec and 80 msec exposures, respectively. For anti-FLAG immunostained samples, images were collected with 10% 488 and 10% 568 laser power and 40 msec and 25 msec exposure, respectively. For HJH anti-MreB immunostained samples, images were collected with 10% 488 and 10% 568 laser power and 70 msec and 25 msec exposure, respectively. For JTH6 α-MreB immunostained samples, images were collected with 20% 488 and 20% 568 laser power, respectively, and 25 msec exposure. For anti-CcmA immunostained samples, images were collected with 15% 488 and 15% 568 laser power and 30 msec and 40 msec exposure, respectively. Images were processed using included Softworx software. Figures were generated by opening files in Fiji (Schindelin et al., 2012), adjusting brightness and contrast, and assembling in Adobe Photoshop. Intensity scaling of maximum projection and Z-slice images are equal for all samples within a set (D-Ala-alk and mock; MurNAc-alk and mock; anti-FLAG M2; anti-MreB and preimmune serum; and anti-CcmA and preimmune serum), with the exception of the I55A CcmA anti-CcmA and preimmune images, which were brightened in comparison to other anti-CcmA and preimmune images to compensate for the reduced expression of I55A CcmA. Intensity scaling is equal for I55A CcmA anti-CcmA and preimmune images.

3D reconstructions and curvature enrichment

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3D cell surfaces were generated from the 3D-SIM OMX software reconstructions using existing software (Bartlett et al., 2017; Bratton et al., 2018) with parameters optimized for the difference in imaging modality and file formats. This method minimizes the difference between the observed image and a forward convolution model of the true intensity distribution and the microscope’s transfer function. While the images generated by 3D-SIM are not precisely equal to the convolution of the true intensity distribution, we consider the observed images as if they had been generated with an effective blurring function that we parameterize as a 3D Gaussian blur. For each individual cell, the reconstruction algorithm returns the 3D shape of the cell as a collection of vertex positions {Vi} and a collection of faces defining which vertices are connected to each other. These faces and positions allow us to calculate geometric properties including the volume, surface area, local principal curvatures, etc. (Bratton et al., 2018; Rusinkiewicz, 2004). The Gaussian curvature at any point on the surface is the product of the principal curvatures and is therefore independent of the sign convention chosen for the principal curvatures. Following reconstruction, each cell surface undergoes a visual inspection quality control step. To estimate the diameter of each cell, we use the distance from each surface point to its nearest centerline point as a proxy for the local radius. The cell diameter is then the weighted average of twice the local radius, weighted by the surface area represented by each vertex.

In addition to the geometric properties of the surface, we calculate the intensity of a secondary fluorophore at the coordinates of the surface, for example D-Ala-alk, MurNAc-alk, or immunofluorescence. For each individual cell, the average surface concentration was calculated as the surface area weighted sum of the fluorescence at the surface divided by the total surface area of that cell. This normalization sets the concentration scale for the enrichment analysis; a value of one is the same concentration as if all the intensity was uniformly spread on the surface, concentrations greater than one are enriched and concentrations less than one are depleted. When considering the entire cell surface, the normalization included all surface vertices. When only considering the sidewalls of the cell, we first removed all the vertices in the polar regions. These regions were defined as all the points on the surface whose nearest centerline point was closer to the pole than 0.75 of the cell diameter (Figure 1B). Following normalization, we calculated the geometric enrichment in each individual cell by averaging the concentration across all the vertices of a particular Gaussian curvature. This enrichment profile was then averaged across the entire population of cells. We truncate the analysis to Gaussian curvatures which have sufficient representation (>4e-4). For error estimation, we report 90% confidence intervals from bootstrap analysis across cells and plot this interval, along with the mean, using cubic smoothing splines (Figure 6, lines). Each sample is the composite dataset from three biological replicates.

We approximated the total fluorescent signal from each cell including the contributions from inside the cell and surface intensities. This total signal is a good proxy for the selectivity of the labeling experiments. As a first step, the entire z-stack was summed to make a 2D projection. A thresholded, binary mask of each cell was generated using Otsu’s method on the color channel used to generate the computation cell surface reconstruction and dilated by three pixels to make sure that we captured all the intensity in the cell. The total intensity in the corresponding pixels of the other color channel were added together to calculate the total intensity in the cell. To normalize for effects of cell size, this total intensity was divided by the number of pixels in the mask, resulting in the total fluorescence signal/cell.

The MATLAB scripts used to reconstruct cell surfaces and perform the geometric enrichment analyses are publicly available under a BSD 3-clause license at https://github.com/PrincetonUniversity/shae-cellshape-public and archived at https://doi.org/10.5281/zenodo.3627045 and http://arks.princeton.edu/ark:/88435/dsp01h415pd457.

Determining helical fits of 3D centerlines

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To examine the eight helical parameters of each cell's centerline, we adapted the helical fitting algorithm from Nievergelt (1997). The first step in the routine is to estimate a right-cylindrical surface on which all the data lie. This is defined by four parameters, three of which define a vector parallel to the helical axis (Xa, Ya, Za) and the fourth is the cylinder diameter (D). The subsequent steps determine the remaining four parameters that define a point on the helix (Xo, Yo, Zo) and the helical pitch (P). The algorithm takes advantage of the speed of singular value decomposition (SVD) by framing the best fit as a linear algebra problem. The modifications that we made to the algorithm were in a preconditioning step as well as steps 2 and 3. The center of mass of the data was subtracted off from all the observations and then added back into X0, Y0, and Z0. For our real cells the two smallest singular values in step 2.3 are sometimes of similar magnitude and are both checked to see which right-singular vector is more consistent with a cylinder. The use of SVD instead of eigenvalue decomposition does not retain the right-handed convention of space forcing us to switch step 2.4 to an eigenvalue decomposition. In estimating the pitch of the helix in step 3.2, the algorithm by Nievergelt did not support helical data that covered more than one helical turn. This type of data presents a phase wrapping issue. To solve this issue, we first sorted the data by its projected position along the helical axis. We assumed that the relative phase difference between any two subsequent points was close to zero and calculated an absolute phase at each point by summing the relative phase differences along the whole curve. This then allowed us to calculate the relative slope of the helical phase. Here we again had to break from Nievergelt’s SVD approach and used simple linear regression to retain the right-handed convention of space.

For each cell that was independently reconstructed, we estimate the best fit helical parameters for the centerline. Because we do not consider the orientation and offset of the helix to be shape parameters, we do not present any statistics on them. To estimate if the best fit helix was consistent with the centerline, we calculated the root mean squared deviation (RMSD) between the observed centerline coordinates and the best fit helix. One third (402/1137) of the cells had centerlines consistent with single helix. From the one third of the population that matched a single helix, we generated synthetic helical rods with the same helical parameters as each individual cell. From these, we compared the simulated and reconstructed cells in terms of their surface area, volume, volume of the convex hull, and Euclidean distance from pole to pole. If any of the parameters from the simulated cell deviated from the measured value by more than 10%, we excluded that cell from the analysis. In the end, we were left with almost 20% (231/1137) of the wild-type cells that were consistent with our model that cell shape is close to a spherocylinder wrapped around a helical centerline.

Synthetic cells were generated using two major components, a helical centerline and a cylindrical coordinate system about that centerline. In cylindrical coordinates (R, θ, L), a cylinder with hemispherical endcaps has a simple form of a constant radius in the cylinder region and parabolic dependence in the endcaps. We then wrap the coordinate system around a helical axis by calculating the Frenet-Serret frame at each point of the helical centerline from the local tangent, normal, and their cross-product, the binormal. This wraps a fixed angular coordinate θ around the centerline, generating the helical rod surface of interest. However, these surfaces are still in a rectangular format, meaning that they are stored as three matrices {x,y,z} each as a function of the (θ, L). This surface is resampled into a triangular approximation of the surface with approximately equilateral triangles using the surface reconstruction tools that we have previously developed (Bartlett et al., 2017; Bratton et al., 2018). Some geometric parameters, including the Gaussian curvature at each point on the surface and the surface area and the volume of the cells, can be calculated for both real cell reconstructions and the synthetic cells (Figure 3C–F, Figure 3—figure supplement 1C-E, and Figure 3—figure supplement 2, left column). For these, we defined the pole surface area as the surface within 0.75 cell diameters of the end. Because of their intrinsic unwrap coordinate system, synthetic cells have defined surface helical axes, which allows us to compute the length of the major and minor axes as well as the Gaussian curvature at these axes. Since the decrease in local diameter near the pole changes both the curvature and the length of the helical axis, we calculate the major and minor axis lengths and Gaussian curvatures from the central 50% of the cell, where the measurements are not influenced by the poles (Figure 3G–H and Figure 3—figure supplement 2, middle and right columns). Decreasing the total length of the cell proportionally decreases the both the sidewall portion of the cell (including surface curvature properties) and the length of the major and minor axes, retaining the same ratio of major axis to minor axis length. As shown in Figure 3—figure supplement 2A (center and right columns), the length of the cell has negligible influence on the distribution of surface curvatures and the ratio of major to minor axis length, further validating our aggressive threshold for removing the ends of the cells for these measurements.

The MATLAB scripts used to fit helical centerlines are publicly available under a BSD 3-clause license at https://github.com/PrincetonUniversity/shae-cellshape-public and archived at https://doi.org/10.5281/zenodo.3627045 and http://arks.princeton.edu/ark:/88435/dsp01h415pd457.

Purification of recombinant 6His-CcmA and variants

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Plasmids containing N-terminal 6-histidine fusions to WT CcmA (pKB62) and CcmA containing point mutations were generated using site directed mutagenesis primers (Table 3) to generate CcmA I55A (pKB69H; primers SDM 155A F and SDM I55A R) and CcmA L110S (pKB72D; primers SDM L110S F and SDM L110S R). Plasmids were transformed into E. coli protein production host BL21. Strains were grown in liquid culture overnight at 37°C in LB with 0.2% glucose and 100 µg/ml ampicillin. The next day, cells were diluted 1/1000 into fresh media without glucose, grown to mid-log (0.5–0.75), chilled on ice for 15 min, then induced for protein expression by adding 1.0 mM IPTG. Flasks were transferred to room temperature and incubated with shaking for 3.5–4 hr. Cells were harvested by centrifugation and either used immediately for protein purification or frozen at −80°C. For purification, cells were resuspended in 2/5 culture volume of lysis buffer (25 mM Tris pH 8.0, 2 M urea, 500 mM NaCl, 2% glycerol, 0.5 mg/ml lysozyme) supplemented with ¼ EDTA-free protease inhibitor tablet (Pierce, Waltham, MA) and 2 U Benzonase nuclease (EMD Millipore, Burlington, MA) and incubated at room temperature with gentle rolling for 1 hr. After lysing, cells were sonicated at 20% power with 15 s pulses until all cells were lysed. Lysates were cleared at 5000 x g at 4°C, then applied to equilibrated TALON metal affinity resin (TaKaRa, Shiga, Japan) and incubated for 2 hr at room temperature with gentle rolling. The protein bound to resin was washed twice with wash buffer (25 mM Tris pH 8.0, 2 M urea, 500 mM NaCl, 2% glycerol, 7.5 mM imidazole), and proteins eluted from the resin using 25 mM Tris pH8.0, 2 M urea, 500 mM NaCl, 2% glycerol, 250 mM imidazole). Fractions were analyzed by SDS-PAGE for purity and yield. Protein concentration was determined using a Nanodrop 1000 (Thermo Fisher Scientific, Waltham, MA) using the Protein A280 program. One biological replicate is defined as beginning with a new overnight liquid culture.

Immunoblotting H. pylori extracts

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Whole cell extracts were prepared by harvesting 1.0 OD600 of log phase (0.3–0.7 OD600/ml) H. pylori liquid culture by centrifugation for 2 min at max speed in a microcentrifuge and resuspending in 2x protein sample buffer (62.5 mM Tris pH 8, 2% SDS, 0.02% bromophenol blue, 20% glycerol) or Lämmli buffer at 10.0 OD600/ml and boiled for 10 min. Whole cell extracts were separated on 4–15% gradient BioRad TGX gels or 4–15% mini-PROTEAN TGX Stain-Free gels (used according to manufacturer instructions) by SDS-PAGE and transferred onto PVDF membranes using the BioRad Turbo-transfer system according to the manufacturer’s instructions (BioRad, Hercules, CA). Membranes were blocked for 2 hr at room temperature with 5% non- fat milk in TBST (0.5 M Tris, 1.5 M NaCl, pH 7.6, 0.05% Tween 20). Membranes were incubated with primary antibody for 2 hr at room temperature or overnight at 4°C with 1:10,000 anti-CcmA primary antibody, 1:20,000 dilution for α-Cag3 (Pinto-Santini and Salama, 2009), or 1:25,000 dilution for anti-MreB, in TBST. Six washes with TBST over a 30 min period were followed by a 1 hr incubation at room temperature with horseradish peroxidase-conjugated anti-rabbit immunoglobulin G (Santa Cruz Biotechnology, Dallas, TX) antibody at 1:20,000 dilution in TBST. After six washes with TBST over a 30 min period, antibody detection was performed with ECL Plus (Pierce) detection kit or Immobilon Western Chemiluminescent HRP substrate (Millipore), following the manufacturer’s protocol and imaged with the BioRad Gel Documentation System. One biological replicate is defined as beginning with a new liquid culture.

2D H. pylori quantitative cell shape analysis

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Phase-contrast microscopy was performed on cells grown in shaken liquid culture until mid-log phase (OD600 0.3–0.6), fixed in a 4% PFA/PBS + 10% glycerol solution, and mounted on glass slides. Resulting images were acquired using a Nikon TE 200 microscope with a 100X oil-immersion objective and Nikon CoolSNAP HQ CCD camera controlled by MetaMorph software (MDS Analytical Technologies, Sunnyvale, CA). Images were thresholded using the ImageJ software package. Quantitative analysis of thresholded images were used to measure both side curvature and central axis length with the CellTool software package as described previously (Sycuro et al., 2010). One biological replicate is defined as beginning with a new liquid culture.

Transmission electron microscopy

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For TEM, 10 µM WT, I55A, or L110S CcmA was dialyzed overnight at 4°C against 25 mM Tris pH 8. The proteins were applied to glow-discharged carbon-coated grids and negatively stained with 0.75% uranyl acetate. Images were acquired with JEOL 1400 transmission electron microscope using a Gatan UltraScan 1000xp camera with 2K × 2K resolution.

Appendix 1

Selecting a subset of wild-type cells whose geometry is consistent with the four parameter model of helical-rod shape

We generated a set of simulated helical cells based on the three-dimensional reconstructions of the wild-type population shown in Figure 2. Inputs to this simulation are the measured pole-pole cell lengths along the curved centerlines (Figure 3A and C, gray); the diameters of the cells (Figure 3A and D, purple); the helical pitches of the centerlines (Figure 3A and E, pink); and the helical diameters of the centerlines (Figure 3A and F, green). To determine the helical pitch and radius from each reconstructed cell, we borrowed heavily from previous algorithms designed to calculate the best fit helix to a set of observations (Nievergelt, 1997). We modified these algorithms to accommodate helices longer than one helical repeat and to allow the pitch to be a signed value, with positive pitches corresponding to right-handed helices and negative to left-handed ones. Not all centerlines fit well to a single helical fit as some centerlines have kinks or variable pitch along their long axis. We calculated the relative error of the helical fit as the root mean squared deviation (RMSD) of the error in the fit to the RMSD between two subsequent points along the centerline. This relative error is unitless; we set a threshold value of two for satisfactory fits (Figure 3—figure supplement 1A and Figure 3—video 1). About one quarter of the centerlines had a good fit to a single helix (402/1137). Wild type H. pylori cells have been shown to be right handed (Yoshiyama and Nakazawa, 2000). Our algorithm finds that 96% of the cells with satisfactory fits are right handed (387/402). Infrequently (15/402), the algorithm returned a left-handed helix as the best fit. Upon visual inspection, none of these centerlines were globally left-handed and were thus discarded.

From the four calculated 3D shape parameters, we generated synthetic cells to mimic the original wild-type population. Just as we ignored cells whose centerlines were not well fit by a single helix, we also removed cells whose simulated counterpart differed from the real cell reconstruction by more than 10% in surface area, volume, volume of the convex hull, or Euclidean distance from pole to pole. For roughly 20% of the total wild-type population (231/1137), the observed geometry of the cell was consistent with the simple four parameter model (see Materials and methods and Figure 3—figure supplement 1A and B). It is not reasonable to look at the distribution of helical parameters for centerlines that do not have satisfactory fits. The distribution of cell lengths, cell diameters, and surface curvatures for the entire population and the population subset are closely matched (Figure 3—figure supplement 1C–E), indicating that the subset adequately represents the population. Both wild-type and synthetic cells share a multimodal distribution of Gaussian curvatures with peaks around 5 µm−2 and between −5 and −10 µm−2. However, there is a notable difference in the widths and magnitudes of these peaks between the wild-type and corresponding synthetic cells, consistent with the fact that, unlike real cells, the synthetic cell surfaces are perfectly smooth.

Using this subset of simulated cells, we then proceeded to characterize the major and minor helical axes. Because we simulated these cells based on a model of a cylinder wrapped and twisted about a helical axis, they inherently have a natural unwrap helical coordinate system (Figure 3B and Figure 3—figure supplement 1A and 2). We chose to set the unwrap angle of the major helical axis to 0° and the minor helical axis to 180° allowing us to measure the relative length of the major to minor helical axes as well as measure the average Gaussian curvature along the helical axes. The average Gaussian curvature at the major axis is 5 ± 1 µm−2, and the average Gaussian curvature at the minor axis is −11 ± 4 µm−2. There was substantially more variation in the average curvature at the minor axis than at the major axis (Figure 3H).

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Decision letter

  1. Tâm Mignot
    Reviewing Editor; CNRS-Aix Marseille University, France
  2. Anna Akhmanova
    Senior Editor; Utrecht University, Netherlands

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The paper provides compelling evidence that the helical shape of Helicobacter pylori is established through the complementary action of two cytoskeletal polymers, the bactofilin protein CcmA and the actin-like protein MreB. Because in Helicobacter, these scaffolds preferentially accumulate at sites of opposite gaussian curvature (positive CcmA, negative, MreB) their balanced action directs cell wall synthetic complexes so as to counteract each other spatially along the cell axis. The work thus sets an important framework to elucidate how this opposing spatial action of the polymers finally generates a helical shape of the cell wall.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting your work entitled "Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in Helicobacter pylori" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife. As you will see from the individual reviews, both reviewers found interest in the study and acknowledged a real technical tour de force, developing new tools to study PG synthesis in H. pylori. However, they also raised a number of significant concerns that preclude publication of the work at this time. Currently, the reviewers do not feel that the work proves that MreB is really fulfilling on negative-curvature a similar role as CcmA is fulfilling on positive curvature. It is also not clear how these polymers respectively influence each other and whether there is a balance between their activity/abundance that dictates whether the cell commits to helicity or rod shape. Last, a controversy about the function of MreB in H. pylori PG synthesis remains to be resolved.

Reviewer #2:

Taylor and colleagues have done beautiful work studying the pattern of peptidoglycan synthesis in Helicobacter pylori using two distinct clickable probes. The work is thorough and well performed. The authors try to correlate their patterns of labeling with the role of two cytoskeletal proteins in H. pylori, MreB and CcmA. This part is also very well done but fails to make the functional connection between the two aspects of this work. My major comments:

1) Regarding the labeling with the clickable probes, the authors do raise the limitations of using alkyne-D-alanine compared to alkyne-MurNAc. I agree with their assessment. Indeed, D-Ala-alk could be incorporated directly in the periplasm through transpeptidase activity although this has not been shown to happen in H. pylori. Alternatively, it could be incorporated through the precursors. The authors did not test that by analyzing the precursor pool composition in presence of D-Ala-Alk. If used for precursor synthesis, in that case, it would reflect direct peptidoglycan synthesis and help with the interpretation of the results. In fact, the authors could have used a 3-azido-D-Ala to co-label in the same cells the sugar and the peptide moiety of the PG and compare it in the same cells.

2) The authors discuss the surprising fact that synthesis rarely occurs in zero Gaussian curvature. The way the data was acquired may be misleading. The authors did a pulse of 18 minutes to see where the probe is being incorporated. It is possible that there is simply more effective turnover in zero Gaussian curvature and therefore less label retained. The authors should also do chase experiments where they labeled the entire PG layer and then chase in media without alkyne versions of the MurNAc or D-Ala. If there is really less synthesis in zero Gaussian curvature, then the label should be retained as with the poles. This would also help in interpreting the D-Ala-alk data since its incorporation is the result of synthesis vs. D,D-carboxypeptidase activity. In a chase experiment, it would only reveal where on the cell wall is D,D-carboxypeptidase activity enriched.

3) The authors did a marvelous job studying the role of CcmA on the morphology of H. pylori and how it affects the positive Gaussian curvature. They also show that MreB is enriched in negative Gaussian curvature. But the authors do not actually show how the absence of CcmA or MreB actually impacts the pattern of probe incorporation. Does it become completely random on the lateral wall? This is crucial. In particular, it is very important to compare a ccmA mutant to the two point ccmA mutants. The pattern could be radically different in the absence of ccmA compared to the two mutants despite a similar cell shape of the strains.

4) The authors discuss that MreB has been shown to be not essential in H. pylori and although they haven't been able to reproduce the data in their strain, the work on MreB showed that MreB was not required for cell shape in H. pylori. Furthermore, A22, a known MreB inhibitor, had minimal effect of H. pylori morphology. Hence, despite the nice data on MreB localization, there is no evidence in H. pylori that MreB is involved in PG synthesis and cell shape regulation. Thus, MreB preferred localization to negative Gaussian curvature might be just a general characteristic of MreB conserved in H. pylori but independent of PG synthesis. The authors' model of cell shape regulation and pattern of PG synthesis is not supported by the existing evidence.

5) Please, provide with gels showing the purity of the recombinant MreB and CcmA (and its mutants). Since, the authors have a CcmA-flag version, they should localize it by cryo-EM by antibody staining. It would be very reassuring to observe that in the bacterium CcmA is organized as polymers.

Reviewer #3:

General assessment:

Overall an interesting collection of approaches (genetics, biochemistry, fluorescent microscopy, advanced 3D imaging and analysis) and results, clearly written, which together lead to a slightly disappointing, rather descriptive report. The manuscript presents three main results: the localization of newly inserted PG, that of the cytoskeletal protein MreB and, the most convincing part, the demonstration of the polymerization properties of CcmA and its subcellular accumulation to distinct locations in the cell. All the localization experiments rely on a post-processing analysis previously described and a bit oversell in the first part of the manuscript. The results concerning the PG and MreB localization present strong limitations (described below), that restrains their interpretation, hence their importance and the conclusions and model concerning MreB should be tuned down. The results on CcmA are at the contrary convincing, interesting, and are shedding light on the helical morphogenesis of H. pylori. However, the absence of mechanism explaining how this protein controls the curvature limits the overall impact of the findings, and probably its interest for a broad audience.

Summary of substantive concerns:

1) The authors show that +/- Gaussian curvatures (Gc) are different in an helix than in a rod or curved shape (Figure 2-3).

The corresponding sections of the Results are required because this is the basis of all the subsequent image analyses to localize CcmA, MreB and labelled-PG. However it is presented in a way putting forward the obvious and masking its weaknesses. The authors are largely emphasizing the existence of huge +/- Gc as if it was revealed by their approach ("we show that the helical centerline (…) dictate surface curvatures of considerably higher + and - Gaussian curvatures than those present in straight or curved-rod bacteria" in the Abstract). But this is an obvious consequence of the Helicobacter cell geometry, whose helicity is not a discovery (Figure 2) and the whole simulation part (Figure 3) is just stating the obvious that curvature depends on the helical diameter and pitch of the helix. A helical object as H. pylori is expected to have these strong positive and negative Gc as the application of their method to Staphylococcus aureus would predictably show a peak of Gc ~ +4 µm-2 because it is a sphere of 1µm diameter. They however do not comment much on the limit of this method when applied to H. pylori: indeed this method was originally designed to detect small, non obvious fluctuations of the seemingly straight rod E. coli, but when applied here to H. pylori, only the major – and again known – +/- Gc due to the rod torsion are really visible. It is unclear to me if H. pylori cells are just more "regular" in their curvature or, as I suspect, these huge +/- curvatures due to the cell torsion is somehow masking smaller variations. My guess is that if they were focusing their analysis on the side (meaning excluding the major and minor helical axes) of the cells they may see the kind of fluctuations they observed with E. coli cells. That said, I am not utterly convinced of the accuracy of the method to discriminate such small curvatures neither of their relevance, thus I am not suggesting adding such analysis. Especially since the important finding of the study concerns the localization of CcmA to large, unambiguously curved, regions.

Thus, to summarize, albeit useful for the subsequent imaging analysis because it shows that their 3D reconstruction and calculus of +/- GC matches what would be expected for a helical object, this whole part should be rephrase, shorten, less emphasizing the obvious and mainly placed as supplementary.

2) Using clickable D-Ala and an engineered mutant to allow incorporation of a clickable modified PG precursor, the authors labelled PG insertion (Figure 4). The depletion from the pole is unambiguous (Figure 5-6), but the other claims are less convincing. First, the use of 90% Bootstrap confident intervals give the false impression of highly reproducible experiments while the Figure 6—figure supplement 1 reveals the important variability between the 3 replicates and how misleading is this representation. Plotting standard deviations to the mean (not the variations between replicates but between all the pooled values) would certainly be less impressive. Regarding this, please let the reader make their own judgment and avoid using opinionated wording such as "clearly" (subsection “PG synthesis is enriched at both negative Gaussian curvature and the major helical axis area”) or "highly reproducible" (especially in the figure title and when it is not!).

Next, looking at Figure 6—figure supplement 1, it seems that for Gc below -8 and above +8, the variability is so important compared to the relative increase or decrease of concentration that it precludes interpretation. From this, I would agree that there is an enrichment at high positive Gc (~6µm²) but it seems difficult to conclude for negative curvatures: is there an increase for higher negative Gc or is that a depletion at low negative Gc (-2µm²)? The interpretation is even complicated by the fact that the two labeling approaches give different patterns (possibly because of PG maturation) with D-Ala showing virtually no enrichment for both +6 and -2µm² and an enrichment at lowest negative Gc. Importantly, and as commented by the authors in the Discussion, the patterning shows minimal labelling at the side wall (meaning the regions not along the major and minor helical axes, thus, the regions with GC close to 0), while shape maintenance certainly request a higher synthesis rate at this location than along the minor helical axis. Thus, it is likely that in fine the labeling patterns do not properly reflect the CW synthesis pattern, which undermine the results and makes interpretation difficult and speculative.

Altogether, except for the depleted poles (no surprise here considering the literature) and the 20% relative increase of labelling at high Gc (6µm²) (if we ignore the D-Ala result), the other findings are not very solid, and difficult to interpret.

3) Of a lesser importance, but I have been puzzled by the labelling experiment: albeit the strategy is sound and obviously efficient (Figure 5), the authors certainly have a good reason not to use the much easier fluo-DAA labelling (developed by one of the co-authors, E. Kuru). Is it because the approach (modeling, 3D reconstruction) requires working on fixed cells and that implies postponing the labelling step? Or because they wanted to acquire all the data using an identical procedure for fair comparison (and fixed cells being imposed by immuno-fluorescence labelling of the proteins)? This reason for this strategy may be briefly mentioned in the corresponding result section.

4) The authors claim that MreB is enriched at negative Gc (and excluded from poles). Again, and for the same reasons mentioned in point 2, the results are not as strong as the authors are trying to convince us ("highly reproducible" in subsection “MreB is enriched at negative Gaussian curvature” and Figure 7—figure supplement 3 title). Although the reproducibility seems better here than for the PG localization (but again the SD to the mean seems more appropriate than the bootstrap) there is still a large experiment to experiment variability in enrichment for Gc below -7 and above +7 (Figure 7—figure supplement 3). Once the poles, clearly depleted of MreB as reported previously in the literature for other bacteria, and the highly variable areas (for higher +/- Gc) are removed, the trends of MreB accumulation is weak (+/-10%). Surprisingly, another monotonically increasing concentration (this time with increasing Gc) is reported, presenting an enrichment similar to that of MreB (+/-10%; Figure 10—figure supplement 1): the negative control (no Flag) of Figure 10. This control is nonetheless described as showing "negligible curvature preference", suggesting that a +/-10% enrichment is not significant. Thus, MreB is not significantly enriched either.

5) The authors suggest that MreB is playing an active role in CW synthesis. Although this would not surprise most of the MreB crowd, this hypothesis is contradicting previous findings from Waidner et al., 2009, claiming that MreB is not essential and not required for H. pylori CW synthesis and shape (in fairness, a point mentioned by the authors). If the authors want to claim a role for MreB in CW control in the present study, they need to address this discrepancy (request strains from PLG, or make depletion strains, or show an effect of A22 on cell shape). As an alternative, considering the weakness of their evidence on this topic, they may prefer to dampen their conclusions, remove MreB from the model, and refocus their study toward CcmA.

6) In their model, MreB increased frequency of localization at negative Gc should translated into increased synthesis along the minor helical axis, which would exactly counteract the maintenance of the helical shape by trying to restore the rod shape. In such a model, while it is easy to imagine that CcmA promote synthesis and deformation along the major helical axis, it is really unclear how the small axis would be maintained and why MreB would fail to restore the rod. Thus, in addition to be lightly supported by their data, this hypothesis does not enlighten our understanding of the building of H. pylori helical shape.

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

Thank you for re-submitting your article "Distinct cytoskeletal proteins define zones of enhanced cell wall synthesis in Helicobacter pylori" for consideration by eLife. Your article has been reviewed by two peer reviewers, a reviewer previously involved in the original review and a new reviewer. The evaluation has been overseen by Tâm Mignot as a Reviewing Editor and Anna Akhmanova as the Senior Editor. The reviewers have opted to remain anonymous.

In this re-submission, Taylor and colleagues describe a quantitative characterization of the relationship between two cytoskeletal polymers – MreB and CcmA – and the localization of PG metabolic activity and subsequent cell shape in the helical pathogen Helicobacter pylori. Their major contributions are (1) development of a quantitative framework for describing and analyzing H. pylori cell shape; (2) demonstration of the relationship between local Gaussian curvature of the cell surface and the local incorporation of 2 metabolic probes for PG; (3) the same for the relationship between local Gaussian curvature and localization of cytoskeletal proteins MreB and CcmA; (4) demonstration that MreB is (contrary to a prior report) essential for H. pylori growth; (5) characterization of the in vitro polymerization of CcmA; (6) functional characterization of two point mutants of CcmA that fail to polymerize in vitro; and (7) demonstration of the impact of loss of CcmA function on PG probe incorporation and MreB localization. Collectively these results lead to a model wherein two cytoskeletal systems work to spatially modulate cell wall metabolic activity at regions of different Gaussian curvature to elicit helical cell shape.

Overall the authors went to extreme length to answer the concerns raised by the previous reviewers in a convincing way. There are nevertheless key interpretations issues that would need be addressed before the work can be accepted for publication:

1) The paper now shows convincingly that MreB is essential for growth of H. pylori, which likely is linked to its function in PG synthesis. The model proposes that the recognition of regions of negative curvature by MreB and positive curvature by CcmA underlies the helicity of the H. pylori cell. However, although it is understood that these mechanisms are likely contributing mechanisms, the authors still do not discuss clearly why the action of MreB acting at negative gaussian curvature does not counteract helicity toward Rod restoration. This question is especially important given that a study by Wollrab et al. (bioRxiv 716407; doi:10.1101/716407) questions the MreB negative curvature recognition in E. coli cells. If MreB is indeed localizing to -GC regions in the Helicobacter cell, how do the authors reconcile this with the Wollrab et al. study?

The authors seem to rule out mutual exclusion mechanisms (ie MreB occupies cylindrical parts that CcmA does not occupy) because in subsection “CcmA localization to positive curvature correlates with cell wall synthesis, CcmA polymerization, and

helical cell shape” they conclude that MreB localization is the same in ∆ccmA as in WT. However, there appears to be a difference wherein the relative concentration increases more for MreB at negative curvatures in ∆ccmA than it does in WT and (as the authors actually note later in that section) there is a slight peak of MreB enrichment at ~3 µm-2 in the mutant but not in WT. There also appears to be a dip in concentration around 10-15 µm-2. Importantly, the differences in MreB distribution look to be of a similar magnitude as the differences in D-Ala-alk incorporation between WT and ∆ccmA but the D-Ala-alk incorporation pattern is described as different.

2) For the biochemical characterization of CcmA and mutant forms of CcmA, it would be important to note in the text (not just the Materials and methods) that the proteins are purified in denatured form and refolded to induce/monitor polymer formation. This is important because it does somewhat complicate interpretation of the mutants. It could be that the I55A or L110S mutations influence the ability of CcmA to fold properly during renaturation, rather than having a direct effect on polymerization capacity. The punctate cytoplasmic signal by IF could be consistent with either effect. Related to this, the authors say that the mutants "failed to form any higher order structure under any buffer condition tested". What conditions were tested? Did they test whether the mutants that cannot polymerize were soluble when purified?

https://doi.org/10.7554/eLife.52482.sa1

Author response

[Editors’ note: what follows is the authors’ response to the first round of review.]

We appreciate the reviewers’ support for our work and the concerns that they raise. Both reviewers were concerned with two major issues which we have extensively modified in the main text and discussed below. In short, there seems to be (1) conceptual misunderstanding about the enrichment analysis and how we calculate the relative concentration of signals and (2) disagreement between us and the literature about the essentiality of MreB in Helicobacter pylori. In regard to the conceptual misunderstanding, we add to Figure 6 a schematic showing how the arithmetic is performed as well as theoretical example cells showing different relative concentration profiles. In regard to the essentiality of MreB, we have added substantial experimental evidence that at least in our strain, the G27 derivative LSH100, MreB is essential. Additionally, we have reached out to the authors of the study mentioned (Waidner et al., 2009) and they have been unable to provide us with the plasmid they used to delete MreB nor have they been able to regrow any of their MreB deletion strains from their freezer stocks.

Reviewer #2:

Taylor and colleagues have done beautiful work studying the pattern of peptidoglycan synthesis in Helicobacter pylori using two distinct clickable probes. The work is thorough and well performed. The authors try to correlate their patterns of labeling with the role of two cytoskeletal proteins in H. pylori, MreB and CcmA. This part is also very well done but fails to make the functional connection between the two aspects of this work. My major comments:

1) Regarding the labeling with the clickable probes, the authors do raise the limitations of using alkyne-D-alanine compared to alkyne-MurNAc. I agree with their assessment. Indeed, D-Ala-alk could be incorporated directly in the periplasm through transpeptidase activity although this has not been shown to happen in H. pylori. Alternatively, it could be incorporated through the precursors. The authors did not test that by analyzing the precursor pool composition in presence of D-Ala-Alk. If used for precursor synthesis, in that case, it would reflect direct peptidoglycan synthesis and help with the interpretation of the results. In fact, the authors could have used a 3-azido-D-Ala to co-label in the same cells the sugar and the peptide moiety of the PG and compare it in the same cells.

The reviewer’s concerns about the D-Ala-alk incorporation mechanism do not alter the conclusions we have presented. We have been successful incorporating D-Ala-alk into H. pylori PG in vitro with the transpeptidase PBP4 from Staphylococcus aureus (unpublished results), showing that this transpeptidation mechanism can be used for incorporation as it is in other bacteria. It is possible that D-Ala-alk can be incorporated through both precursor synthesis and transpeptidation, but unlikely that it would be solely incorporated through precursor synthesis. However, neither case negates the conclusions as presented. With respect to the suggested dual D-Ala, MurNAc labeling, we have attempted labeling with azido-modified probes, but the click reaction results in high levels of non-specific signal, making these probes unusable in our hands. While future work exploring in more detail all possible modes of incorporation of these precursors would be interesting, we do not feel such analysis is necessary for our conclusions.

2) The authors discuss the surprising fact that synthesis rarely occurs in zero Gaussian curvature. The way the data was acquired may be misleading. The authors did a pulse of 18 minutes to see where the probe is being incorporated. It is possible that there is simply more effective turnover in zero Gaussian curvature and therefore less label retained. The authors should also do chase experiments where they labeled the entire PG layer and then chase in media without alkyne versions of the MurNAc or D-Ala. If there is really less synthesis in zero Gaussian curvature, then the label should be retained as with the poles. This would also help in interpreting the D-Ala-alk data since its incorporation is the result of synthesis vs. D,D-carboxypeptidase activity. In a chase experiment, it would only reveal where on the cell wall is D,D-carboxypeptidase activity enriched.

We accidentally omitted the word “relative” in our statement regarding synthesis at the sidewall, which made it sound like we were commenting on low absolute rather than low relative PG incorporation on the sides of the cells. We apologize for the confusion and have changed the text. Please see reviewer 3 item #2 for a detailed discussion of the enrichment plot analysis. In brief, a relative concentration signal at the y=1 line does not imply a lack of synthesis. Rather, it indicates that the area concentration of synthesis is the same at that geometry as the average synthesis everywhere.

In the figure legend for Figures 6, the text "(>1 is enriched; <1 is depleted)”, while correct, may have led the reviewers to overemphasize the importance of when the relative concentration is greater than the average concentration rather than looking holistically at the shape of the relative enrichment curves. We have thus removed this text from the figure legend.

As the reviewer notes, D-Ala-alk and MurNAc-alk signal is a function of incorporation plus turnover. We are interested in PG turnover but assessing turnover rates will require extensive additional work and the development of additional sophisticated image analysis tools and thus is beyond the scope of this work. Given the clarification that there indeed is substantial total synthesis at near-zero GC (though less relative synthesis), we feel that these experiments are not necessary for the conclusions presented in this manuscript. Also, we would like to note that turnover of the D-Ala-alk would be difficult to interpret, due to a combination of both D,D-carboxypeptidase activity and muropeptide turnover.

Wording change:

“The lower relative amount of synthesis at Gaussian curvatures corresponding to the sides of the cell body in comparison to the major and minor axis areas is both unexpected and counterintuitive; it suggests additional mechanisms may be required to maintain helical shape.”

3) The authors did a marvelous job studying the role of CcmA on the morphology of H. pylori and how it affects the positive Gaussian curvature. They also show that MreB is enriched in negative Gaussian curvature. But the authors do not actually show how the absence of CcmA or MreB actually impacts the pattern of probe incorporation. Does it become completely random on the lateral wall? This is crucial. In particular, it is very important to compare a ccmA mutant to the two point ccmA mutants. The pattern could be radically different in the absence of ccmA compared to the two mutants despite a similar cell shape of the strains.

We agree with the reviewer that demonstration of an altered pattern of new cell wall incorporation in the absence of CcmA or MreB would further support our model. Unfortunately, mreB is essential in our strain, as further described in our response to Point 4 below and we have not yet been able to engineer a system to deplete mreB in our strain. We have, however, undertaken both MreB immunofluorescence and metabolic labeling using the D-Ala-alk probe in a ccmA null strain. A caveat of this experiment is that this strain has limited positive Gaussian curvature outside of the cell poles due to its nearly straight morphology. We found that the MreB signal enrichment pattern in ccmA cells resembled that in wildtype. In contrast, the cell wall probe enrichment pattern changed in ccmA cells compared to wildtype, showing lower relative enrichment at signal Gaussian curvatures representative of the major helical axis (5 µm-2) and exclusively mirroring the signal enrichment pattern of MreB, This result appears consistent with retention of MreB driven synthesis and loss of CcmA-dependent synthesis. These data have been added as below and Figure 11 A-B and Figure 11—figure supplement 1.

Wording change:

“To ascertain the impact of deleting ccmA on MreB localization and cell wall synthesis patterning, we performed immunostaining and 18-minute D-Ala-alk pulse labeling on ΔccmA cells (JTH6, amgK murU ΔccmA, Figure 11A and B, dark pink and dark blue, respectively). […] These data suggest that proper localization of CcmA to the major helical axis may be required for promoting extra cell wall synthesis at the major axis area and patterning helical cell shape.”

4) The authors discuss that MreB has been shown to be not essential in H. pylori and although they haven't been able to reproduce the data in their strain, the work on MreB showed that MreB was not required for cell shape in H. pylori. Furthermore, A22, a known MreB inhibitor, had minimal effect of H. pylori morphology. Hence, despite the nice data on MreB localization, there is no evidence in H. pylori that MreB is involved in PG synthesis and cell shape regulation. Thus, MreB preferred localization to negative Gaussian curvature might be just a general characteristic of MreB conserved in H. pylori but independent of PG synthesis. The authors' model of cell shape regulation and pattern of PG synthesis is not supported by the existing evidence.

In this revision, we demonstrate that in our strain background, MreB is indeed essential. Additionally, H. pylori MreB appears to be A22-resistant. Our result that MreB is essential for H. pylori growth result is in conflict with the published work of Waidner et al., 2009, who claim to have been able to delete MreB from H. pylori strains 26695, KE88-3887, and 1061. It is possible that MreB essentiality is a strain specific effect. However, we have been unable to obtain and verify the deletion of MreB in their strains since upon request, we were informed that they have been unable to regrow these strains from their freezer stocks. Therefore, we cannot comment on whether or not MreB was in fact knocked out in the published strains.

In our revised manuscript we show explicitly that MreB is essential in H. pylori strain LSH100 by testing our ability to delete MreB. If the gene is essential, we should only be able to grow up transformants if the cells have a redundant copy of the gene. We transformed both the wild-type strain and an MreB merodiploid strain that has a second copy of MreB at a neutral intergenic locus (McGee locus) with a linear fragment of DNA that would disrupt the MreB locus by inserting a chloramphenicol resistance (cat) cassette into MreB. In the merodiploid strain, transformants were obtained at a frequency of 2.3x10-4, but in the wild-type strain we obtained only two colonies (frequency = 6.7x10-7). In the merodiploid strain, the cat cassette was able to insert into either copy of mreB, demonstrating that mreB at the native locus can be readily disrupted in the presence of a second copy of mreB. The two transformants in the wild-type background contained duplications at the mreB locus, resulting in both a disrupted copy of mreB and a nearly full length intact copy of mreB, further indicating the essentiality of MreB. We furthermore demonstrated that the wild-type strain is readily transformable by disrupting the non-essential single copy gene CcmA with a cat cassette (transformation frequency = 2.4x10-4). This additional data is discussed in the main text in the Results section “MreB is enriched at negative Gaussian curvature” and Figure 6 and Figure 6—figure supplement 1.

Consistent with Waidner et al., 2009, our strain of H. pylori is resistant (no effect on cell growth) to high A22 concentrations (>10ug/mL) (unpublished results).These concentrations have previously been used to isolate mreB point mutants that are insensitive to A22 in other species. H. pylori’sresistance to A22 does not imply anything about the essentiality of MreB, but it does prevent us from using A22 to disrupt MreB activity.

With respect to whether MreB plausibly contributes to PG synthesis and cell shape regulation in H. pylori, we point out that the core rod elongation complex described in E. coli (RodA, Pbp2, MreC, MreB) appears to be conserved in H. pylori. Work from Ivo Boneca’s group has shown that Pbp2 and MreC are essential and depletion of these proteins leads to cell rounding and cell growth arrest (El Ghachi M et al., 2011, Mol Micro, 82(1):68-86), consistent with observations in E. coli. Moreover, their X-ray structure of a complex between H. pylori MreC and Pbp2 (Contreras-Martel C et al., 2017, Nat Commun, 8(1):776) reveals conservation of structure in the platform regulatory domain where E. coli Pbp2 suppressor mutations that relieve the requirements for MreC and RodZ, but not MreB for cell morphology map (Rohs PDA et al., 2018, PLOS Genetics, 14(10):e1007726). While MreB is not present in several rod-shaped organisms that have a polar growth mode, in this manuscript we show that H. pylori has a growth mode like that of E. coli; dispersed along the sidewall and largely excluding the poles. This observed cell wall synthesis pattern combined with our findings that MreB is essential and shows a localization of puncta dispersed along the sidewall do not contradict a function for MreB in PG synthesis and cell shape regulation in H. pylori.

Wording changes:

“It has been reported that MreB is not essential in H. pylori and that treatment with the MreB inhibitor A22 does not alter cell shape (Waidner et al., 2009), though growth inhibition only occurred at concentrations well above those used to select for A22 resistance in other organisms (Gitai et al., 2005; Ouzounov et al., 2016; Srivastava et al., 2007; Wu et al., 2011). […]While we requested the previously published mreB mutant strains (Waidner et al., 2009), they could not be revived from frozen stocks. We thus conclude that MreB is essential in LSH100 and perhaps all H. pylori strains.”

5) Please, provide with gels showing the purity of the recombinant MreB and CcmA (and its mutants). Since, the authors have a CcmA-flag version, they should localize it by cryo-EM by antibody staining. It would be very reassuring to observe that in the bacterium CcmA is organized as polymers.

Please note that we did not report purifying or studying recombinant MreB. However, we have happily included gels showing the purity of WT and mutant CcmA (Figure 8—figure supplement 1D). We have tried immunogold labeling of thin sections with traditional TEM sample prep methods to detect CcmA-FLAG but unfortunately did not see any distinctive labeling. Unfortunately, with thin sections and only a small surface of accessible antigen, this becomes a “needle in a haystack” problem. Electron cryotomography is not trivial to perform or interpret. As positive identification of putative densities will be challenging, success in this arena will merit a separate manuscript.

Reviewer #3:

[…]

Summary of substantive concerns:

1) The authors show that +/- Gaussian curvatures (Gc) are different in an helix than in a rod or curved shape (Figure 2-3).

The corresponding sections of the results are required because this is the basis of all the subsequent image analyses to localize CcmA, MreB and labelled-PG. However it is presented in a way putting forward the obvious and masking its weaknesses. The authors are largely emphasizing the existence of huge +/- Gc as if it was revealed by their approach ("we show that the helical centerline (…) dictate surface curvatures of considerably higher + and - Gaussian curvatures than those present in straight or curved-rod bacteria" in the Abstract). But this is an obvious consequence of the Helicobacter cell geometry, whose helicity is not a discovery (Figure 2) and the whole simulation part (Figure 3) is just stating the obvious that curvature depends on the helical diameter and pitch of the helix. A helical object as H. pylori is expected to have these strong positive and negative Gc as the application of their method to Staphylococcus aureus would predictably show a peak of Gc ~ +4 µm-2 because it is a sphere of 1µm diameter. They however do not comment much on the limit of this method when applied to H. pylori: indeed this method was originally designed to detect small, non obvious fluctuations of the seemingly straight rod E. coli, but when applied here to pylori, only the major – and again known – +/- Gc due to the rod torsion are really visible. It is unclear to me if H. pylori cells are just more "regular" in their curvature or, as I suspect, these huge +/- curvatures due to the cell torsion is somehow masking smaller variations. My guess is that if they were focusing their analysis on the side (meaning excluding the major and minor helical axes) of the cells they may see the kind of fluctuations they observed with E. coli cells. That said, I am not utterly convinced of the accuracy of the method to discriminate such small curvatures neither of their relevance, thus I am not suggesting adding such analysis. Especially since the important finding of the study concerns the localization of CcmA to large, unambiguously curved, regions.

Thus, to summarize, albeit useful for the subsequent imaging analysis because it shows that their 3D reconstruction and calculus of +/- GC matches what would be expected for a helical object, this whole part should be rephrase, shorten, less emphasizing the obvious and mainly placed as supplementary.

We are unclear exactly what the reviewer is suggesting in this comment, and precisely what the reviewer feels is intuitive about the geometry of the surface of a helical rod. Here we try to summarize the objections that we think the reviewer is making: (a) The observation that H. pylori has a helical-rod morphology is not new; (b) the distribution of surface curvatures depends on the shape of the object; (c) the goal of our 3D reconstruction method is detect non-obvious deviations of a surface from an idealized geometry; (d) there is a side/surface to helical rod cells which is neither major nor minor helical axis and this side/surface may be similar to non-helical rod-like cells; (e) Action point: edit the text to be shorter and move it mainly to the supplement. We apologize if these were not the points the reviewer was attempting to make but will address them each in turn.

We respectfully disagree with reviewer #3 regarding these comments. The geometric points made herein are the first 3D description of H. pylori shape; facilitate clarity in a field struggling with using accurate spatial language; and are essential for interpretation of all the subsequent curvature enrichment plots, on which the conclusions of this manuscript depend. The computational simulations and validation were already included as supplementary figures; the corresponding section in the main text is one paragraph; and we already included the finer details of the computational simulations as an appendix.

a) We absolutely agree with the reviewer that there have been many previous reports of H. pylori as a helical-rod bacterium. Where this manuscript pushes that qualitative description forward is by quantitatively describing the 3D shape of H. pylori both in terms of surface curvatures and helical-rod parameters. Even our previous state of the art estimates of H. pylori’s 3D shape came from 2D images (Sycuro et al., 2010; Martinez et al., 2016), which has various known issues including a length-dependent bias in estimating pitch. As requested, we have updated the Abstract and text to clarify that the helicity of H. pylori and some amount of intuitable geometric properties are not a ‘discovery’ and to clarify the importance of the 3D population parameter measurements.

Wording changes:

“The helical centerline pitch and radius of wild-type H. pylori cells dictate surface curvatures of considerably higher positive and negative Gaussian curvatures than those present in straight- or curved-rod H. pylori.”

“Display of the Gaussian curvature, which is the product of the two principal curvatures, at each point on the meshwork shows the distinct curvatures on opposite sides of helical cells (Figure 1B).”

“Furthermore, prior shape parameter characterizations of H. pylori have been performed using 2D images (Martínez et al., 2016; Sycuro et al., 2013, 2012, 2010; Yang et al., 2019); measurement of pitch and helical radius from 2D images is subject to systematic errors for short cells (approximately <1.5 helical turns) depending on their orientation on the coverslip. Therefore, we also wished to determine H. pylori population shape parameters from our 3D dataset.”

b) We agree with the reviewer that the distribution of surface curvatures for different shapes are different, and that they would also be different for different sizes. As the reviewer mentions, this is a key point for our enrichment analysis. Before we ran the simulations, we were unable to intuit which helical parameters would be most important for changing the distribution of surface curvatures. It is possible that the reviewer’s intuitive sense of geometry is keener than ours. Following the reviewer’s suggestion, this will remain a supplementary figure.

There appears to be additional confusion about the content of Figure 3. While the supplements (Figure 3—figure supplement 3 and 4) do indeed discuss the effect on curvature of modulating the “core” helical shape parameters (within the biologically-relevant +/- 1.5 standard deviations of the LSH100 population average), this is not presented within the main Figure 3. In fact, as Figure 3 provides the first 3D measurement of the parameters of helical cell shape for H. pylori, it is an important contribution to the field. Figure 3C-F (3D length, diameter, helical pitch, and helical diameter) are, as stated in the figure legend, measurements from actual WT cell surfaces. Due to technical limitations of fitting the major and minor axis to actual cell surfaces with imperfections (i.e. H. pylori cells are not perfect helices), Figure 3G and H (major:minor axis length and Gaussian curvature at the surface helical axes) are calculated from “perfect” simulated cells, which we demonstrated accurately reflect the full population (Figure 3—figure supplement 1). As these are experimentally observed population measurements, we do not agree that these measurements are obvious and can be intuited. Furthermore, the connection between surface landmarks (major and minor helical axes) and Gaussian curvature are crucial for interpreting our curvature enrichment results in the context of cellular features. In supplementary figures (but not in main figures), we discuss the contribution of the core helical parameters to both the distribution of Gaussian side curvature and to the major:minor axis length ratio. While the reviewer may be able to intuit the contribution of modulating these parameters to the side curvature distribution and the major:minor axis length ratio (indeed being fundamentally mathematical in nature one expects a well-formed geometric intuition to predict general conclusions), we disagree that these conclusions are obvious and without value. We have been unable to find any publication that explores how modulating a helical rod shape would impact sidewall curvature or the major:minor axis ratio. Moreover, the helical shape field is hindered by imprecise or completely flawed geometric descriptions (please see for example Stahl et al., 2016, in which a Δ1228 strain is described as having “decreased pitch”, when one can see clearly from their Figure 1B that the authors actually meant that the strain has increased pitch). It is our intention that these helical shape parameter discussions will also help the field to use precise and correct geometric language.

c) We disagree with the reviewer’s assertion that our “method was originally designed to detect small, non obvious fluctuations of the seemingly straight rod E. coli, but when applied here to H. pylori, only the major – and again known – +/- Gc due to the rod torsion are really visible.” In fact, the first paper we published that used this method (Ursell et al., 2014) measured the 3D shape of both rod-shaped and sinusoidal cells with considerably more curvature than unperturbed E. coli. We have also shown that our 3D surface reconstruction achieves 30-nm accuracy in all three dimensions (Nguyen, Methods in Molecular Biology 2016) and thus we accurately probe Gaussian curvatures on small and large scales. Our 3D shape reconstruction method describes the shape of the surface, including small surface fluctuations. For example, most of the positive Gaussian curvature values along the sidewall occur near the major axis. But if there is an area of positive curvature at the minor axis, this surface is not discarded but rather contributes to the data at that curvature point.

d) We have provided an additional supplemental figure to help provide some graphical examples of what simulated additional signal at different types of geometric localizations would look like with our analysis platform (Figure 6—figure supplement 1; see more extensive discussion in our response to point 2). Because the Gaussian curvature goes from positive at the major helical axis to negative at the minor, there must be a region that has zero Gaussian curvature. The region of the surface which has zero Gaussian curvature is a very small ribbon which wraps around the cell (shown in white in Figure 1B). For the reviewer’s point about the ‘side’ of the cell which is neither major nor minor axis, we would like to refer the reviewer to Figure 6—figure supplement 1C, the row labeled ‘enriched at zero’. We think the reviewer may have an inaccurate mental picture that has much of the sidewall as zero Gaussian curvature and discontinuities at the major and minor axis. Rather, a more realistic picture is a smoothly varying Gaussian curvature as one goes around the helical unwrap theta dimension (Figure 3H).

e) Regarding the reviewer’s request that we move the majority of this discussion to the supplement, we already presented the finer details of some of the simulations as a supplemental appendix and presented most of these figure panels as supplemental figures in the original manuscript, leaving only measurements of actual H. pylori population parameters as a main figure.

2) Using clickable D-Ala and an engineered mutant to allow incorporation of a clickable modified PG precursor, the authors labelled PG insertion (Figure 4). The depletion from the pole is unambiguous (Figure 5-6), but the other claims are less convincing. First, the use of 90% Bootstrap confident intervals give the false impression of highly reproducible experiments while the Figure 6—figure supplement 1 reveals the important variability between the 3 replicates and how misleading is this representation. Plotting standard deviations to the mean (not the variations between replicates but between all the pooled values) would certainly be less impressive. Regarding this, please let the reader makes is own judgment and avoid using opinionated wording such as "clearly" (subsection “PG synthesis is enriched at both negative Gaussian curvature and the major helical axis area”) or "highly reproducible" (especially in the figure titles and when it is not!).

Next, looking at Figure 6—figure supplement 1, it seems that for Gc below -8 and above +8, the variability is so important compared to the relative increase or decrease of concentration that it precludes interpretation. From this, I would agree that there is an enrichment at high positive Gc (~6µm²) but it seems difficult to conclude for negative curvatures: is there an increase for higher negative Gc or is that a depletion at low negative Gc (-2µm²)? The interpretation is even complicated by the fact that the two labeling approaches give different patterns (possibly because of PG maturation) with D-Ala showing virtually no enrichment for both +6 and -2µm² and an enrichment at lowest negative Gc. Importantly, and as commented by the authors in the discussion, the patterning shows minimal labelling at the side wall (meaning the regions not along the major and minor helical axes, thus, the regions with GC close to 0), while shape maintenance certainly request a higher synthesis rate at this location than along the minor helical axis. Thus, it is likely that in fine the labeling patterns do not properly reflect the CW synthesis pattern, which undermine the results and makes interpretation difficult and speculative.

Altogether, except for the depleted poles (no surprise here considering the literature) and the 20% relative increase of labelling at high Gc (6µm²) (if we ignore the D-Ala result), the other findings are not very solid, and difficult to interpret.

From these comments (as well as other comments throughout from both reviewer 2 and 3), it is clear that there has been a fundamental misunderstanding of the relative concentration plots we present throughout the manuscript. We are grateful to the reviewers for revealing this issue and we have striven to modify the manuscript text to allow the reader to understand these curvature enrichment plots. We have added an additional supplemental figure (Figure 6—figure supplement 1) and modified text in various places to try to help clarify. In the response below, we discuss the curvature enrichment plot, which is relevant for both reviewers as well as the general audience, and then we address reviewer #3’s specific concerns about statistical analysis and interpretation of error bars.

The first step in generating the curvature enrichment profile, or a plot of the relative concentration as a function of Gaussian curvature, is to collect the raw fluorescence intensity data at each point on the reconstructed cell surface. We add together all the intensities that came from a particular curvature to generate a raw signal profile for a single cell. We then normalize these raw intensities by setting the average surface concentration to 1 for each cell. Numerically, this is performed by dividing both by the total signal in the cell and by the fractional surface area contributed to that cell by each Gaussian curvature bin. This division results in the final single cell enrichment profile: the concentration of the signal relative to a uniform distribution of the same total amount of signal. We have added a graphical depiction of these calculations to Figure 6 (Figure 6A). To generate the sidewall only plots, we perform the normalization after we computationally remove the poles. The resulting “without poles” relative concentration plot is different than the “with poles” plot due to changes in all three components: the raw signal, the total signal, and the fractional surface area. After the enrichment plot has been calculated for each cell, it is averaged across all the cells. The bootstrap confidence intervals of that mean value are calculated by bootstrap sampling the single cell normalized enrichment profiles.

Due to the normalization process, the relative concentration at each Gaussian curvature value is intimately related to the intensities at all of the other Gaussian curvatures. To clarify this point, we have provided eight different noise-free example cells with added signal intensity at different geometric distributions in Figure 6—figure supplement 1 and a paragraph in the main text discussing this figure. The first two have perfectly uniform labeling, one with a low concentration and one with 25% higher signal at all curvature points. The fourth column shows that these have identical relative concentration profiles exactly at the y=1 line.

Now we simply add 25% extra intensity to regions that are not the poles (pole exclusion, pink lines) without removing any signal from the rest of the cell. The addition of signal means that the average intensity is greater. After normalization, the relative concentration at the sidewall is above average and thus above 1. Even though the intensity at the poles in the raw signal is the same, because the average concentration increased, the relative concentration at the poles is below the y=1 line.

Consider next a set of signal increases at particular parts of the cell. The next three rows show the effect of increasing the intensity at the minor axis, zero Gaussian curvature, and the major axis by 25%. Along the minor axis (gold lines), there is very little total surface area. Thus, a 25% increase raises the average value only slightly and the relative concentration at the ‘excluded’ regions is only slightly below the y=1 line. Because of the greater proportion of sidewall represented, a 25% increase at zero Gaussian curvature results in a greater total signal increase and therefore “sinks” the other side of the curve more. This effect is even stronger for the 25% increase at the major axis.

To further illustrate the interrelated nature of the enrichment plots with the simulations, we added 25% signal to the major axis (blue lines), 25% signal at negative Gaussian curvature with a monotonically declining profile (red lines), and 25% signal both at the major axis and at negative Gaussian curvature with a monotonically declining profile (purple lines). By adding 25% extra signal at the major axis, the average signal increases, which then “sinks” the rest of the curve and influences how far the above the y=1 average signal line the plot sits at negative Gaussian curvature, even though there is still 25% extra signal added at negative Gaussian curvature (compare the monotonic decline alone (red) to the combination of monotonic decline and major axis (purple)).

With these points in mind, we return to our actual data for a more complicated example of the interrelated nature of this data. Please refer to Figure 6, in which we present in panel B the enrichment plots with poles included and in panel C the same cells, but with the poles removed before calculating the average surface intensity and rescaling the data. In B, the low values at the poles decrease the average compared to the average in C with the poles removed. As a result, in B the relative average intensity at the Gaussian curvatures corresponding to the non-pole sidewall increases compared to in C. Note, for example, that the “trough” for the 18-minute MurNAc-alk labeling now just barely dips below the average value and the “enrichment” at negative axis is at 1.2 with poles vs. at less than 1.1 without poles. For this reason, it is inappropriate to describe enrichments in terms of percentage of enrichment. Rather, the curve must be considered holistically.

When reviewer 3 comments that enrichment “seems difficult to conclude for negative curvatures: is there an increase for higher negative Gc or is that a depletion at low negative Gc (-2µm²)?”, the interrelated nature of the relative curvature enrichment has been missed. The answer to the question posed is “yes” – there is a relative increase for higher negative GC and “yes” – there is a relative depletion at near-zero GC. By definition, there is no way for this to be an either/or situation. What we can say is that there is above average signal intensity at negative GC and also above average signal intensity at positive GC, which dictates that the signal elsewhere (near-zero GC) is below average. The final row of our simulated supplement (purple line) shows a more complicated situation by augmenting the baseline with both the monotonic decline and enrichment at the major axis profiles. While this is, in some sense, the sum of the two rows above it (25% monotonic decline in red combined with 25% major axis enrichment in light blue), the relative weights of the two in the enrichment plot of the final row (purple line) are different because of the fraction of total surface area that each contributes.

We now return to the scientific conclusions we derived from our observed enrichment profiles. As stated by the reviewer: “Importantly, and as commented by the authors in the Discussion, the patterning shows minimal labelling at the side wall (meaning the regions not along the major and minor helical axes, thus, the regions with GC close to 0), while shape maintenance certainly request a higher synthesis rate at this location than along the minor helical axis. Thus, it is likely that in fine the labeling patterns do not properly reflect the CW synthesis pattern, which undermine the results and makes interpretation difficult and speculative.”

We agree that our statement in the Discussion is misleading due to the accidental omission of the word “relative” and apologize for the confusion this caused. We have corrected this omission. The reviewer statement that “shape maintenance certainly request a higher synthesis rate at this location [near-zero GC] than along the minor helical axis”, however, is unjustified. Shape maintenance does require that the total amount of surface area near zero GC be greater than that along the minor helical axis, which we show in the distribution of surface curvatures (Figure 2). However, this does not necessarily require that the rate of synthesis be greater and the total steady state amount of PG could include contributions from wall removal or recycling, synthesis of wall with differential material properties, localized stress generation, etc. The difference between the D-Ala-alk and MurNAc-alk curves, in fact, supports the idea that other mechanisms also contribute to helical shape maintenance. Even if shape maintenance occurs through synthesis of uniform material, it would not necessarily require differential rates of synthesis per unit area. One could very reasonably propose a model in which synthesis rates per unit area are uniform along the entire cell surface which results in more total material inserted where there is already more surface area. We find no support for the reviewer’s contention that “Thus, it is likely that in fine the labeling patterns do not properly reflect the CW synthesis pattern”. We are also somewhat confused by the reviewer’s seemingly contradictory statement about this in their point 3, “the [labeling] strategy is sound and obviously efficient (Figure 5)”, but again apologize if the misleading statement led to a mistaken interpretation of our data.

Reviewer 3 mentions “variability is so important compared to the relative increase or decrease of concentration that it precludes interpretation” when looking at the data from independent biological replicates. For the reasons detailed above, we never try to comment on a “percent enrichment” at various cellular locations. What we do comment on is (1) if there is a relative enrichment of signal and (2) at what GC this enrichment occurs. Even though there is some variation in the amount of enrichment seen, (1) enrichment of signal is still present at the minor and major axis areas (because as explained above, it matters where the curve is increasing/decreasing rather than where the line crosses the y=1 line) and (2) the biological replicate curves have “peaks” and “troughs” at consistent GC values. While great attention was paid to perform these experiments in as identical a manner as possible, these biological replicates were performed on separate days with separate batches of liquid cultures. We use these replicates to convince ourselves that the pattern of the enrichment (position and existence of peaks or overall trends) was not merely an artifact. Regarding the increased variability at the GC extremes, this is an inherent component of the analysis: there are fewer points in each cell with these values, so the measurements are noisier. This is reflected in the size of the confidence interval. We have taken the reviewer’s suggestion and altered the titles of Figure 6—figure supplement 2, Figure 7—figure supplement 4, and Figure 10—figure supplement 2, but contend that the existence and location of peaks and troughs in the enrichment profile are robust across the biological replicates.

Regarding error bars and statistical analysis of variability, we are confused by what the reviewer requests that we plot by the description “standard deviations to the mean.” We assume here that they are requesting a plot of the standard error of the mean as calculated by the standard deviation of the data divided by the square root of the number of samples. Our response is based on that assumption and we apologize if the reviewer had intended something else. Choosing appropriate error bars is discussed in detail in “Error bars in experimental biology” 10.1083/jcb.200611141. We would be happy to consider alternate ways of presenting the data and, historically, we have debated amongst ourselves and with other reviewers about the most appropriate way of displaying the data. We have chosen to represent in the main text error bars that reflect the precision of our measurement, and not the variability across different cells. We additionally provide supplemental figures to allow the reader to assess the variability in these average values across different biological replicates. Two reasons why we have chosen to avoid the ‘standard error of the mean’ approach and have instead chosen the bootstrap CI is (1) the SEM technique ignores within-cell correlations in the data, and (2) the SEM method assumes the data are normally distributed, an assumption that is not valid for our data. For a further discussion of CI, SEM and STD, please see “Misuse of standard error of the mean (sem) when reporting variability of a sample. A critical evaluation of four anaesthesia journals” https://doi.org/10.1093/bja/aeg087. That being said, if the reviewer is requesting a standard error of the mean plot, they are incorrect that “Plotting standard deviations to the mean (not the variations between replicates but between all the pooled values) would certainly be less impressive.” Because of the large number of cells in each sample, such SEM bars are thinner than the solid lines.

Wording changes:

“As a tool to facilitate understanding and interpretation of these relative enrichment plots, we generated a synthetic cell surface with the same geometric properties as the average wild-type cell (Figure 3), applied a variety of example intensity distributions, and generated curvature enrichment plots. […] The key features of interest are the overall increases, decreases, and peaks in the curves, along with the curvatures at which these occur.”

Figure 6 legend “(A) The calculation of relative concentration for a specific probe involves two steps of normalization. […] In the experimental data presented in the main text, the single cell relative concentration profile is averaged over hundreds of cells, each with their own unique geometry.”

“Biological replicates are shown in Figure 6—figure supplement 2.”

“Figure 6—figure supplement 2. Curvature enrichment analysis of biological replicates of MurNAc-alk-, D-Ala-alk-, and mock-labeling.”

“Biological replicates are shown in Figure 7—figure supplement 4.”

“Figure 7—figure supplement 4. Curvature enrichment analysis of biological replicates of MreB.”

“Biological replicates are shown in Figure 10—figure supplement 2A.”

“Figure 10—figure supplement 2. Curvature enrichment analysis of biological replicates of CcmA-FLAG.”

3) Of a lesser importance, but I have been puzzled by the labelling experiment: albeit the strategy is sound and obviously efficient (Figure 5), the authors certainly have a good reason not to use the much easier fluo-DAA labelling (developed by one of the co-authors, E. Kuru). Is it because the approach (modeling, 3D reconstruction) requires working on fixed cells and that implies postponing the labelling step? Or because they wanted to acquire all the data using an identical procedure for fair comparison (and fixed cells being imposed by immuno-fluorescence labelling of the proteins)? This reason for this strategy may be briefly mentioned in the corresponding result section.

Our preliminary experiments began with using various FDAAs with great success. However, we decided to use the D-Ala-alk for two reasons. (1) We can use brighter and more photostable fluorophores, which are superior for SIM and allow for more rapid image acquisition which adds up over the >1500 SIM images used to generate this manuscript. (2) D-Ala-alk is far more cost effective and easy to obtain in large quantities commercially, allowing us to use the same probe for both the peptidoglycan mass spectrometry experiments and microscopy experiments. We do not find this information to be sufficiently relevant to merit mention in the text of the manuscript.

4) The authors claim that MreB is enriched at negative Gc (and excluded from poles). Again, and for the same reasons mentioned in point 2, the results are not as strong as the authors are trying to convince us ("highly reproducible" in subsection “MreB is enriched at negative Gaussian curvature” and Figure 7—figure supplement 3 title). Although the reproducibility seems better here than for the PG localization (but again the SD to the mean seems more appropriate than the bootstrap) there is still a large experiment to experiment variability in enrichment for Gc below -7 and above +7 (Figure 7—figure supplement 3). Once the poles, clearly depleted of MreB as reported previously in the literature for other bacteria, and the highly variable areas (for higher +/- Gc) are removed, the trends of MreB accumulation is weak (+/-10%). Surprisingly, another monotonically increasing concentration (this time with increasing Gc) is reported, presenting an enrichment similar to that of MreB (+/-10%; Figure 10—figure supplement 1): the negative control (no Flag) of Figure 10. This control is nonetheless described as showing "negligible curvature preference", suggesting that a +/-10% enrichment is not significant. Thus, MreB is not significantly enriched either.

We refer to the thorough discussion of reviewer 3’s item #2 to address the concerns about the credibility of the enrichment of MreB, the inapplicability of interpreting the data as +/- 10%, and the variation at extreme GC values. We realize that our phrasing in the manuscript has once again caused confusion and have modified the language to clarify. The negative control has negligible curvature preference relative to the corresponding signal of interest (PG synthesis, CcmA, MreB). We acknowledge that there is actually a “characteristic” enrichment pattern for each negative control. This is most clear for the anti-CcmA preimmune serum, and it is unsurprising that antibodies in the serum might recognize something with a preference for specific GC values. In fact, this signal even contributes to the overall anti-CcmA curvature enrichment plot, as seen by the increase at negative GC that is not seen in the anti-FLAG CcmA-FLAG plots nor in the anti-FLAG WT control. In fact, if anything, the anti-FLAG and preimmune serum plots vs. the anti-FLAG plots strengthen our conclusions about the relative enrichment of PG synthesis signal at negative GC, as one can see the actual contribution of added signal at negative GC with the presence of an enriched signal at the major axis area to the shape of the enrichment plot.

The reviewer’s comments about comparing the negative control to the actual signal led us to further quantify the total signal in different conditions. To that end, we now provide histograms showing the amount of signal for both the negative control and the signal of interest (Figure 6—figure supplement 2B, Figure 7—figure supplement 4B, Figure 10—figure supplement 2B and 3B, and Figure 11—figure supplement 1C). We also have added text to discuss the negative controls in more detail.

Wording changes:

“The mock labeling control showed minimal curvature bias and is on average 3.6% of the D-ala-alk signal and 4.5% of the MurNAc-alk signal (Figure 6B and C, gray and Figure 6—figure supplement 2B).”

“Preimmune serum signal was 36.4% of the MreB signal (Figure 7—figure supplement 4B), but did not show a curvature preference (Figure 7E, gray)”

“Immunostaining with CcmA preimmune serum showed some background signal in the interior of wild-type and mutant cells (Figure 9 and Figure 9—figure supplement 2).”

“The wild-type (no FLAG) negative control was 28.9% of the CcmA-FLAG signal (Figure 10—figure supplement 2B). While the negative control showed a small peak at 5 µm-2, the magnitude of the CcmA-FLAG peak was far greater (Figure 10A and supplement 1A).”

“Preimmune signal was 33.0% of the anti-CcmA signal in wild-type (Figure 10—figure supplement 3).”

“Preimmune signal was 50.6% and 26.7% of anti-CcmA signal in I55A and L110S, respectively (Figure 10—figure supplement 3B).”

5) The authors suggest that MreB is playing an active role in CW synthesis. Although this would not surprise most of the MreB crowd, this hypothesis is contradicting previous findings from Waidner et al., 2009, claiming that MreB is not essential and not required for H. pylori CW synthesis and shape (in fairness, a point mentioned by the authors). If the authors want to claim a role for MreB in CW control in the present study, they need to address this discrepancy (request strains from PLG, or make depletion strains, or show an effect of A22 on cell shape). As an alternative, considering the weakness of their evidence on this topic, they may prefer to dampen their conclusions, remove MreB from the model, and refocus their study toward CcmA.

We have addressed these comments in our response to reviewer #2, item 4.

6) In their model, MreB increased frequency of localization at negative Gc should translated into increased synthesis along the minor helical axis, which would exactly counteract the maintenance of the helical shape by trying to restore the rod shape. In such a model, while it is easy to imagine that CcmA promote synthesis and deformation along the major helical axis, it is really unclear how the small axis would be maintained and why MreB would fail to restore the rod. Thus, in addition to be lightly supported by their data, this hypothesis does not enlighten our understanding of the building of H. pylori helical shape.

We appreciate that the reviewer understands our model that the MreB-based enrichment of synthesis at the minor axis area would, excluding other contributors, result in loss of helical shape and formation of a straight rod, as we illustrate in the top portion of the “helical shape maintenance” model in Figure 11C. We refer the reviewer to the bottom portion of the “helical shape maintenance” model for our proposed model that CcmA-promoted PG synthesis at the major helical axis counterbalances the MreB-patterned PG synthesis at negative Gaussian curvature. We do not claim that synthesis patterning is the only possible contributing mechanism, but we do propose and support with our data that MreB- and CcmA-biased PG insertion is one of the contributing mechanisms, and is in fact to date the best-described mechanism for H. pylori cell shape maintenance and the first mechanism showing where changes to the cell wall necessary for helical shape are occurring. Moreover, we present a novel mechanism for curvature maintenance; the two other proposed mechanisms for curvature maintenance demonstrate that a cell-spanning skeletal filament (CreS in Caulobacter crescentus and CrvA in Vibrio cholerae) on the minor axis/negative GC yields increased synthesis on the opposite face of the cell. Here, we show a new mechanism for promoting curvature: the cytoskeletal protein CcmA forms puncta at the major axis/positive GC and promotes synthesis at that face of the cell. As such, this paper is a marked step forward for understanding strategies for how cells can achieve a given shape.

[Editors’ note: what follows is the authors’ response to the second round of review.]

Overall the authors went to extreme length to answer the concerns raised by the previous reviewers in a convincing way. There are nevertheless key interpretations issues that would need be addressed before the work can be accepted for publication:

1) The paper now shows convincingly that MreB is essential for growth of H. pylori, which likely is linked to its function in PG synthesis. The model proposes that the recognition of regions of negative curvature by MreB and positive curvature by CcmA underlies the helicity of the H. pylori cell. However, although it is understood that these mechanisms are likely contributing mechanisms, the authors still do not discuss clearly why the action of MreB acting at negative gaussian curvature does not counteract helicity toward Rod restoration. This question is especially important given that a study by Wollrab et al. (bioRxiv 716407; doi:10.1101/716407) questions the MreB negative curvature recognition in E. coli cells. If MreB is indeed localizing to -GC regions in the Helicobacter cell, how do the authors reconcile this with the Wollrab et al. study?

Thank you for pointing out this interesting preprint. The study by Wollrab et al. has two major observations that conflict with current wording and/or observations in our manuscript.

The first observation relates to the role of MreB in localizing the Rod PG synthesis complex. Previous published data highlights roles for MreB in E. coli and B. subtilis in promoting circumferential motion of the Rod complex and relative enrichment at negative Gaussian curvature. There is much debate in the field about the mechanisms driving the curvature localization bias of MreB and the Rod complex. Recent literature has invoked curvature-based constraints on MreB assembly dynamics or membrane interactions (Wang and Wingreen, 2013, Biophys J; Quint et al., 2016, Biophys J; Wong et al., 2019, eLife). The Wollrab study provides convincing data that in E. coli the PG transpeptidase binds PBP2 binds some cell envelope feature (likely cell wall) and a subset of PBP2 binding events show simultaneous or subsequent recruitment of MreB and other Rod complex members resulting in persistent circumferential motion. At several points in our manuscript we summarized prior literature as suggesting that MreB recruits side wall PG synthesis complexes. Given these new data, it seems more likely that rather than initiating Rod complex assembly, MreB plays roles in further assembly or activation of the Rod complex and in promoting or enhancing the persistent circumferential motion of Rod complexes. To accommodate this new understanding we have changed “help recruit” to “help maintain”. At other points in the manuscript we use the wording “helps direct”, which we think remains consistent with the available data. We have changed “recruits” to “helps to pattern” and changed “MreB-driven” to “MreB-associated”. To clarify that static measurements of enrichment at negative Gaussian curvature are not necessarily in conflict with observations that MreB tends to move circumferentially about the cell, we added “which may result from circumferential motion about the cell.”

The second observation relates to MreB enrichment at negative Gaussian curvature. The Wollrab study concludes that MreB does not have a curvature localization bias because curvature correlations are diminished if they remove cell poles (which have very high curvatures) and center line cell bends from their analysis. In our study, we retain a curvature localization bias for MreB even when we exclude the cell poles. Given the significant contribution of the poles to curvature, as discussed in the Wollrab study, we updated Figure 11 and supplements and now present the sidewall only plots in the main figure and moved the whole cell surface plots to the supplement. The Wollrab manuscript also raises concerns about bending induced by placing cells on an agarose pad and the influence this could have on MreB curvature enrichment. As we fix cells in liquid culture and then gently deposit cells on coverslips after fixation, these concerns do not apply to our experiments. Even with excluding the poles, we still see an enrichment of MreB at negative curvature in both wild-type and ΔccmA cells. The different observations by our two studies could result from the fact that unlike E. coli, H. pylori sidewalls retain a much larger range of curvatures in wild-type cells and/or the fact that these two studies use different methods to measure curvature. The Wollrab study infers surface curvature from 2D cell outline contour curvature measurements while our study used 3D reconstructions of cell surfaces to measure curvature. Given these major differences in the experimental systems and analysis methods, we do not feel it is appropriate to directly comment on the Wollrab observations.

We do agree with the comment that the action of MreB at negative Gaussian curvature would be expected to counteract helicity toward rod restoration if there are not other contributors to cell wall patterning. As already discussed in the manuscript (and depicted the model in Figure 11), we suggest that CcmA promotion of synthesis at positive curvature may be one mechanism that counters the hypothesized rod-restoration activity of MreB. As pointed out in the paragraph, a major outstanding question is how CcmA or MreB promotes cell wall synthesis in H. pylori and will require further experiments beyond the scope of this paper. We also already specifically call out unexpected and counterintuitive implications of our finding of higher relative synthesis at both negative and positive curvatures. As indicated in this paragraph we think it likely that incorporation of measurements of PG turnover and changes in PG architecture will be necessary to resolve the new questions raised by our study.

To help further clarify our model, we made the following text updates:

“To be able to maintain curvature in the presence of MreB, the curved-rod shaped Gram-negative Proteobacteria Caulobacter crescentus and Vibrio cholerae appear to limit relative levels of PG synthesis at negative curvatures through the action of long, cell-spanning cytoskeletal filaments (CreS and CrvA) that preferentially localize to the minor axis (negative Gaussian curvature) and enable cells to increase relative synthesis rates on the opposite side of the wall (positive Gaussian curvature) (Bartlett et al., 2017; Cabeen et al., 2009).”

H. pylori leverages the bactofilin CcmA, which localizes preferentially to the major helical axis area, to promote synthesis at positive Gaussian curvatures on the sidewall, and supplements the MreB associated enhanced synthesis that is enriched at negative Gaussian curvatures (the minor helical axis) (Figure 11A, right). Adding the contribution of CcmA to the PG synthesis patterning allows H. pylori to maintain curvatures in the presence of MreB-associated PG synthesis.”

“Overall, our results are consistent with a model in which MreB-patterned straight-rod shape is the default pattern for H. pylori cells and helical shape is facilitated by adding major axis area PG synthesis via CcmA to augment straight-rod cell wall patterning.”

“Furthermore, our labeling strategy allowed us to determine the curvature bias of new PG insertion, but spatially-regulated turnover of old PG may also contribute to cell wall homeostasis.

The authors seem to rule out mutual exclusion mechanisms (i.e. MreB occupies cylindrical parts that CcmA does not occupy) because in subsection “CcmA localization to positive curvature correlates with cell wall synthesis, CcmA polymerization, and

helical cell shape” they conclude that MreB localization is the same in ∆ccmA as in WT. However, there appears to be a difference wherein the relative concentration increases more for MreB at negative curvatures in ∆ccmA than it does in WT and (as the authors actually note later in that section) there is a slight peak of MreB enrichment at ~3 µm-2 in the mutant but not in WT. There also appears to be a dip in concentration around 10-15 µm-2. Importantly, the differences in MreB distribution look to be of a similar magnitude as the differences in D-Ala-alk incorporation between WT and ∆ccmA but the D-Ala-alk incorporation pattern is described as different.

At present our data do not support or rule out mutual exclusion mechanisms because we did not do colocalization studies. Our data indicate that both MreB and CcmA can occupy the same curvatures although at different frequencies. Interpreting the nuanced features of the MreB enrichment curve in ΔccmA is complicated by the non-uniform enrichment of preimmune signal. The relative concentration of MurNAc-alk and D-Ala-alk signal at negative Gaussian curvature is markedly higher in ΔccmA cells as compared to wild-type. As the plots show relative signal enrichment, this is consistent with the observed marked reduction in enrichment at positive Gaussian curvature. Now that we are able to include the MurNAc-alk data, the difference between labeling patterns in wild-type and ΔccmA are even more pronounced. These data are consistent with MreB-patterned synthesis playing a dominant role in the overall distribution of PG synthesis, with some minimal enhanced synthesis at positive Gaussian curvature not patterned by CcmA. We have updated the text to clarify and expand upon these points in the Discussion:

“MreB curvature preference appears largely similar in both wild-type (HJH1, amgK murU, light pink) and ΔccmA with poles excluded (JTH6, amgK murU ΔccmA, dark pink) (Figure 11A). […] There is a small peak for MreB at approximately 3 µm-2, however interpretation of the MreB peak is complicated by the presence of a peak at the same curvature range for the preimmune signal.”

“Furthermore, we demonstrate that in the absence of CcmA, similarly to in wild-type, MreB is still enriched at negative Gaussian curvature, but that MurNAc-alk and D-Ala-alk synthesis patterning shift to more closely resemble the MreB curvature enrichment profile[…] CcmA is one of a suite of proteins required for helical cell shape maintenance; it is possible that other cell shape proteins can influence PG synthesis to promote some limited curvature in the absence of CcmA, consistent with multiple complementary mechanisms being required for helical shape maintenance.”

2) For the biochemical characterization of CcmA and mutant forms of CcmA, it would be important to note in the text (not just the Materials and methods) that the proteins are purified in denatured form and refolded to induce/monitor polymer formation. This is important because it does somewhat complicate interpretation of the mutants. It could be that the I55A or L110S mutations influence the ability of CcmA to fold properly during renaturation, rather than having a direct effect on polymerization capacity. The punctate cytoplasmic signal by IF could be consistent with either effect.

WT CcmA, CcmAI55A,and CcmAL110S were not purified under denaturing conditions. While the elution buffer used contains 2 M urea, this is not a sufficient concentration of urea to denature CcmA. In the elution buffer directly off of the column, WT CcmA and CcmAL110S form individual filaments as well as bundles of filaments when visualized by transmission electron microscopy (TEM) and negative staining while CcmAI55A does not form any detectable structures (Author response image 1 A-C and magnified insets). These data suggest that when we purify WT CcmA and CcmAL100S in the presence of 2 M urea the proteins are already folded and spontaneously polymerize. Thus, refolding is not occurring when the proteins are dialyzed against our 25 mM Tris pH 8 buffer used in our experiments in the manuscript because the proteins are already folded.

Author response image 1

In another set of experiments not included in this manuscript we found that 8 M urea is sufficient to denature WT CcmA and CcmAL110S and reduce all filament, bundle, and lattice structures observable by TEM and negative staining (Figure 1 D, E). Our lab is continuing to characterize CcmA in ongoing projects and have analyzed the structure of WT CcmA by Circular Dichroism spectroscopy. These data indicate that WT CcmA forms the predicted beta helix structure and can rapidly refold after heat denaturation.

Related to this, the authors say that the mutants "failed to form any higher order structure under any buffer condition tested". What conditions were tested?

We tested polymerization of WT CcmA, CcmAI55A, and CcmAL110S in five different buffers chosen based on other published studies of bactofilins. WT CcmA and CcmAL110S show changes in the fraction of filaments observed in larger bundles based on pH but both proteins form filaments in all five buffers. CcmAI55A was unable to form any filaments, bundles, or lattices detectable by TEM and negative staining in any of the buffers. The buffers we have tested are:

· Elution buffer: 25 mM tris pH 8, 2 M urea, 500 mM NaCl, 2% glycerol, 250 mM imidazole

· 25 mM tris pH 8, 20 mM glycine (Zuckerman et al., 2015, PLoS One)

· 25 mM tris pH 8 (Vasa et al., 2014, PNAS)

· 25 mM CAPS pH 11 (similar to a buffer used in Deng et al., 2019, Nature Microbiology)

Did they test whether the mutants that cannot polymerize were soluble when purified?

As explained in the Materials and methods section of the manuscript, to purify CcmA we overexpressed polyhistidine-tagged versions of WT CcmA, CcmAL110S, or CcmAI55A, in E. coli, lysed the cells by sonication, and purified CcmA using nickel affinity resin from the soluble fraction of the sonicated cells. Each time we performed this protocol we recovered very similar yields of WT CcmA, CcmAL110S and CcmAI55A (see SDS-PAGE gel of purified proteins in Figure 8—figure supplement 1D).

https://doi.org/10.7554/eLife.52482.sa2

Article and author information

Author details

  1. Jennifer A Taylor

    1. Department of Microbiology, University of Washington, Seattle, United States
    2. Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
  2. Benjamin P Bratton

    1. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
    2. Department of Molecular Biology, Princeton University, Princeton, United States
    Contribution
    Conceptualization, Software, Formal analysis, Funding acquisition, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1128-2560
  3. Sophie R Sichel

    1. Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    2. Molecular Medicine and Mechanisms of Disease Graduate Program, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology
    Competing interests
    No competing interests declared
  4. Kris M Blair

    1. Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    2. Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Funding acquisition, Investigation, Methodology
    Competing interests
    No competing interests declared
  5. Holly M Jacobs

    1. Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    2. Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, United States
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
  6. Kristen E DeMeester

    Department of Chemistry and Biochemistry, University of Delaware, Newark, United States
    Contribution
    Resources, Funding acquisition, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
  7. Erkin Kuru

    Department of Genetics, Harvard Medical School, Boston, United States
    Contribution
    Conceptualization, Methodology
    Competing interests
    No competing interests declared
  8. Joe Gray

    Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Formal analysis, Investigation, Visualization, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2338-0301
  9. Jacob Biboy

    Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1286-6851
  10. Michael S VanNieuwenhze

    Department of Chemistry, Indiana University, Bloomington, United States
    Contribution
    Supervision, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
  11. Waldemar Vollmer

    Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
    Contribution
    Formal analysis, Supervision, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
  12. Catherine L Grimes

    1. Department of Chemistry and Biochemistry, University of Delaware, Newark, United States
    2. Department of Biological Sciences, University of Delaware, Newark, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
  13. Joshua W Shaevitz

    1. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, United States
    2. Department of Physics, Princeton University, Princeton, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8809-4723
  14. Nina R Salama

    1. Department of Microbiology, University of Washington, Seattle, United States
    2. Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, United States
    3. Molecular Medicine and Mechanisms of Disease Graduate Program, University of Washington, Seattle, United States
    4. Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration
    For correspondence
    nsalama@fhcrc.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2762-1424

Funding

National Institutes of Health (R01 AI136946)

  • Nina R Salama

National Institutes of Health (U01 CA221230)

  • Catherine L Grimes
  • Nina R Salama

National Institutes of Health (T32 CA009657)

  • Kris M Blair

National Institutes of Health (T32 GM95421)

  • Sophie R Sichel

National Institutes of Health (T32 GM008550)

  • Kristen E DeMeester

National Institutes of Health (P30 CA015704)

  • Nina R Salama

National Center for Research Resources (Stanford Imaging Award Number 1S10OD01227601)

  • Nina R Salama

Wellcome (101824/Z/13/Z)

  • Waldemar Vollmer

National Science Foundation (DGE-0718124)

  • Jennifer A Taylor

National Science Foundation (DGE-1256082)

  • Jennifer A Taylor
  • Kris M Blair

Department of Defense (National Defense Science & Engineering Graduate Fellowship (NDSEG))

  • Jennifer A Taylor

Graduate Opportunities and Minority Achievement Program (Graduate Opportunity Program Research Assistantship Award)

  • Sophie R Sichel

National Science Foundation (PHY-1734030)

  • Benjamin P Bratton
  • Joshua W Shaevitz

Glenn Centers for Aging Research

  • Benjamin P Bratton

National Institutes of Health (R21 AI121828)

  • Benjamin P Bratton
  • Joshua W Shaevitz

National Institutes of Health (GM113172)

  • Michael S VanNieuwenhze

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The opinions, findings, and conclusions or recommendations expressed in this material contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCRR, the National Institutes of Health, the Department of Defense, or the National Science Foundation.

Acknowledgements

This research was supported in part by US National Institutes of Health R01 AI136946 (NRS), U01 CA221230 (CLG and NRS), T32 CA009657 (KMB), T32 GM95421 (SRS), T32 GM008550 (KED), GM113172 (MSV), the FHCRC Cellular Imaging and Genomics and Bioinformatics Shared Resources of the NCI Center Support Grant P30 CA015704, the Stanford Imaging Award Number 1S10OD01227601 from the National Center for Research Resources (NCRR), the Life Sciences Research Foundation (EK), and the Wellcome Trust grant 101824/Z/13/Z (WV).This work was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-0718124 (JAT) and DGE-1256082 (JAT and KMB), the Department of Defense (DoD) through the National Defense Science and Engineering Graduate Fellowship (NDSEG) Program (JAT), and the GO-MAP Graduate Opportunity Program Research Assistantship Award (GOP Award) (SRS). This work was supported by National Science Foundation PHY-1734030 (BPB and JWS), the Glenn Centers for Aging Research (BPB), and National Institutes of Health NIH R21 AI121828 (BPB and JWS).

The opinions, findings, and conclusions or recommendations expressed in this material contents are solely the responsibility of the authors and do not necessarily represent the official views of the NCRR, the National Institutes of Health, the Department of Defense, or the National Science Foundation. The authors have no conflicts of interest to report.

We would like to thank Laura Sycuro, Desirée Yang, and Irina Mavrodi for strain construction; Dr. Cintia Santiago for assistance with NAM purification and compound shipment; Patrina Pellett (GE Healthcare) for assistance with OMX imaging; the David Baker Lab (University of Washington) for OMX access and use; and Sloan Siegrist (University of Massachusetts Amhurst) for the D-alanine-D-alanine-alkyne and D-alanine-alkyne-D-alanine reagents. We kindly thank Dr. Hong Wu and Dr. Kouichi Sano (Osaka Medical College) for the anti-MreB and corresponding preimmune sera used in this study; Anson Chan (University of British Columbia) for consultation on CcmA mutant design; and Zachary Jones for assistance in synthesizing the MurNAc sugars.

Senior Editor

  1. Anna Akhmanova, Utrecht University, Netherlands

Reviewing Editor

  1. Tâm Mignot, CNRS-Aix Marseille University, France

Publication history

  1. Received: October 5, 2019
  2. Accepted: January 7, 2020
  3. Accepted Manuscript published: January 9, 2020 (version 1)
  4. Version of Record published: February 11, 2020 (version 2)

Copyright

© 2020, Taylor et al.

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

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