Is e coli motile

Is e coli motile DEFAULT

From swimming to swarming: Escherichia coli cell motility in two-dimensions&#x;

1. Berg HC. Random Walks in Biology. Princeton University Press; Princeton: [Google Scholar]

2. Berg HC. E coli in Motion. Springer; New York, NY, USA: [Google Scholar]

3. Purcell EM. Am J Phys. ;[Google Scholar]

4. Renner LD, Weibel DB. MRS Bull. ;[PMC free article] [PubMed] [Google Scholar]

5. Copeland MF, Weibel DB. Soft Matter. ;[PMC free article] [PubMed] [Google Scholar]

6. Kearns DB. Nat Rev Microbiol. ;[PMC free article] [PubMed] [Google Scholar]

7. Harshey RM, Matsuyama T. Proc Natl Acad Sci U S A. ;[PMC free article] [PubMed] [Google Scholar]

8. Jones B, Young R, Mahenthiralingam E, Stickler DJ. Infect Immun. ;[PMC free article] [PubMed] [Google Scholar]

9. Tuson HH, Copeland MF, Carey S, Sacotte R, Weibel DB. J Bacteriol. ;[PMC free article] [PubMed] [Google Scholar]

Darnton NC, Turner L, Rojevsky S, Berg HC. Biophys J. ;[PMC free article] [PubMed] [Google Scholar]

Turner L, Zhang R, Darnton NC, Berg HC. J Bacteriol. ;[PMC free article] [PubMed] [Google Scholar]

Copeland MF, Flickinger ST, Tuson HH, Weibel DB. Appl Environ Microbiol. ;[PMC free article] [PubMed] [Google Scholar]

Sokolov A, Aranson IS, Kessler JO, Goldstein RE. Phys Rev Lett. ; [PubMed] [Google Scholar]

Wu XL, Libchaber A. Phys Rev Lett. ; [PubMed] [Google Scholar]

Wu Y, Berg HC. Proc Natl Acad Sci U S A. ;[PMC free article] [PubMed] [Google Scholar]

Maki N, Gestwicki JE, Lake EM, Kiessling LL, Adler J. J Bacteriol. ;[PMC free article] [PubMed] [Google Scholar]

Wolfe AJ, Conley MP, Berg HC. Proc Natl Acad Sci U S A. ;[PMC free article] [PubMed] [Google Scholar]

Xia Y, Whitesides GM. Angew Chem, Int Ed. ; [PubMed] [Google Scholar]

http://rsbweb.nih.gov/ij/.

Fauvart M, Phillips P, Bachaspatimayum D, Verstraeten N, Fransaer J, Michiels J, Vermant J. Soft Matter. ;[Google Scholar]

Lemelle L, Palierne JF, Chartre E, Place C. J Bacteriol. ;[PMC free article] [PubMed] [Google Scholar]

Zhang R, Turner L, Berg HC. Proc Natl Acad Sci U S A. ;[PMC free article] [PubMed] [Google Scholar]

Li G, Tang JX. Phys Rev Lett. ;[PMC free article] [PubMed] [Google Scholar]

Li G, Bensson J, Nisimova L, Munger D, Mahautmr P, Tang JX, Maxey MR, Brun YV. Phys Rev E. ; [PubMed] [Google Scholar]

Berke AP, Turner L, Berg HC, Lauga E. Phys Rev Lett. ; [PubMed] [Google Scholar]

DiLuzio WR, Turner L, Mayer M, Gartecki P, Weibel DB, Berg HC, Whitesides GM. Nature. ; [PubMed] [Google Scholar]

Berg HC, Turner L. Biophys J. ;[PMC free article] [PubMed] [Google Scholar]

Lauga E, DiLuzio WR, Whiteside GM, Stone HA. Biophys J. ;[PMC free article] [PubMed] [Google Scholar]

Li G, Tam LK, Tang JX. Proc Natl Acad Sci U S A. ;[PMC free article] [PubMed] [Google Scholar]

Berg HC. Curr Biol. ;R [PubMed] [Google Scholar]

Vigeant MAS, Wagner M, Tamm LK, Ford RM. Langmuir. ;[Google Scholar]

Drescher K, Dunkel J, Cisneros LH, Ganguly S, Goldstein RE. Proc Natl Acad Sci U S A. ;[PMC free article] [PubMed] [Google Scholar]

Binz M, Lee AP, Edwards C, Nicolau DV. Microelectron Eng. ;[Google Scholar]

Aranson IS, Sokolov A, Kessler JO, Goldstein RE. Phys Rev E. ; [PubMed] [Google Scholar]

Ishikawa T, Sekiya G, Imai Y, Yamaguchi T. Biophys J. ;[PMC free article] [PubMed] [Google Scholar]

Cisneros L, Dombrowski C, Golstein RE, Kessler JO. Phys Rev E. ; [PubMed] [Google Scholar]

Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC/

The role of motility and chemotaxis in the bacterial colonization of protected surfaces

Abstract

Internal epithelial surfaces in humans are both oxygenated and physically protected by a few hundred microns thick hydrogel mucosal layer, conditions that might support bacterial aerotaxis. However, the potential role of aerotaxis in crossing such a thin hydrogel layer is not clear. Here, we used a new setup to study the potential role of motility and chemotaxis in the bacterial colonization of surfaces covered by a thin hydrogel layer and subjected to a vertical oxygen gradient. Using the bacterium Escherichia coli, we show that both non-motile and motile-but-non-chemotactic bacteria could barely reach the surface. However, an acquired mutation in the non-chemotactic bacteria that altered their inherent swimming behavior led to a critical enhancement of surface colonization. Most chemotactic strains accumulated within the bulk of the hydrogel layer, except for the MG strain, which showed a unique tendency to accumulate directly at the oxygenated surface and thus exhibited distinctly enhanced colonization. Even after a long period of bacterial growth, non-motile bacteria could not colonize the hydrogel. Thus, switching motility, which can be spontaneously acquired or altered in vivo, is critical for the colonization of such protected surfaces, whereas aerotaxis capacity clearly expedites surface colonization and can lead to diverse colonization patterns.

Introduction

A hydrogel layer with a thickness of a few hundred micrometers covers most of the internal epithelial surfaces in humans1,2,3. This hydrogel consists of cross-linked mucin molecules and provides the first line of defense against bacterial invasion. Several cases were reported in which bacterial motility or chemotaxis could affect bacterial infection4,5,6,7,8. In particular, it was suggested that because the epithelial surfaces are oxygenated by the underlying bloodstream, an oxygen gradient is maintained across the mucin layer that can be exploited by bacterial cells for oxygen-driven taxis9,10,11 (aerotaxis), which might expedite the infection process. Clearly, the hydrogel layer can generally inhibit the otherwise free access of the bacterial cells to the surface. However, although the thickness of the layer is much larger than the size of individual bacteria, it is comparable to the size of a single bacterial colony, potentially making the role of bacterial growth more significant in this context. In addition, bacterial diffusion and active random walk are normally negligible over large distances; however, these factors could potentially play more significant role over the short distances and long time scales that are typical in this case. Finally, previous studies indicated that bacterial aerotaxis might actually inhibit the bacterial population from getting close to oxygen-rich surfaces12. Thus, given the intermediate thickness of this layer, the role of bacterial taxis capacity is not a priori clear in this context.

Many quantitative studies of bacterial chemotaxis have been conducted using a variety of behavioral assays13,14,15,16,17,18,19. However, none of these assays capture the basic constraints imposed by the mucosal layer: a wide-area hydrogel barrier, an intermediate thickness of the hydrogel and an oxygen gradient that is maintained vertically across the layer. In addition, although the ability of bacterial cells to colonize both biotic and abiotic surfaces has been studied in various in vitro setups, most of these studies lack a physical barrier between the bacteria and the surface. Clearly, under such conditions, bacterial motility or chemotaxis capacity might play a fundamentally different role.

To study the role of bacterial motility and chemotaxis in the colonization of protected surfaces, we focused on the bacterium Escherichia coli, a common resident of the human gut20,21, whose chemotaxis system has been well studied at the molecular and behavioral levels. E. coli propels itself by rotation of long helical flagellar filaments that extend from its outer membrane and are powered by proton-driven motors22. If all of the flagella turn in a direction defined as counter-clockwise(CCW), they form a compact single bundle that propels the cell forward; however, if one or more flagella switch to the opposite direction (clockwise, CW), that flagellum leaves the bundle and the cell switches its swimming direction. These two modes of swimming, referred to as ‘run’ and ‘tumble’, respectively, constitute an active random walk. Similar to all motile bacteria, E. coli is also equipped with a sensory system that detects external chemical changes along the bacterial swimming trajectory and guides the bacterium along chemical gradients, a behavior known as chemotaxis23. This sensory system consists of four types of MCP chemoreceptors with various sensing specificities and an additional MCP-like Aer receptor24. These chemoreceptors activate and regulate an associated histidine kinase (CheA), which in turn donates phosphoryl groups to a cytoplasmic response regulator CheY. A dedicated phosphatase CheZ removes the phosphoryl groups from CheY, thus allowing the intracellular level of phospho-CheY to rapidly follow changes in the external environment. The binding of phospho-CheY to the base of the flagellar motor biases its rotation towards CW rotation and thus promotes switching of the bacterial swimming direction. Sensory adaptation mediated by methylation or demethylation of the receptors by CheR and CheB, respectively, allows for time-resolved comparison of ligand concentrations and extends the dynamic range of the responses.

The capacity of E. coli cells to navigate along oxygen gradients (aerotaxis) relies on the Aer receptor, which detects the redox potential across the cytoplasmic membrane, as does the Tsr receptor12,24,25. However, whether E. coli seeks specific oxygen levels26,27 or generally seeks the highest level possible28 is still a topic of debate. The molecular mechanism of aerotaxis behavior is also not fully understood because the Aer receptor lacks methylation sites and thus is not subjected to the conventional adaptation mechanism29,30.

In this work, we report a new chemotaxis setup used to study the contribution of bacterial motility and chemotaxis to the ability of bacterial cells to colonize surfaces protected by a thin hydrogel layer subjected to a vertical oxygen gradient across the layer. Using this setup, we tested the capacity of different E. coli strains, including strains with specific motility or chemotaxis properties, to populate the hydrogel layer and colonize the surface.

Results

Bacterial surface colonization setup: the MG strain as a test case

The setup used to measure bacterial surface colonization in this work is shown in Fig. 1A (see Materials and Methods for a detailed description). A hydrogel layer (% Bacto-agar) with a thickness of – μm was cast between an oxygen-permeable slide and a grid and subsequently placed in a titanium flow-chamber, which allowed for the continuous exchange of the medium above the gel. Given that the hydrogel layer is only a few hundred microns thick, diffusion of chemicals across the gel occurs on a time scale of several minutes and thus allows efficient exchange of the chemical environment in this layer. In addition, the hydrogel shields the cells in it from the flow and thus cells are only carried in the flow above the grid but not underneath. The bacteria to be tested were suspended in a standard ‘motility’ buffer, which optimally supports bacterial motility and chemotaxis but does not support bacterial growth13. To induce a low level of oxygen in the cell suspension, after growing the cells to mid-exponential phase, we concentrate them in motility buffer to OD=1. The bacterial suspension was then equilibrated in a closed tube and was drawn into the flow chamber from the bottom of the tube. Evidently, when such bacterial suspension was inserted into a capillary tube, cells at a distance larger than ~2 mm from the liquid/air interface did not swim, indicating that the oxygen level inside the cell suspension is very low31. Because oxygen enters through the oxygen-permeable surface and is consumed by the dense bacterial population in the flow chamber, an oxygen gradient forms across the hydrogel layer with a heightened oxygen level near the surface. However, the detailed oxygen profile is expected to be dynamically modified as the bacteria enter the hydrogel layer15,31. As shown below, the use of an oxygen permeable surface is evidently essential for observing surface colonization. The accumulation of cells at the surface and inside the gel layer was monitored using an inverted microscope with either transmitted light or fluorescence microscopy. Experiments were performed at 30 °C, a temperature commonly used in chemotaxis behavioral assays13.

Surface colonization assay.

(A) Schematic description of the setup. A thin hydrogel layer (% Bacto-agar in motility buffer) approximately  μm thick was cast between a silicon-based oxygen-permeable surface (Paragon-HDS; Dk =58) and a nylon grid (80 × 80 μm2 square). This structure was subsequently placed in a titanium flow-chamber held at 30 °C and the accumulation of bacterial cells was monitored using an inverted microscope. Also shown are sample images of the grid (right: transmission) and the surface (left: fluorescence). (B) Bacterial accumulation at the surface: MG cells in a standard surface-down orientation of the chamber (filled black symbols); MG cells in a surface-up orientation of the chamber (filled gray symbols); AVE3 (aer) cells (filled light gray symbols); non-motile UU or AVE5 cells (black bars). Data from 3–5 independent experiments of each strain are presented. The accumulation of MG cells near the surface was also tested with an oxygen-impermeable glass surface (open black symbols) or with a denser (%) hydrogel (open gray symbols). Data from two independent experiments of each are presented.

Full size image

As a test case, we first studied the behavior of the commonly used E. coli lab strain MG, which contains the insertion element IS1 upstream of the motility master regulator flhD. The accumulation dynamics of these cells on the surface are shown in Fig. 1B. Each experiment began by initiating the flow of the bacterial suspension through the chamber and the accumulation of cells at the surface was monitored over time as described in Materials and Methods. Bacterial cells could be detected at the surface 30–40 minutes after their introduction to the flow chamber and continued to accumulate over time (filled black symbols). In these experiments, as in most of the experiments reported in this work, the flow chamber was oriented with the oxygen-permeable surface pointing downward; however, similar bacterial accumulation was measured in experiments with the oxygen-permeable surface pointing upward (filled gray symbols).The bacterial cells could not reach the surface when the hydrogel density was increased from % to % (open gray symbols) or when non-motile bacteria were used (black bars). Next, we tested the importance of oxygen in the observed bacterial accumulation. First, we repeated the experiments with an oxygen-impermeable glass slide replacing the oxygen-permeable surface; in this case, no accumulation of cells could be detected (open black symbols). Second, we constructed a derivative strain of MG that lacks the primary oxygen sensor Aer (AVE3); the accumulation of these mutant cells was significantly reduced (light-gray symbols). The AVE3 cells showed normal chemotactic behavior in standard semi-solid agar plates (% Bacto-agar in TB), indicating that the chemotaxis capacity of the AVE3 strain is generally similar to that of the wild-type MG strain when aspartate or serine gradients drive the colony expansion (Fig. S1). These observations indicate that oxygen indeed plays a key role in the process of surface colonization. Thus, the accumulation of bacterial cells on the surface was insensitive to the orientation of the flow chamber, blocked by higher gel densities, required bacterial motility and critically depended on the presence of an oxygen-permeable surface and the Aer sensor.

Surface colonization by various E. coli strains

When tested in standard soft agar motility plates (see Materials and Methods), the colony expansion of commonly used E. coli lab strains varied widely (Fig. 2A). Because chemotaxis ability was previously shown to depend on insertion elements at the promoter region of the flhD operon32, we sequenced the flhD promoter regions of the specific strains used in this study and determined that the MG strain carries an IS1 element, the RP and W strains carry the IS5 element and the BW and EPEC strains lack an insertion element (Figs 2A and S2). When tested in the surface colonization setup, all of these strains showed similar surface colonization dynamics, with the exception of the MG strain, which showed a distinct behavior (Fig. 2B); the MG cells could be detected at the surface earlier and accumulated faster and to a higher level. Thus, under the conditions tested in this work, surface colonization is not sensitive to the variations among most strains except for the MG strain, which had a clear advantage.

Surface accumulation of different E. coli lab strains.

(A) Colony radius of the different strains used in this work in a semi-solid agar plate (% Bacto-agar in TB) after 8 hours at 30 °C, averaged over 3 experiments with error bars representing SD. Strains are also labeled with the insertion element (IS) type found upstream of their flhD gene (Fig. S2). (B) Surface colonization dynamics of the different chemotactic strains. Between 2 and 5 repetitions are shown of the experiment with each strain on different days. Typical phase contrast images of the surface taken after 4 hours of accumulation with either the MG or RP strain are also shown.

Full size image

To gain further insight into the unique behavior of the MG strain, we compared the dynamics of its bacterial distribution across the hydrogel with that of other strains, primarily the RP strain. Bacterial distribution was evaluated by acquiring a series of fluorescence images at different distances from the surface and at different time points. The fluorescence intensity profiles were obtained by averaging the related images at each time point (see Materials and Methods). Figure 3A shows four sets of profiles (two of each strain) illustrating the progression of bacterial distribution within the hydrogel layer. Clearly, although cells of strain MG had a strong tendency to accumulate directly at the oxygenated surface, cells of strain RP tended to accumulate within the bulk of the hydrogel layer. In the case of the MG strain, examples are shown for experiments performed with the oxygen permeable surface pointing either downward (right plot) or upward (left plot). Notably, the additional accumulation of the MG cells near the top of the hydrogel layer shown in the right-hand plot represents an accumulation of cells at the gel/flow interface; this accumulation was reduced when the flow-chamber was inverted. The profiles shown in Fig. 3A are intrinsically broadened by the optics. To extract more authentic distributions, we obtained intensity profiles from a defined thin bacterial layer and used this information to obtain more realistic distribution profiles using 1D-deconvolution (see Materials and Methods). The bacterial distributions obtained using this procedure are shown in Fig. 3B, further demonstrating the strong tendency of the MG strain to accumulate near the surface (black lines) and the tendency of the RP strain to accumulate within the bulk of the hydrogel layer (gray lines). Thus, the rapid and enhanced surface colonization of the MG strain shown in Fig. 2B clearly correlates with the strong tendency of these cells to accumulate directly at the oxygenated surface (see also Fig. S4).

Bacterial distribution across the hydrogel layer.

(A) Evolution of the fluorescence profile across the hydrogel layer (see Materials and Methods) for experiments with either the MG or RP strain; two independent experiments with each strain are shown. For the MG strain, profiles are shown for experiments conducted with the oxygen-permeable surface pointing downwards (right plot) or upwards (left plot). Each profile is labeled with the time elapsed since cells were applied to the flow chamber (in minutes). (B) Normalized cell distribution after  minutes for either MG cells (black lines) or RP cells (gray lines) after 1D de-convolution analysis (see Materials and Methods).The arrows represent the margins of the fit variability, mostly in the peak position for the MG strain and in the peak sharpness for the RP

Full size image

Because the unique behavior of the MG strain correlated with its unique insertion element (IS1) within the promoter region of the flhD operon32, we sought to test the possibility that the identity of the insertion element might be important. We replaced the IS5 element in the RP chromosome with the IS1 element from the MG strain, resulting in strain AVE1 (Fig. S3). However, as shown in Fig. 2B (open squares) and in Fig. S4, the behavior of strain AVE1 was generally more similar to that of the parental RP strain and clearly different from that of the MG strain. Thus, the IS1 insertion element by itself is clearly not responsible for the unique behavior of the MG strain. We also tested whether enhanced expression of the Aer sensor in the MG cells leads to their unique behavior. This possibility was tested by supplementing the RP with plasmid pSB20 carrying the aer gene under an inducible promoter24; however, the enhanced expression of the Aer sensor did not significantly affect surface colonization (Fig. S5). Thus, the unique behavior of the MG strain is not caused by its unique IS1 insertion element or by enhanced expression of the Aer sensor.

Analysis of motility and chemotaxis mutants

As shown in Figs 1 and 2, despite the fact that the hydrogel is, in principle, accessible to bacterial cells, non-motile bacteria were unable to reach the surface, thus indicating that bacterial motility is essential for surface colonization under these conditions. To further identify the essential bacterial properties required to promote surface colonization, we analyzed derivatives of the RP and MG strains that had specific motility and chemotaxis defects (Fig. 4A). The contribution of bacterial motility to surface colonization was tested using three motile but non-chemotactic strains; the UU and UU strains are derivatives of the RP strain that lack all known chemosensory receptors and the AVE2 strain is a derivative of the MG strain that lacks the response regulator CheY. As shown in Fig. 4A, these motile but non-chemotactic strains could barely reach the surface (open gray symbols), and, in fact, they exhibited a behavior comparable to that of the non-motile strains (black bars).

Motility and chemotaxis mutants.

(A) Surface accumulation of strains with specific motility/taxis defects: the wild-type RP strain (open black symbols); the receptorless UU and UU strains as well as the AVE2 cheY strain (open gray symbols); the AVE4 strain, a derivative of the UU strain that exhibits switching motility (filled gray symbols); and the non-motile UU strain (black bars). The dotted line represents the threshold density below which less than one cell is expected in a frame. Data from 2–4 independent experiments with each strain are presented. Note the break in the abscissa. (B) Averaged colony diameter (over 3 experiments) of the AVE4 strain in standard semi-solid agar plates (% Bacto-agar in TB) compared with that of its parental wild-type strain (RP) measured from images taken after hours at 30 °C. The dashed line represents the colony diameter of non-motile cells under the same conditions. (C) Number of switching events (tumble) per cell of free-swimming receptorless bacteria shown for different strains and extracted manually from short movies (50 successive frames with  s exposure time) recorded in motility medium. Approximately 50 cells (from 2–3 independent experiments) of each strain were analyzed. (D) Fraction of swimming bacterial cells counted in short movies (as in C) recorded from cells embedded in a bulk hydrogel. The data was averaged over 4 independent experiments in each approximately cells were analyzed per strain; error bars represents SD.

Full size image

However, these non-chemotactic strains not only lack sensing capacity but are also expected to exhibit smooth-swimming behavior instead of the ‘run-and-tumble’ behavior (switching motility) of the wild-type cells. To test the role of bacterial chemotaxis in surface colonization separate from its effect on the intrinsic swimming traits of the bacteria, we used an additional strain that was obtained as follows. Following Wolfe et al.33, we inoculated cells of strain UU (lacking all chemoreceptors) in a soft-agar plate and incubated the plates at 30 °C under strong selective pressure for cells with improved capacity to spread through the hydrogel. After approximately 48 hours of incubation during which the bacteria barely spread, clear out-grown bulges were observed. We isolated one of these mutants (AVE4) and confirmed that the spreading of this new strain in soft agar is indeed homogeneous (lacking bulges). Similar to the observation of Wolfe et al.33, cells of this AVE4 strain spread in soft agar only slightly better than non-motile cells and considerably less than wild-type chemotactic cells (Fig. 4B). Sequencing showed that this strain has a transposon insertion in the phosphatase cheZ gene. The lack of phosphatase activity, combined with the relatively long lifetime of phospho-CheY and a residual phosphorylation activity by CheA or acetyl phosphate can lead to a substantial level of phospho-CheY and thus to tumbling. Evidently, in contrast to the smooth-swimming behavior of the parental UU strain, the AVE4 mutant bacteria exhibited run-and-tumble swimming behavior with frequent switching of their swimming direction (Fig. 4C). Moreover, quantification of the fraction of swimming cells within bacterial populations that were homogeneously mixed into the bulk of the hydrogel revealed that bacteria that were capable of switching their swimming direction tended to be more motile (Fig. 4D).When tested for surface colonization, the non-chemotactic AVE4 strain, which exhibit run-and-tumble swimming behavior (switching motility), colonized the surface far more efficiently than the parental non-chemotactic strain (Fig. 4A, filled gray symbols).

Effect of bacterial growth on surface colonization: chemotactic vs. non-motile bacteria

In the surface-colonization experiments described thus far, the bacterial suspension was based on motility buffer, which was optimized for bacterial motility13, supports motility over extended periods, but does not support bacterial growth. This suspension was flown through the chamber for four hours while the bacterial surface accumulation was monitored. Thus, these experiments revealed the intrinsic capacity of bacteria to spread through the hydrogel layer and reach the underlying surface. However, when bacteria are allowed to grow, the physical expansion of the bacterial population by itself could potentially lead to spreading through the hydrogel. To test the effect of bacterial growth on surface colonization, we repeated the experiments mentioned above, but then, at the end of the four hours period, we continued the experiment by flowing fresh growth media (TB) through the chamber for additional 14 hours. At this second stage, suspended cells were washed out of the chamber and the trapped cells in the gel were allowed to grow.

As shown in Fig. 5, even after 14 hours of growth, non-motile bacterial cells could not be detected on the surface; moreover, although the outer gel/flow interface was heavily populated with bacteria, the bulk of the hydrogel layer was nearly depleted of bacteria. For comparison, when similar experiments were repeated with chemotactic MG cells, the bacteria clearly populated the oxygen-permeable surface, forming a dense and flat bacterial layer. Interestingly, in the case of MG cells as well, the bulk of the hydrogel layer was nearly depleted of bacteria (Fig. 5). Thus, despite its potential accessibility to the bacterial cells, the thin hydrogel layer was efficient at keeping the non-motile bacteria away from the surface even after an extensive growth period, thus leaving the surface nearly sterile.

Bacterial distribution across the hydrogel layer after growth.

Representative cell distributions (after 1D de-convolution) of the wild-type MG cells (full line) or the non-motile UU cells (dashed line) across the hydrogel layer following 4 hours of the standard surface-colonization experiment (in motility buffer) and an additional 14 hours of flowing fresh TB growth medium. The gray lines represent the margins of the fit variability.

Full size image

Discussion

Several factors could potentially contribute to the ability of bacterial cells to cross a thin protective hydrogel layer and colonize the underlying surface: passive diffusion, growth-driven expansion, random motility and chemotaxis. Although random motility and growth-driven expansion are both negligible over large distances, these processes might be potentially relevant over the short-length scale typical of the hydrogel mucosal layer and the time scale typical for the in vivo regeneration time of this layer (few hours). For example, the effective diffusion coefficient of free swimming E. coli cells conducting a random walk was estimated to be on the order of 4·10−6 cm2/sec and thus can effectively expand over ~ μm in only a few minutes but would require ~1,fold longer time to expand over one centimeter22. In addition, the size of the colony formed by non-motile bacteria in soft agar (in TB at 30 °C) can reach a size of several hundred microns. Thus, the contribution of the different bacterial properties to surface colonization under these conditions is not a priori clear. To study the potential contribution of these bacterial properties to surface colonization under these conditions, we developed a setup for studying the ability of bacteria to colonize surfaces protected by a thin hydrogel layer subjected to a vertical oxygen gradient.

Role of bacterial growth

In principle, the pressure formed within a confined bacterial colony could drive the expansion of cells through the soft hydrogel, which is evidently accessible to these bacteria. Under the conditions described in these experiments a single colony grown for 14 hours within a TB-based soft agar can reach a diameter of – microns. However, despite the continuous supply of fresh growth medium by the flow in the channel assay and the large number of bacteria that colonized the upper gel-flow interface, non-motile bacteria were essentially undetected within the bulk of the hydrogel layer or at the underlying surface even after 14 hours (Figs 1 and 5). Note that even in the in vivo system, regeneration of the mucin hydrogel provides a natural time limit of several hours34. Thus, under the conditions tested in this work, despite its general potential, growth-driven expansion is insufficient to promote bacterial colonization or substantial colonization of the thin hydrogel layer. This behavior might be related to the fact that in contrast to the bulk hydrogel, the hydrogel/flow interface is intrinsically asymmetric and allows shedding of bacterial cells into the flow; thus, it is not conducive to the expansion of the bacterial into the bulk of the hydrogel.

Role of switching motility

Motile bacteria that lack chemotactic capacity were deficient at surface colonization (Fig. 4), indicating that motility by itself is not effective in promoting bacterial spreading even through a thin hydrogel layer. However, in addition to affecting taxis capacity, defects in the chemosensory system also modify the intrinsic swimming behavior of the bacteria, exhibiting smooth-swimming behavior instead of the switching motility (run-and-tumble) exhibited by the wild-type cells. This is generally expected because the default state of the flagella motor is CCW rotation in the absence of a functional sensory system that phosphorylates CheY. As noted by Wolfe et al.33 (see also Fig. 4B), this ability of the bacterial cells to actively switch their swimming direction promotes somewhat more efficient spreading of the colony in semi-solid agar plates. However, because of the short distance across the hydrogel layer, cells that exhibited switching motility (strain AVE4), even in the absence of sensing capacity, showed a dramatic improvement in their capacity to colonize the surface over that of the smooth-swimming bacteria (Fig. 4). This observation suggests that the behavior of non-chemotactic strains in an in vivo context should be interpreted cautiously, particularly because inherent changes in swimming traits can be acquired by spontaneous mutations. Moreover, it might be expected that environmental factors within the hydrogel layer that could affect the tumbling rate of non-chemotactic bacteria can potentially affect their capacity to cross the protective hydrogel layer. For example, acetate (a common metabolic product in the gut) or factors that modulate the cyclic-di-GMP level in the cell can affect the rotation bias of the flagellar motor and thus might affect surface colonization35,36.

The effect of switching motility on the ability of non-chemotactic bacteria to spread through the hydrogel is correlated with their ability to swim vigorously within the hydrogel (Fig. 4D), despite the fact that their intrinsic swimming speed in liquid was not altered. As suggested by Wolfe et al.33, the hydrogel is a non-homogeneous porous environment in which smooth-swimming bacteria might be more likely to become trapped. However, additional reasons for the advantage of switching motility are also possible. For example, a direct effect of oxygen on swimming speeds might have stronger effect on cells with an appropriate switching rate, induced by mutations. In addition, it was recently observed that the flagellar motors can undergo remodeling in response to changes in either the phospho-CheY level or a load that can affect the output torque37,38. It is possible that increased tumbling by itself affects the output torque of the flagellar motor and thus allows these bacteria to swim more efficiently within the hydrogel39.

Role of chemotactic capacity

The role of chemotaxis in surface colonization is clearly demonstrated in the following examples. First, the accumulation of the RP strain is more rapid and enhanced compared with that of its non-chemotactic derivative strains, including strain AVE4 that exhibits switching motility (Fig. 4). Second, the accumulation of the MG strain is clearly more rapid and enhanced compared with that of its aer derivative (Fig. 1), which lacks the primary oxygen sensor but maintains normal chemotaxis capacity in soft-agar plates (Fig. S1). This substantial effect of the Aer sensor further indicates that aerotaxis is playing a significant role in the observed surface accumulation. Note that the Tsr sensor can contribute to the residual bacterial accumulation in the absence of Aer12. The role of aerotaxis is further supported by the fact that replacing the oxygen-permeable surface with oxygen-impermeable slide abolished the bacterial accumulation (Fig. 1). The permeability of the surface to oxygen on the one hand and the low oxygen level induced by the bacterial suspension on the other hand, are expected to lead to an oxygen gradient across the gel layer, in a similar way as in ref. Evidently, when the bacterial suspension was tested in a capillary tube, cells were not swimming at a distance larger than ~2 mm from the liquid/air interface, indicating that the oxygen level there is low31. However, near the oxygen-permeable surface, bacterial cells were vigorously swimming, indicating that the oxygen level near the surface is clearly elevated. A simple estimate of the expected change in the oxygen concentration across the gel layer, suggests that this change can be larger than 30% (see Supplementary). Given that the half-maximal response of the cells to oxygen can be as low as  μM40 and that the bacterial typical ‘run’ (swimming) length is 10 μm, the oxygen gradient that can potentially trigger an aerotaxis response can be less than  μM/μm, corresponding to a difference of approximately 30 μM oxygen across the  μm of the gel layer. This difference is approximately 10% of the oxygen concentration at the air/liquid interface. Thus, aerotaxis appears to be indeed feasible under the conditions tested here. Clearly, additional factors might also play a role in the observed surface colonization, particularly in the experiments in which bacterial growth is involved (Fig. 5). In this case, the nutrients in the growth medium or even chemicals produced by the bacteria41 might also affect their distribution, which becomes sharper at the end of the growth period.

Comparing the MG strain with other chemotactic strains (Fig. 2) revealed a unique behavior of the MG strain in that it rapidly approached the oxygenated surface and accumulated directly at the surface, whereas the RP cells, similar to other strains, tended to accumulate within the bulk of the hydrogel layer (Fig. 3). This tendency of the MG strain also led to a clear enhancement in its ability to approach the surface. The different distributions of the different strains across the hydrogel persisted over time and thus appear to reflect a qualitatively different preference of the two strains rather than a dynamic effect. Thus, chemotactic capacity not only enhances surface colonization but also offers a means for diversifying and controlling the spatial organization of the bacteria within such environments.

What makes the behavior of the MG strain unique? A well characterized motility-related difference between the MG strain and all of the other strains tested in this work is the unique presence of the IS1 insertion elements upstream of the flhDC operon, which encodes the motility master regulators (Figs 2A and S2). It was previously shown that various insertions in this location can lead to different chemotactic behaviors32. However, the behavior of the AVE1 strain, a derivative of the RP strain that carries the IS1 insertion element instead of its original IS5 element upstream to the flhD gene, was still essentially similar to that of its parental RP strain (Figs 2 and S4). Thus, the flhDC regulatory region does not cause the unique behavior of the MG strain. Another candidate is the Aer sensor, which significantly contributes to surface colonization under the conditions tested in this work (Fig. 1). However, elevation of the expression of this sensor in the RP cells still could not promote a behavior similar to that of the MG strain (Fig. S5). In an attempt to identify additional candidates for the different behaviors of the specific strains used in this work, we conducted a whole-genome search for mutations in the RP strain relative to that of the MG strain (Table S2). We subsequently compared these mutations with the documented differences between the W and MG strains42 by searching for mutations in similar loci in both RP and W but not in MG As candidates, this search identified the crp gene (the regulator of catabolic repression) and the rpoS gene (the dominant sigma factor in stationary phase), both of which were mutated in the RP and W strains compared with the MG strain (see Supplementary Table 2). Both genes can indeed affect bacterial motility43,44,45. However, because this analysis does not include genomic insertions, additional candidates are also possible and the mechanistic explanation for the distinct behavior of the MG strain awaits further study.

Hydrogel and the bacterial dilemma

Bacterial motility is generally not conducive to the expression of genes that promote bacterial adherence46. Such opposing regulations, together with the observation that motility can play a critical role in surface colonization (Fig. 5), suggest that the protective hydrogel layer presents an intrinsic dilemma to the bacteria related to their capacity to access the epithelial surface and their capacity to adhere to epithelial cells. Thus, when an heterogeneous bacterial population is faced with the challenge of surface colonization, cells that are prone towards the sessile state and could potentially initiate efficient contact with the epithelial surface are expected to have lower probability of actually approaching the surface and vice versa; cells that are highly motile can efficiently approach the epithelial surface, but their capacity to adhere to the surface is expected to be lower. Such intrinsic conflict might require physiological adaptation of bacteria during the colonization process, which might rely on specific cues within the mucosal layer. The setup presented in this work could be used to study such interactions between bacteria and epithelial surfaces while maintaining the requirement that the bacteria must first cross the hydrogel layer prior to contact with the surface.

Materials and Methods

Bacterial surface colonization setup and experimental procedure

The setup used to measure bacterial surface colonization in this work is shown in Fig. 1A. A hydrogel layer with a thickness of – μm was cast between an oxygen-permeable slide and a nylon grid with an 80 μm mesh size (80 μm mesh size, SPI supplies). The hydrogel layer used in the experiments reported in this work was based on the % Bacto-agar gel commonly used in chemotaxis and motility studies. The oxygen-permeable surface was made from a flat, rigid, transparent and biocompatible silicon-based surface (Paragon-HDS, 58 Dk; Soflex) that is routinely used in commercial contact lenses. After solidification, this structure was subsequently placed in a titanium flow-chamber (volume: 1 ml) that allowed continuous exchange of the medium in the chamber. The flow chamber was mounted in an inverted microscope (Nikon Ti) equipped with a homemade temperature-controlled stage insert and the temperature was set to 30 °C.

The bacteria to be tested were grown overnight at 30 °C in 2 ml of TB (10 g/l Bacto-Tryptone, 5 g/l NaCl) supplemented with  μg/ml ampicillin. Overnight cultures were then diluted fold in  ml of TB supplemented with  μg/ml ampicillin and  μM IPTG and allowed to grow aerobically at  °C. When cultures reached an ODnm of  cells were washed twice in 10 ml of motility buffer ( M KPO4,  mM EDTA,  M NaCl, 1 μM methionine and  M lactic acid, in 1 L DDW, pH ) and gently re-suspended into 6 ml of motility buffer via slow rocking. Cells were washed without re-suspension in between washings to minimize potential motility damage; however, any residual traces were extensively diluted by this washing procedure. Finally, motility buffer was added to form a bacterial suspension with OD~1. The final cell suspension was slowly ( ml/min) flowed through the chamber. The accumulation of cells at the surface and inside the gel layer was monitored using a 40X air objective via either transmitted light or fluorescence microscopy. The microscope was equipped with a temperature-controlled stage set to 30 °C, a temperature commonly used in chemotaxis behavioral assays13. Bacterial surface accumulation was routinely followed for approximately four hours. During this time, the bacterial colonization of the underlying silicon surfaces and their distribution profiles were analyzed as described below. For a few strains, at the end of this taxis experiment, clean motility buffer was flowed through the chamber for 30 min to wash the suspended cells and fresh TB supplemented with  μg/ml ampicillin and  μM IPTG was subsequently flowed through the flow chamber for an additional 10–14 hours ( ml/min), after which the surface colonization and profile were analyzed.

Strains, plasmids and growth conditions

Five commonly used Escherichia coli lab strains were tested in this work (Table S1): MG (IS1+, V. Sourjik, Heidelberg University), RP (J. S. Parkinson, University of Utah), W (R. Hengge, Free Berlin University), BW (Keio collection) and EPEC (E/69, I. Rosenshine, Hebrew University). In addition, we used mutants with specific motility or chemotaxis defects (see also Table S1): the UU47 and AVE5 (fliC) are non-motile, the UU48 and UU49 receptorless strains and the AVE2 (cheY) strain are non-chemotactic, AVE3 (aer) is deleted for the primary oxygen sensor and AVE1 strain is a derivative of the RP strain in which the original IS5 element upstream of the flhD gene was replaced by the corresponding IS1 element from strain MG (see Fig. S3). In addition, strain AVE4 is a derivative of the UU strain that was selected (this study) for increased spreading on a semi-solid agar plate. This strain was characterized by sequencing and was found to carry a Tn5 transposon in the cheZ gene. Strains UU, UU and UU are derivatives of the RP strain (Parkinson J S, University of Utah). Strain AVE5 (fliC) is a derivative of the JW strain (Keio collection) with the kanamycin resistance cassette removed. The AVE2 (cheY) is a derivative of the MG strain constructed by P1 transduction from JW (Keio collection) and subsequent removal of the kanamycin resistance cassette. The AVE3 (aer) is a derivative of the MG strain and was constructed by P1 transduction from JW (Keio collection) and subsequent removal of the kanamycin resistance cassette. All strains were transformed with plasmid pAV41 carrying free myfp (eyfpAK) and were induced with  μM IPTG.

Surface colonization measurements

To evaluate the dynamics of bacterial accumulation on the surface, fluorescence images (5 s exposure time) were taken at 3 to 6 different random locations on the surface every 20–30 minutes and the average number of cells bound to the surface was calculated for each time point. The bacterial distribution across the hydrogel layer was evaluated from a similar set of fluorescence images (1 s exposure time) taken at increasing heights above the surface (with 50 μm intervals). Such a set of measurements was acquired once every 30 minutes. The integrated fluorescence intensity was calculated for each image and plotted as a function of the distance from surface (z) (Fig. S6, blue symbols). To correct for the optical resolution, we constructed a defined thin layer (~50 μm) of bacterial cells in a hydrogel and measured its fluorescence profile in the same manner as described above. The obtained intensity profile could be fit by a Lorentzian G(z − z’) (inset of Fig. S6). Thus, for each bacterial distribution F(z), the expected measured intensity profile I(z) is a weighted sum of the fluorescence of each cell layers, or

Using the measured I(z) and G(z − z’) and equation Q1, we could extract F(z’) by looking for the F(z’) that provides the I(z) that best fits the data after integration. In all cases, the obtained cell distribution was qualitatively verified by direct imaging.

Semi-solid agar plates

From an overnight culture, 1 μl of cell suspension was inoculated in a semi-solid Bacto-agar hydrogel (% Bacto Agar in TB, supplemented with 50 μg/ml ampicillin) poured in a  mm culture plate. The expansion diameter of the colony in the hydrogel was measured after 12 hours of incubation at 30 °C.

Analysis of bacterial swimming behavior

Cells of the UU, UU and AVE4 strains that were grown overnight were diluted in TB and allowed to grow at  °C up to ODnm ≈  The cells were washed and gently re-suspended in motility buffer to an ODnm of For measurement of the bacterial intrinsic switching rate, the bacterial suspension was placed in a titanium chamber with a glass bottom and phase-contrast movies (50 frames, s exposure time each) were collected to track the bacterial swimming inside the chamber (at 30 °C). The average number of tumble events per cell was counted in each movie. A tumble event was counted if the cell abruptly switched its swimming direction. To measure the fraction of swimming bacteria in the hydrogel, the bacterial cells were mixed in the hydrogel (% Bacto-agar in motility buffer) prior to its solidification, placed in the same titanium chamber and allowed to solidify. Similar movies were collected and the relative number of swimming cells was counted.

Sequence analysis

Starting from an overnight culture, cells of strains MG and RP were diluted and re-grown to an OD of ; afterward, genomic DNA was extracted using a ‘DNeasy Blood and Tissue’ kit (QIAGEN). The DNA samples were sequenced at the Laboratory for Whole Genome Sequencing (Hadassah Medical School, The Hebrew University) using a Nextera-XT kit to prepare the DNA libraries and a Miseq sequencer ( × 2 V2 kit) and samples were subsequently analyzed in the Galaxy environment. The sequencing data from each strain were aligned using the BWA mapping tool with the genome of E. coli MG strain (K, version NC_) as the reference genome. The SNPs were identified using the SAM tools with thresholds set to coverage ≥4 and frequency >70%. Out of all identified SNPs, 67 amino acid replacements and 3 intergenic mutations were identified (Table S2). The identified mutations were compared with the known mutations in strain W42 to identify genes mutated in both RP and W compared with MG

Additional Information

How to cite this article: Tamar, E. et al. The role of motility and chemotaxis in the bacterial colonization of protected surfaces. Sci. Rep.6, ; doi: /srep ().

References

  1. Bevins, C. L. & Salzman, N. H. Paneth cells, antimicrobial peptides and maintenance of intestinal homeostasis. Nat. Rev. Micro. 9, – ().

    ArticleCAS Google Scholar

  2. Hansson, G. C. Role of mucus layers in gut infection and inflammation. Curr. Opin. Microbiol. 15, 57–62 ().

    ArticleCAS Google Scholar

  3. McGuckin, M. A., Lindén, S. K., Sutton, P. & Florin, T. H. Mucin dynamics and enteric pathogens. Nat. Rev. Micro. 9, – ().

    ArticleCAS Google Scholar

  4. Butler, S. M. & Camilli, A. Both chemotaxis and net motility greatly influence the infectivity of Vibrio cholerae. Proc. Natl. Acad. Sci. USA , –, doi: /pnas ().

    ADSArticlePubMedPubMed CentralCAS Google Scholar

  5. Celli, J. P. et al. Helicobacter pylori moves through mucus by reducing mucin viscoelasticity. Proc. Natl. Acad. Sci. , –, doi: /pnas ().

    ADSArticlePubMedPubMed Central Google Scholar

  6. Ramos, H. C., Rumbo, M. & Sirard, J.-C. Bacterial flagellins: mediators of pathogenicity and host immune responses in mucosa. Trends Microbiol. 12, –, doi: /j.tim ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  7. Stecher, B. et al. Motility allows S. Typhimurium to benefit from the mucosal defence. Cel. Microbiol. 10, –, doi: /jx ().

    ArticleCAS Google Scholar

  8. Aihara, E. et al. Motility and chemotaxis mediate the preferential colonization of gastric injury sites by Helicobacter pylori. PLoS Pathogens 10, 1–17 ().

    ArticleCAS Google Scholar

  9. Marteyn, B., Scorza, F. B., Sansonetti, P. J. & Tang, C. Breathing life into pathogens: the influence of oxygen on bacterial virulence and host responses in the gastrointestinal tract. Cel. Microbiol. 13, –, doi: /jx ().

    ArticleCAS Google Scholar

  10. Horne, S., Mattson, K. & Prüß, B. An Escherichia coli aer mutant exhibits a reduced ability to colonize the streptomycin-treated mouse large intestine Antonie van Leeuwenhoek 95, –, doi: /sz ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  11. Rivera-Chávez, F. et al. Salmonella uses energy taxis to benefit from intestinal inflammation. PLoS Pathog. 9, e, doi: /journal.ppat ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  12. Taylor, B. L., Zhulin, I. B. & Johnson, M. S. Aerotaxis and other energy-sensing behavior in bacteria. Annu. Rev. Microbiol. 53, – ().

    ArticleCAS Google Scholar

  13. Adler, J. A method for measuring chemotaxis and use of the method to determine optimum conditions for chemotaxis by Escherichia coli. J. Gen. Microbiol. 74, 77–91, doi: / ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  14. Armstrong, J. B., Adler, J. & Dahl, M. M. Nonchemotactic mutants of Escherichia coli. J. Bacteriol. 93, – ().

    PubMedPubMed CentralCAS Google Scholar

  15. Adler, J. Chemotaxis in bacteria. Science , – ().

    ADSArticleCAS Google Scholar

  16. Mao, H., Cremer, P. S. & Manson, M. D. A sensitive, versatile microfluidic assay for bacterial chemotaxis. Proc. Natl. Acad. Sci. , –, doi: /pnas ().

    ADSArticlePubMedPubMed CentralCAS Google Scholar

  17. Berg, H. C. & Turner, L. Chemotaxis of bacteria in glass capillary arrays. Escherichia coli, motility, microchannel plate and light scattering. Biophys. J. 58, – ().

    ADSArticleCAS Google Scholar

  18. Kalinin, Y., Neumann, S., Sourjik, V. & Wu, M. Responses of Escherichia coli bacteria to two opposing chemoattractant gradients depend on the chemoreceptor ratio. J. Bacteriol. , –, doi: /jb ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  19. Tanaka, Y. et al. Biological cells on microchips: New technologies and applications. Biosens. Bioelectron. 23, – ().

    ArticleCAS Google Scholar

  20. Kaper, J. B., Nataro, J. P. & Mobley, H. L. T. Pathogenic Escherichia coli. Nat. Rev. Micro. 2, – ().

    ArticleCAS Google Scholar

  21. Tenaillon, O., Skurnik, D., Picard, B. & Denamur, E. The population genetics of commensal Escherichia coli. Nat. Rev. Micro. 8, – ().

    ArticleCAS Google Scholar

  22. Berg, H. C. E. coli in Motion (Springer, ).

  23. Hazelbauer, G. L., Falke, J. J. & Parkinson, J. S. Bacterial chemoreceptors: high-performance signaling in networked arrays. Trends Biochem. Sci. 33, 9–19 ().

    ArticleCAS Google Scholar

  24. Bibikov, S. I., Biran, R., Rudd, K. E. & Parkinson, J. S. A signal transducer for aerotaxis in Escherichia coli. J. Bacteriol. , – ().

    PubMedPubMed CentralCAS Google Scholar

  25. Rebbapragada, A. et al. The Aer protein and the serine chemoreceptor Tsr independently sense intracellular energy levels and transduce oxygen, redox and energy signals for Escherichia coli behavior. Proc. Natl. Acad. Sci. USA 94, –, doi: /pnas ().

    ADSArticlePubMedPubMed CentralCAS Google Scholar

  26. Taylor, B. L., Watts, K. J. & Johnson, M. S. in Methds. Enzymol. Vol. (eds Brian R. Crane Melvin I. Simon & Crane Alexandrine ) – (Academic Press, ).

  27. Bespalov, V. A., Zhulin, I. B. & Taylor, B. L. Behavioral responses of Escherichia coli to changes in redox potential. Proc. Natl. Acad. Sci. USA 93, – ().

    ADSArticleCAS Google Scholar

  28. Adler, M., Erickstad, M., Gutierrez, E. & Groisman, A. Studies of bacterial aerotaxis in a microfluidic device. Lab. Chip. 12, –, doi: /c2lca ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  29. Mazzag, B. C., Zhulin, I. B. & Mogilner, A. Model of bacterial band formation in aerotaxis. Biophys. J. 85, –, doi: /s(03) ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  30. Bibikov, S. I., Miller, A. C., Gosink, K. K. & Parkinson, J. S. Methylation-independent aerotaxis mediated by the Escherichia coli Aer protein. J. Bacteriol. , –, doi: /jb ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  31. Douarche, C., Buguin, A., Salman, H. & Libchaber, A. E. Coli and Oxygen: A Motility Transition. Phys. Rev. Lett. , ().

    ADSArticleCAS Google Scholar

  32. Barker, C. S., Prüß, B. M. & Matsumura, P. Increased motility of Escherichia coli by insertion sequence element integration into the regulatory region of the flhD operon. J. Bacteriol. , –, doi: /jb ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  33. Wolfe, A. J. & Berg, H. C. Migration of bacteria in semisolid agar. Proc. Natl. Acad. Sci. USA 86, – ().

    ADSArticleCAS Google Scholar

  34. Atuma, C., Strugala, V., Allen, A. & Holm, L. The adherent gastrointestinal mucus gel layer: thickness and physical state in vivo. Am. J. Physiol Gastrointest. Liver Physiol. , G–G ().

    ArticleCAS Google Scholar

  35. Fraiberg, M. et al. CheY’s acetylation sites responsible for generating clockwise flagellar rotation in Escherichia coli. Mol. Microbiol. 95, –, doi: /mmi ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  36. Paul, K., Nieto, V., Carlquist, W. C., Blair, D. F. & Harshey, R. M. The c-di-GMP Binding Protein YcgR Controls Flagellar Motor Direction and Speed to Affect Chemotaxis by a “Backstop Brake” Mechanism. Molecular cell 38, –, doi: /j.molcel ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  37. Yuan, J., Branch, R. W., Hosu, B. G. & Berg, H. C. Adaptation at the output of the chemotaxis signalling pathway. Nature , – ().

    ADSArticleCAS Google Scholar

  38. Lele, P. P., Hosu, B. G. & Berg, H. C. Dynamics of mechanosensing in the bacterial flagellar motor. Proc. Natl. Acad. Sci. USA , –, doi: /pnas ().

    ADSArticlePubMedPubMed Central Google Scholar

  39. Martinez, V. A. et al. Flagellated bacterial motility in polymer solutions. Proc. Natl. Acad. Sci. USA , –, doi: /pnas ().

    ADSArticlePubMedPubMed CentralCAS Google Scholar

  40. Shioi, J., Dang, C. V. & Taylor, B. L. Oxygen as attractant and repellent in bacterial chemotaxis. J. Bacteriol. , – ().

    ArticleCAS Google Scholar

  41. Park, S. et al. Influence of topology on bacterial social interaction. Proc. Natl. Acad. Sci. USA , –, doi: /pnas ().

    ADSArticlePubMedPubMed CentralCAS Google Scholar

  42. Hayashi, K. et al. Highly accurate genome sequences of Escherichia coli K strains MG and W Mol. Syst. Biol. 2, , doi: /msb ().

  43. Liu, X. & Matsumura, P. The FlhD/FlhC complex, a transcriptional activator of the Escherichia coli flagellar class II operons. J. Bacteriol. , – ().

    ArticleCAS Google Scholar

  44. Silverman, M. & Simon, M. Characterization of Escherichia coli flagellar mutants that are insensitive to catabolite repression. J. Bacteriol. , – ().

    PubMedPubMed CentralCAS Google Scholar

  45. Chilcott, G. S. & Hughes, K. T. Coupling of flagellar gene expression to flagellar assembly in Salmonella enterica Serovar Typhimurium and Escherichia coli. Microbiol. Mol. Biol. Rev. 64, –, doi: /mmbr ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  46. Pesavento, C. et al. Inverse regulatory coordination of motility and curli-mediated adhesion in Escherichia coli. Genes Dev. 22, –, doi: /gad ().

    ArticlePubMedPubMed CentralCAS Google Scholar

  47. Studdert, C. A. & Parkinson, J. S. Crosslinking snapshots of bacterial chemoreceptor squads. Proc. Natl. Acad. Sci. USA , –, doi: /pnas ().

    ADSArticlePubMedPubMed CentralCAS Google Scholar

  48. Ames, P., Studdert, C. A., Reiser, R. H. & Parkinson, J. S. Collaborative signaling by mixed chemoreceptor teams in Escherichia coli. Proc. Natl. Acad. Sci. USA 99, – ().

    ADSArticleCAS Google Scholar

  49. Zhou, Q., Ames, P. & Parkinson, J. S. Biphasic control logic of HAMP domain signalling in the Escherichia coli serine chemoreceptor. Mol. Microbiol. 80, –, doi: /jx ().

    ArticlePubMedPubMed CentralCAS Google Scholar

Download references

Acknowledgements

The authors thank Sandy Parkinson for providing strains and comments on this manuscript and also thank Dr. Miriam Kott-Gutkowski (Laboratory for Whole Genome Sequencing, Hadassah Medical School, Hebrew University) for assistance. This work was supported by the U.S.-Israel Binational Science Foundation and the Minerva Center for Bio-Hybrid Complex Systems.

Author information

Affiliations

  1. The Racah Institute of Physics, The Hebrew University of Jerusalem, Safra Campus, Jerusalem, Givat Ram, Israel

    Einat Tamar, Moriah Koler & Ady Vaknin

Contributions

A.V. conceived and designed the experiments and wrote the manuscript. E.T. performed the experiments and analyzed the data. M.K. prepared the strains. All authors reviewed the manuscript.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Electronic supplementary material

Rights and permissions

This work is licensed under a Creative Commons Attribution International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by//

Reprints and Permissions

Sours: https://www.nature.com/articles/srep
  1. Vet clinics in norman ok
  2. Canon pixma vs hp envy
  3. 2 inch curtain rod rings
  4. 2013 honda accord alternator
  5. Accidentally in love drama

Direct Upstream Motility in Escherichia coli

1. Frymier P.D., Ford R.M., Cummings P.T. Three-dimensional tracking of motile bacteria near a solid planar surface. Proc. Natl. Acad. Sci. USA. ;–[PMC free article] [PubMed] [Google Scholar]

2. Berg H.C. Motile behavior of bacteria. Phys. Today. ;–[Google Scholar]

3. Alexandre G., Greer-Phillips S., Zhulin I.B. Ecological role of energy taxis in microorganisms. FEMS Microbiol. Rev. ;– [PubMed] [Google Scholar]

4. Gest H. Phototaxis and other sensory phenomena in purple photosynthetic bacteria. FEMS Microbiol. Rev. ;–[Google Scholar]

5. Dunin-Borkowski R.E., McCartney M.R., Buseck P.R. Magnetic microstructure of magnetotactic bacteria by electron holography. Science. ;– [PubMed] [Google Scholar]

6. Frankel R.B. Magnetic guidance of organisms. Annu. Rev. Biophys. Bioeng. ;– [PubMed] [Google Scholar]

7. Long T., Ford R.M. Enhanced transverse migration of bacteria by chemotaxis in a porous T-sensor. Environ. Sci. Technol. ;– [PubMed] [Google Scholar]

8. Zonia L., Bray D. Swimming patterns and dynamics of simulated Escherichia coli bacteria. J. R. Soc. Interface. ;–[PMC free article] [PubMed] [Google Scholar]

9. Berke A.P., Turner L., Lauga E. Hydrodynamic attraction of swimming microorganisms by surfaces. Phys. Rev. Lett. ; [PubMed] [Google Scholar]

Li G., Tang J.X. Accumulation of microswimmers near a surface mediated by collision and rotational Brownian motion. Phys. Rev. Lett. ;[PMC free article] [PubMed] [Google Scholar]

Lauga E., DiLuzio W.R., Stone H.A. Swimming in circles: motion of bacteria near solid boundaries. Biophys. J. ;–[PMC free article] [PubMed] [Google Scholar]

Li G., Tam L.K., Tang J.X. Amplified effect of Brownian motion in bacterial near-surface swimming. Proc. Natl. Acad. Sci. USA. ;–[PMC free article] [PubMed] [Google Scholar]

Lawrance J.R., Delaquis P.J., Caldwell D.E. Behavior of Pseudomonas fluorescens within the hydrodynamic boundary layers of surface microenvironments. Microb. Ecol. ;– [PubMed] [Google Scholar]

Costerton J.W., Stewart P.S., Greenberg E.P. Bacterial biofilms: a common cause of persistent infections. Science. ;– [PubMed] [Google Scholar]

Hill J., Kalkanci O., Koser H. Hydrodynamic surface interactions enable Escherichia coli to seek efficient routes to swim upstream. Phys. Rev. Lett. ; [PubMed] [Google Scholar]

Nash R.W., Adhikari R., Cates M.E. Run-and-tumble particles with hydrodynamics: sedimentation, trapping, and upstream swimming. Phys. Rev. Lett. ; [PubMed] [Google Scholar]

Tailleur J., Cates M.E. Statistical mechanics of interacting run-and-tumble bacteria. Phys. Rev. Lett. ; [PubMed] [Google Scholar]

Xia Y., Whitesides G.M. Soft lithography. Annu. Rev. Mater. Sci. ;–[Google Scholar]

Kaya T., Koser H. Characterization of hydrodynamic surface interactions of Escherichia coli cell bodies in shear flow. Phys. Rev. Lett. ; [PubMed] [Google Scholar]

Crocker J.C., Grier D.G. Methods of digital video microscopy for colloidal studies. J. Colloid Interface Sci. ;–[Google Scholar]

Glen A.G., Lawrence M.L., Drew J.H. Computing the distribution of the product of two continuous random variables. Comput. Stat. Data Anal. ;–[Google Scholar]

Rohatgi V.K. Wiley; New York: An Introduction to Probability Theory Mathematical Statistics. [Google Scholar]

Jeffery G.B. The motion of ellipsoidal particles immersed in a viscous fluid. Proc. R. Soc. A. ;–[Google Scholar]

Chattopadhyay S., Moldovan R., Wu X.L. Swimming efficiency of bacterium Escherichia coli. Proc. Natl. Acad. Sci. USA. ;–[PMC free article] [PubMed] [Google Scholar]

Weis R.M., Koshland D.E., Jr. Chemotaxis in Escherichia coli proceeds efficiently from different initial tumble frequencies. J. Bacteriol. ;–[PMC free article] [PubMed] [Google Scholar]

Shum H., Gaffney E.A., Smith D.J. Modelling bacterial behaviour close to a no-slip plane boundary: the influence of bacterial geometry. Proc. R. Soc. A. ;–[Google Scholar]

Möller E., McIntosh J.F., Van Slyke D.D. Studies of urea excretion. II. Relationship between urine volume and the rate of urea excretion by normal adults. J. Clin. Invest. ;–[PMC free article] [PubMed] [Google Scholar]

Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC/
Motility test - Microbiology (Microbial Biochemical test)

A novel role for RecA under non-stress: promotion of swarming motility in Escherichia coli K

  • Research article
  • Open Access
  • Published:

BMC Biologyvolume 5, Article number: 14 () Cite this article

  • 14k Accesses

  • 42 Citations

  • Metrics details

Abstract

Background

Bacterial motility is a crucial factor in the colonization of natural environments. Escherichia coli has two flagella-driven motility types: swimming and swarming. Swimming motility consists of individual cell movement in liquid medium or soft semisolid agar, whereas swarming is a coordinated cellular behaviour leading to a collective movement on semisolid surfaces. It is known that swimming motility can be influenced by several types of environmental stress. In nature, environmentally induced DNA damage (e.g. UV irradiation) is one of the most common types of stress. One of the key proteins involved in the response to DNA damage is RecA, a multifunctional protein required for maintaining genome integrity and the generation of genetic variation.

Results

The ability of E. coli cells to develop swarming migration on semisolid surfaces was suppressed in the absence of RecA. However, swimming motility was not affected. The swarming defect of a ΔrecA strain was fully complemented by a plasmid-borne recA gene. Although the ΔrecA cells grown on semisolidsurfaces exhibited flagellar production, they also presented impaired individual movement as well as a fully inactive collective swarming migration. Both the comparative analysis of gene expression profiles in wild-type and ΔrecA cells grown on a semisolid surface and the motility of lexA1 [Ind-] mutant cells demonstrated that the RecA effect on swarming does not require induction of the SOS response. By using a RecA-GFP fusion protein we were able to segregate the effect of RecA on swarming from its other functions. This protein fusion failed to regulate the induction of the SOS response, the recombinational DNA repair of UV-treated cells and the genetic recombination, however, it was efficient in rescuing the swarming motility defect of the ΔrecA mutant. The RecA-GFP protein retains a residual ssDNA-dependent ATPase activity but does not perform DNA strand exchange.

Conclusion

The experimental evidence presented in this work supports a novel role for RecA: the promotion of swarming motility. The defective swarming migration of ΔrecA cells does not appear to be associated with defective flagellar production; rather, it seems to be associated with an abnormal flagellar propulsion function. Our results strongly suggest that the RecA effect on swarming motility does not require an extensive canonical RecA nucleofilament formation. RecA is the first reported cellular factor specifically affecting swarming but not swimming motility in E. coli. The integration of two apparently disconnected biologically important processes, such as the maintenance of genome integrity and motility in a unique protein, may have important evolutive consequences.

Background

Motility is an important bacterial niche colonization factor [1, 2], thus many bacterial species are motile by flagellar rotation. In Escherichia coli flagella propel bacteria swimming in liquid medium or in semisolid agar media [3]. In response to chemotactic external signals, E. coli is able to direct its swimming movement towards a microenvironment that is optimal for its growth and survival [4]. When the conditions for swimming become adverse E. coli develops a different program for swarming motility [1, 5–7]. In many bacterial species this flagella-driven coordinated motility is mediated by cell-cell communication mechanisms such as quorum sensing [8]. Thus, this bacterial migration activity is an intrinsically surface-linked phenomenon, leading to a change from an individual to a collective "social" behavior that allows the rapid exploration and colonization of surfaces [1, 8, 9].

Several environmental and nutritional factors promoting swarming behavior in some bacterial species such as Proteus mirabilis [10], Serratia liquefaciens [11], Bacillus subtilis [12], Pseudomonas aeruginosa [13, 14] and Salmonella enterica Serovar Thyphimurium [15] have been described. In E. coli, however, the chemical and physical factors involved in swarming migration are still to be characterized. By contrast, although the relationship between several types of environmental stress and bacterial motility has been well studied in swimming [2, 16–19], their effect on swarming motility has been poorly explored.

Bacteria need to cope with different types of stress in nature [20], environmentally induced DNA damage (e.g. UV irradiation) being a common one [21]. The relationship between DNA damage and colonial motility has not been previously addressed. RecA is a protein with a central role in DNA stability and repair under stress conditions leading to DNA damage [22–27]. RecA has multiple functions affecting different cellular processes, such as genetic recombination [22, 28], the rescue of stalled or collapsed replication forks [25, 29], and the replication of damaged DNA through translesion DNA synthesis (TLS) by DNA polymerase V (pol V) [30, 31]. Moreover, it is a key component for LexA self-cleavage [27]. These processes require the interaction of RecA with single-stranded (ss)-DNA forming a helical nucleoprotein filament [27].

In this paper, we describe a previously unsuspected role for RecA in swarming, but not swimming motility in E. coli. The RecA control of swarming motility represents a novel basic RecA role under proliferating conditions beyond its pivotal activities in situations that lead to DNA damage. Our results indicate that this RecA effect on collective swarming migration is possibly mediated through an operative RecA mode requiring neither SOS-induction nor extensive canonical RecA-ssDNA nucleoprotein filament formation.

Results

RecA is required for E. coliswarming but not swimming motility

E. coli K MG strain growing on LB-glucose harnessed with % Difco agar exhibits a vigorous flagella-driven swarming migration, developing a robust colonial pattern associated to this type of motile collective behavior [7] (Figure 1A). While performing experiments designed to study the response of E. coli MG to DNA-damage stress, we unexpectedly observed that E. coli MG deleted for the recA gene was impaired in its ability for swarming migration. This prompted us to study other recA-defective strains and we observed that all of them showed identical behaviour. The isogenic MG strains lacking the recA gene, JMG Δ(srlR-recA)::Tn10 (here, ΔrecA) or JMG ΔrecAkan (here, ΔrecA), were severely impaired in their ability to migrate over semisolid agar surfaces, with a defective outward colonial expansion. Figure 1B shows the swarming colonial pattern of the JMG (ΔrecA) strain. To learn whether the absence of RecA was responsible for the defective swarming migration, we carried out complementation experiments. The plasmid pRecA-Htg, harboring an E. coli recA wild-type gene that encodes an N-terminal histidine-tagged RecA protein (termed here as RecA-Htg variant), and the empty vector (pCA24N, vector control without recA) were introduced by transformation into the wild-type and both ΔrecArecA and ΔrecA) strains. Similar results were obtained with MG ΔrecA or ΔrecA derivate strains, hence the results are only shown for one of them: the ΔrecA derivative. Figure 2A shows that the swarming motility of the JMG ΔrecA strain harboring the plasmid pRecA-Htg formed a colonial pattern similar to that of the wild-type MG strain. By contrast, the strain harboring the pCA24N vector was severely impaired in swarming. The ability of the RecA-Htg variant to complement the swarming defect indicates that the lack of RecA is the sole cause of the motility defect in the recA mutant strain. This result also demonstrated that the engineered RecA variant (RecA-Htg) protein used in our experiments was capable of promoting the swarming motility process.

Swarming colonial patterns. Swarming colonial patterns developed on a semisolid "Difco" agar surface for 24 h at 37°C by (A) MG wild-type, (B) MGΔ(srlR-recA)::Tn10, and (C) MG lexA1. Plates have a diameter of 9 cm.

Full size image

Swarming complementation test. (A) E. coli JMG ΔrecA (pCA24N) and JMG ΔrecA (pRecA-Htg) strains. (B) E. coli JMGΔrecA (pCA24N) and JMGΔrecA (pRecA::GFP) strains. (C) E. coli RPΔrecA (pCA24N) and RP ΔrecA (pRecA-Htg) strains. Plates were photographed after 24 h of development at 37°C. Plates have a diameter of 9 cm.

Full size image

Swimming motility is another well-known E. coli flagella-driven motility mode [3, 4]. To test whether RecA is also involved in the control of this individual motility process, we examined the swimming ability of the JMG ΔrecA (pCA24N) and JMG ΔrecA (pRecA-Htg) strains. Figure 3A shows that the ΔrecA mutant only had a slightly reduced swimming colony size. This small difference may be due to the additional time that the ΔrecA mutant takes at the start of swimming colony expansion, compared to the wild-type strain. However, the speed of the swimming advance was similar in both strains (data not shown). This is consistent with normal flagellation, cellular size, and the individual movement of both swimming cell types (data not shown). Thus, it is likely that RecA is unnecessary for swimming, although it is absolutely essential for swarming motility.

Swimming motility ofE. coliMG and RP strains. (A) E. coli JMG ΔrecA (pCA24N) and JMG ΔrecA (pRecA-Htg) strains. (B) E. coli RPΔrecA (pCA24N) and RP ΔrecA (pRecA-Htg) strains. Plates were photographed after 24 h of development at 37°C. Plates have a diameter of 9 cm.

Full size image

The RecA effect on swarming motility is not restricted to the E. coliMG strain

The ability of E. coli strains to swarm on semisolid surfaces was initially described by Harshey and Matsuyama in [5], and has been studied intensively in the RP strain [6]. To know whether the effect of RecA on swarming motility was specific to the strain MG, both the swarming and swimming motility behaviour of the RP and RP ΔrecA [32] strains were assayed. The swarming capacity of the RP ΔrecA strain was severely impaired when compared to the wild-type RP strain. Figure 2C shows that the negative effect produced by the recA deletion on the swarming motility of the RP strain was almost fully restored by the introduction of the pRecA-Htg plasmid. As in the case of JMG ΔrecA, swimming motility was not affected by the ΔrecA mutation (Figure 3B). These findings indicate that the effect of RecA on swarming motility on "Difco" semisolid surfaces is not restricted to MG Examination of the swarming colonial patterns of JMG (pRecA-Htg) (Figure 2A) and RP ΔrecA (pRecA-Htg) (Figure 2C) also revealed that, under our experimental conditions, swarming of strain RP was not as active as that of strain MG In addition, we also observed that when RP and MG strains were inoculated together on swarming plates, the MG swarming migration overtook the RP swarming ability on semisolid Difco agar surfaces, under the conditions employed in this study (Gómez-Gómez and Blázquez, unpublished results). On the basis of these observations, we decided to use the MG strain as a model for the study of the RecA effect on swarming motility in E. coli.

Finally, by a simple visual observation of the colony advance edge of both the wild-type and ΔrecA strains, we can rule out any effect of RecA on the production of slime because the slime that normally surrounds the colony periphery was, apparently, similar in both cases (data not shown).

The RecA effect on swarming motility does not require SOS induction

RecA functions as a key component of the regulatory system controlling the induction of the SOS response [27]. The E.coli LexA protein represses the transcription of over 40 "SOS response genes", the SOS regulon, including lexA itself [33]. The RecA interaction with ss-DNA unveils its coprotease activity (RecA*) that facilitates self-cleavage of the LexA repressor, with a subsequent deregulation of the SOS response genes [27] in an exquisite proportionally-tuned response adjusted to the level of DNA damage [34].

To address whether one or more components of the SOS system could be the direct effector(s) promoting swarming motility, comparative transcriptome expressions between the recA+ and ΔrecA cells, sampled from the edge of colonies grown on swarming plates, were performed by DNA microarray analysis. The obtained results (see Additional file 4) indicated that the transcriptional profile of genes belonging to the SOS regulon [33] were identical in both cases. In addition, an MG derivative, harboring the chromosomal lexA1 [Ind-] allele, encoding a well characterized LexA protein resistant to the RecA stimulated LexA self-proteolysis, thus preventing the induction of the SOS response [33], was found to be fully proficient in swarming motility (Figure 1C). Thus, it is likely that the ability of E. coli to perform swarming migration on semi-solid surfaces does not require the induction of the SOS response.

The ΔrecAstrain grown on swarming agar does synthesize flagella yet shows a non-motile colony leading edge without any swarming motion

Optical microscopy was used to determine whether ΔrecA cells showed defective individual and/or collective movements. Wild-type cells extracted from the swarming colony edge showed a vigorous and rapid individual movement when suspended in an LB liquid drop. However, ΔrecA cells showed a very slow movement. Direct observation of the leading edge of the swarming colonies indicated that the wild-type colony edge exhibited a vigorous and very rapid convective and collective cell movement, with swirling patterns [7]. In contrast, the leading edge of the ΔrecA swarming colonies presented a completely resting edge colony (see Additional files 1 and 3). This deficient collective movement of the ΔrecA strain, leading to a defective colony pattern development, could possibly be due to the absence or damage of the flagella. To test this hypothesis, flagella were stained in both the wild-type and ΔrecA cells. A total of cells from each strain (10 to 20 different fields) were visualized (see Methods). Figure 4A shows a typical wild-type swarming cell with five or six peritrichous flagella. A total of (%) MG recA+ cells and (75%) MG ΔrecA (pRecA-Htg) cells were similar to the one shown in the figure. The ΔrecA preparations contained abundant flagella, yet appeared disconnected from the cell (Figure 4B), thereby indicating that ΔrecA cells have the ability to synthesize flagella. More than 99% ( out of ) of the MG ΔrecA (pCA24N) cells were similar to those shown in the figure.

Flagella filaments. Visualization of cell flagella extracted from the leading colony edges grown on swarming plates. (A) Typical swarming cell from the JMG ΔrecA (pRecA-Htg) strain. (B) Typical swarming cells from the JMG ΔrecA (pCA24N) strain. The arrow indicates the position of the flagella in the preparation. Bar 10 μm.

Full size image

This result is additionally supported by a comparative DNA microarray analysis between the recA+ and ΔrecA cells grown on semisolid surfaces. This analysis showed that both chemotaxis and flagellar genes related with E. coli motility [35] had similar expression levels in both genetic backgrounds (see Additional file 4). Thus, it is likely that the impaired production of flagella is not responsible for the lack of swarming motility of ΔrecA cells. All together these results suggest that the non-motile behavior of the individual ΔrecA cells may be due to the impaired flagellar propulsion function necessary to drive the swarming migration process.

RecA-GFP complementation assays under different proliferating conditions: segregation of RecA functions

RecA is a multifunctional protein that is involved in DNA recombination, DNA double-strand break repair, induction of the SOS response, SOS mutagenesis and the repair of stalled replication forks [27]. These are, in all cases, functions that depend on RecA-ssDNA nucleofilament formation [27]. To gain further insight into the involvement of RecA in swarming motility, we took advantage of the construction of a RecA-green fluorescent protein fusion (RecA-GFP) [36]. Our reasoning was that this fusion protein could lose its ability to generate the RecA filament formation because of the bulky GFP portion. The ability of the RecA-GFP chimera to complement the defects of the ΔrecA strain in growth, viability, SOS induction, UV sensibility, genetic recombination and swarming motility was compared to that of the RecA-Htg variant. No significant differences between MG recA+ harboring pRecA-Htg, pRecA-GFP or pCA24N (empty vector) in growth, UV survival, viability and genetic recombination were observed. Thus, for simplicity, the complementation behavior of plasmids pCA24N, pRecA-Htg and pRecA-GFP in the ΔrecA background refers to the strain MG recA+ (pCA24N) as a wild-type control. Table 1 and Figures 2A, 2B, 5 and 6 show the results of these rescue experiments with both protein variants and several important results can be extracted from them.

Full size table

Growth rates of different strains. Overnight cultures of E. coli strains grown in LB medium were diluted (1/) and subcultured in fresh LB medium supplemented with % D-(+)-glucose and chloramphenicol. MG (pCA24N) (filled circles), JMG ΔrecA (pCA24N) (open circles), JMG ΔrecA (pRecA-Htg) (open triangles), and JMG ΔrecA (pRecA-GFP) (open squares)

Full size image

UV survival and SOS induction assays. Plasmids pCA24N, pRecA-Htg, and pRecA-GFP were introduced in the strain GW (dinB::lacZ) [40] and its derivative ΔrecA [39] by transformation. Norfloxacin-mediated SOS induction of the dinB::lacZ was assayed by the disk-plate method as described [39]. (A) Survival following UV irradiation of wild-type MG recA+ (pCA24N) (1), JMG ΔrecA (pCA24N) (2), JMG ΔrecA (pRecA-Htg) (3) and JMG ΔrecA (pRecA-GFP) (4) strains. UV doses in J/m2 are indicated. (B) The effect of RecA-Htg and RecA-GFP proteins on the norfloxacin-mediated SOS induction of a dinB::lacZ gene fusion and on sensitivity to norfloxacin. Plates on the left side show the effect of norfloxacin on the wild-type strain GW containing either pCA24N, pRecA-Htg or pRecA-GFP plasmids. Plates on the right side show the effect of norfloxacin on the GWΔrecA strain containing the indicated plasmids. The intensity of the gray border around the inhibition halo reflects the level of dinB transcription [39].

Full size image

Swarming motility

Figure 2B shows that the RecA-GFP variant was able to complement the swarming motility defect of ΔrecA cells to a level similar to the one obtained with the RecA-Htg variant. Direct observation of the swarming colonies' leading edge (see Additional file 2) additionally demonstrated that this variant restored the collective movement completely to a level similar to the wild-type strain (see Additional file 1). Several observations rule out any effect of the GFP moiety of the RecA-GFP protein fusion in swarming motility. Firstly, the pCA24N plasmid, employed as a control vector in our experiments, encodes the GFP protein; however it did not complement the swarming defect of the recA mutants (Figure 2A and 2B). Secondly, another different RecA-GFP protein fusion construction encoded in the chromosome of the E. coli SS strain [37], which retains an important residual activity promoting UV survival, genetic recombination, and SOS induction [37], showed defective swarming, although normal swimming motility was observed under our conditions (data not shown). Thus, although the biochemical activities associated to the RecA-GFP protein constructed by Renzette et al [37] remain to be studied, it seems that: (1) the GFP moiety of the RecA-GFP protein is not involved in the rescue of the swarming defect, and (2) the plasmid-borne RecA-GFP employed in our study [36] and the RecA-GFP protein fusion harbored in the E. coli SS strain [37] were different with respect to its effect on swarming motility, suggesting a structural difference between both fusion proteins. However, the possibility that the differential behaviour in swarming motility is due to a gene dosage effect cannot be ruled out.

Genetic recombination

RecA is essential for genetic recombination [22, 28]. As shown in Table 1, RecA-Htg completely restored the production of recombinant ΔrecA transconjugants to a level similar to the wild-type strain. By contrast, the RecA-GFP protein was remarkably inefficient in carrying out this activity. These results strongly suggest that the RecA-Htg variant was proficient for both DNA pairing and strand exchange between homologous DNA molecules, whereas the RecA-GFP variant was not.

Growth and swarm cell viability

It has previously been described that an MG derivative ΔrecA strain shows defective growth [38]. To rule out the possibility that the defective swarming motility of the recA mutant was associated to defective growth or decreased viability, both the growth rate and viability of the wild-type and ΔrecA strains were measured (Figure 5). The growth rate of the ΔrecA (pCA24N) strain in LB broth supplemented with % D-(+)-glucose was reduced when compared to the wild-type (harboring the pCA24N plasmid) strain. The ΔrecA (pRecA-GFP) strain also showed slow growth. The slow growth of the ΔrecA strain could only be partially restored by the RecA-Htg variant protein. Indeed, even though the RecA-GFP protein appears to exacerbate the slow growth of the ΔrecA strain, it is still proficient in rescuing its swarming motility defect. These results therefore strongly suggest that the slow growth rate of the ΔrecA strain is not the cause of the defective swarming motility.

Alternatively, the impaired swarming motility of the ΔrecA cells may be the result of cell viability loss [25]. This possibility was tested by assessing membrane integrity by staining both the wild-type and ΔrecA mutant cells isolated from the leading edge of swarming plates with the Live/Dead BacLight Kit (Molecular Probes). A total of cells were visualized in each case. A similar number of cells ( wild-type and ΔrecA) appeared viable. This result suggested that the loss of viability was not involved in defective swarming migration.

UV survival

RecA is required for the recovery of UV-treated cells [25]. As shown in figure 6A lane 3, the RecA-Htg variant was proficient in rescuing the UV-sensitivity phenotype of the ΔrecA strain, however RecA-GFP was not (Figure 6A, lane 4), suggesting that RecA-GFP is impaired in recovering from the UV-induced DNA lesions, i.e. rescue of the replication fork [25, 29] and the recombinational DNA repair [23] both of which required DNA strand exchange.

SOS induction

Treatment of cells with some agents, such as fluoroquinolone antibiotics, induces SOS response [38, 39]. Here we employed the transcriptional dinB::lacZ gene fusion as a reporter of SOS induction [39, 40]. As shown in figure 6B, the RecA-Htg protein supported the norfloxacin-mediated SOS induction, detected as a strong blue/green band around the norfloxacin inhibition halo in a lawn of the ΔrecA (pRecA-Htg) strain. However, the RecA-GFP protein was unable to complement the defect in SOS induction. By contrast, the RecA-GFP protein was fully inactive in rescuing the ΔrecA antibiotic sensitivity phenotype, as the size of the norfloxacin inhibition halo for the GW ΔrecA (pRecA-GFP) strain ( ± cm) was as large as that corresponding to GW ΔrecA (pCA24N) (Figure 6B).

Thus, by using the RecA-GFP fusion protein we have been able to segregate the RecA function on swarming promotion from the other RecA functions. The combination of all our results strongly supports the notion that, although the RecA-GFP protein is proficient in promoting swarming motility, it has lost its capacity to promote SOS induction, recombinational DNA repair and genetic recombination.

Biochemical studies on ssDNA-dependent ATPase activity of the RecA-GFP protein fusion

The above results obtained with the RecA-GFP protein fusion suggest the possibility that RecA promotes swarming motility via a molecular mode, independently of the RecA filament formation and/or DNA strand exchange. The RecA-Htg and RecA-GFP proteins were purified and biochemically characterized. ATP binding induces RecA assembly on ssDNA and ADP induces RecA disassembly from DNA [24]. Therefore, ATP hydrolysis is an indirect measure of RecA protein binding to ssDNA, and under most condition the rate of ATP hydrolysis correlates to the amount of RecA bound to ssDNA. The RecA, RecA-Htg or RecA-GFP catalyzed ssDNA-dependent ATP hydrolysis was assayed using half-saturating protein concentrations (1 monomer/10 nucleotides) in buffer C containing 20 mM NaCl and 2 mM ATP (see Methods). The rate of RecA and RecA-Htg-mediated ATP hydrolysis increased during the first 30 min after binding to ssDNA, reaching levels similar to those observed in the wild-type RecA protein [28] (Figure 7). In the absence of ssDNA, ATP hydrolysis by wild-type RecA, RecA-Htg or RecA-GFP was just above the background (data not shown).

RecA or RecA-GFP catalyzed ssDNA-dependent ATP hydrolysis. The RecA protein [1 μM (filled circles)] or the RecA-Htg variant [1 μM (triangles)] or increasing concentrations of RecA-GFP [ (circles), (squares), 1 (rhombuses), (plus signs) or 2 μM (crosses)] were preincubated with circular ssDNA (10 μM) in buffer C, then 2 mM ATP was added and the reaction was incubated for a variable time at 37°C. Time zero corresponds to the moment the ATP was added.

Full size image

The ATPase activity of the RecA-GFP variant was 8 to fold lower when compared to the RecA-Htg variant (Figure 7) or wild-type RecA protein [24, 28] (Figure 7). The presence of increasing concentrations of RecA-GFP slightly enhanced ATPase activity (Figure 7). A modification of the pH in the reaction mixture exerted either a negative effect (fold reduction) at pH or a positive effect (fold increase) at pH where RecA binds double stranded (ds) DNA [24, 28] (data not shown). The ATPase activity of RecA-Htg or RecA-GFP variants was strongly inhibited by prebound SSB to ssDNA (data not shown). Therefore, it is improbable that we are measuring the activity of a possibly contaminating ssDNA-dependent ATPase.

If long nucleoprotein filaments are to be formed, the inherent rate of extension of the RecA filaments during assembly should be faster than the end-dependent disassembly. We have shown that RecA-GFP catalyzes ATP hydrolysis less efficiently than RecA-Htg or wild-type RecA protein and assumed that RecA-GFP might promote end-dependent disassembly at a very low rate and perhaps, under these conditions, long nucleoprotein filaments could be formed. To address this hypothesis, the wild-type RecA or the variants RecA-Htg or RecA-GFP (1 μM) were incubated with circular ssDNA in the presence of 1 mM ATPγS and the reaction visualized by electron microscopy. As observed for RecA (Figure 8A) [41], the RecA-Htg-ssDNA complex shows an extended helical conformation in the presence of ATPγS (Figure 8B). Under standard conditions, using 1 μM RecA-Htg, 8–16 nucleoprotein filaments per field were observed (data not shown). However, under identical conditions, we have failed to detect long RecA-GFP nucleoprotein filaments on circular ssDNA in the presence of ATPγS after analysing more than 20 different fields (Figure 8C). Only RecA-GFP blobs were observed even in the presence of 4 μM RecA-GFP (C. M. and R. Lurz, unpublished results). Therefore, we can conclude that quite probably the GFP moiety inhibited the RecA nucleation step in RecA-GFP filament formation.

Visualization of filament formation of RecA or its variants. Circular ssDNA (10 mM in nucleotides) was pre-incubated with (1 mM) RecA (A) or variants RecA-Htg (B) or RecA-GFP (C) 20 min at 37°C in buffer C containing 20 mM NaCl and 1 mM ATPγS, and incubated for 40 min at 37°C. The specimens were prepared for electron microscopy by negative staining with 1%

Full size image

Under typical assay conditions, ATP acts in combination with RecA or its RecA-Htg variant to promote pairing and strand exchanges [28]. RecA-GFP, however, failed to promote strand exchange when using a circular ssDNA and a KpnI-linearized homologous dsDNA.

Overall these biochemical data are consistent with the results obtained from in vivo genetic recombination experiments (Table 1). In combination, the results of genetic complementation and biochemical assays presented in the previous sections strongly support the notion that the RecA-GFP protein, although proficient in promoting swarming motility, does not have the capacity to promote both SOS induction and homologous recombination, activities requiring an extensive canonical RecA-ssDNA filament formation. However, the possibility that a residual localized RecA-GFP short filament unable to reach a sufficient level to induce the SOS could be responsible for this activity in swarming cells cannot be ruled out. A short RecA-GFP filament formation could explain the residual in vivo genetic recombination observed in the ΔrecA (pRecA-GFP) strain (Table 1).

Discussion

In this paper, we present experimental evidence supporting the fact that the RecA protein, described as the prototypic member of the evolutionarily-conserved RecA/RAD51 family of proteins [42], is required for E. coli K swarming but not swimming motility. The RecA protein plays a key role in recombinational DNA repair and genetic recombination processes via its biochemical activity of pairing and exchanging DNA between two homologous chromosomes [22–24, 42, 43]. By forming an active filament on ssDNA, RecA controls the induction of the SOS response, promotes the generation of the SOS-induced DNA polymerase V (polV) active complex (UmuD'2C) [27], and furthermore the RecA filament has the ability to interact with polV in trans to stimulate its activity on translesion DNA synthesis (TLS), a SOS mutagenesis pathway, in response to chronic DNA damage [30, 31]. The results presented here expand on our knowledge of the physiological processes affected by this important protein and reveal a hitherto unknown role for RecA in the E. coli physiology: the promotion of swarming motility. Although it has been described that Salmonella enterica Serovar Typhimurium mutant strains with LPS defects are differentially affected in their abilities to swarm and swim [15], RecA is the first reported cellular factor that specifically affects swarming but not swarming motility in E. coli.

At least two models could explain the RecA requirement for swarming motility. In the first one, RecA interacts with one or several cellular component(s) expressed (or activated) during the swarming differentiation process, but not during swimming motility. This model implies that RecA could influence the expression of a certain gene(s) in swarming cells, however, the results of our transcriptome analysis indicated that the expression of a hypothetical component(s) in swarming cells seems to be a RecA-independent process, because RecA does not influence gene expression in swarming cells. This is consistent with the observation that RecA-GFP fails to induce the SOS response, although it does promote swarming motility. In the second model, RecA may undergo a "modification" (e.g posttranslational) to reach an activated form that allows swarming motility.

Regardless of the molecular mechanism behind RecA-dependent swarming motility, RecA must interact with an unknown cellular target(s) to promote this motility. Recent global protein interaction network experiments in E. coli revealed a direct physical interaction between the RecA and the CheW protein [44]. The CheW protein is a cytoplasmic linker of the chemotactic signal transduction system that couples the membrane chemoreceptors to the intracellular cytoplasmic phosphorylation cascade [4]. This cascade controls the direction of flagellar motor rotation [4], which is an essential component in the mechanical control of the chemotaxis system on swarming motility in Salmonella enterica serovar Typhimutium [45]. Thus, it is tempting to speculate that a RecA-CheW physical interaction, active in swarming but not in swimming cells, could be the direct link between RecA and swarming motility.

All functions so far described for the RecA protein are related to their fundamental biological role in the maintenance of genome integrity and variability. These functions require the formation of an extensive RecA-ssDNA nucleofilament complex as a central molecular scaffold that drives the RecA activities in homologous recombination, SOS induction and SOS mutagenesis. The RecA-ssDNA nucleofilament formation is a very orchestrated, dynamic and precise molecular process during DNA strand exchange [27, 46]. In addition, the cells have several molecular modulators, such as the SSB, single-stranded DNA-binding protein, RecFOR proteins, DinI, RecX and RdgC influencing RecA's filament extension [27, 47, 48]. It is widely accepted that under most conditions the rate of ATP hydrolysis by the RecA protein correlates with the amounts of RecA bound to ssDNA [22, 24]. Although the RecA-GFP protein used in our studies retained a residual ssDNA-dependent ATPase activity, it completely lost its DNA strand exchange ability, a process requiring an extensive RecA-ssDNA nucleofilament formation. Despite this impaired in vitro biochemical activity, RecA-GFP is proficient in complementing the defective swarming motility of a ΔrecA mutant. We consider that the segregation of the RecA effect on swarming motility from those that require the formation of long canonical RecA-ssDNA (or RecA-dsDNA) nucleoprotein filaments, favor the hypothesis that the formation of these long filaments is not strictly necessary for swarming motility. Alternative models, including a RecA activity independent of any DNA interaction cannot, however, be discarded.

The integration of two, apparently disconnected, biological processes, such as the maintenance of genome integrity and motility in a unique protein, may have important evolutive consequences. Adaptive biological evolution, relying upon the existence of genetic variability in a population of organisms generated by mutation and recombination and upon the natural selection of genotypes with better fitness, also depends on the capacity of the fittest to colonize the niche. The existence of a molecule able to receive signals informing on the status of genome stability and transmitting them to cellular systems, which in turn permit rapid spatial migration, could be an important factor for improving the colonization of a new ecological niche. Thus, it is tempting to speculate that the RecA protein could be the molecule that links those apparently independent molecular processes, permitting bacteria to manage the different cellular processes in a coordinated manner, influencing their adaptive capacity.

An important goal in future research will be to determine the ultimate molecular entity and the operative molecular mechanism through which RecA allows swarming motility in E. coli to proceed. The possibility of this novel function being conserved in other RecA homologues should also be explored.

Conclusion

A novel physiological role for E. coli RecA, a protein previously known as a key multifunctional cellular factor required for the maintenance of genome integrity under stress, has been described in this work. RecA participates actively in the promotion of E. coli swarming motility but not swimming motility. While the ultimate operative molecular mode through which RecA acts to control E. coli swarming motility is waiting to be disclosed, the RecA effect on swarming motility appears to be independent of an extensive canonical RecA nucleofilament formation. The integration of two apparently disconnected biologically important processes, such as the maintenance of genome integrity and motility in a unique protein, may have important evolutive consequences.

Methods

E. colistrains, plasmids and growth conditions

The E. coli wild-type strain used in this work was MG (CGSC) obtained from the E. coli Genetic Stock Center [7]. The RP and RP Δ(recA)SstII-EcoRI srl::Tn10 strains were a gift from R.B. Bourret. The MG strain harbouring the lexA1 [Ind-] allele was obtained from Ivan Matic (Necker hospital, Paris). All strains were routinely grown in Luria-Bertani (LB) medium (% "Difco" Bacto-tryptone and % "Difco" Yeast Extract, % NaCl) or on LB plates. When necessary, media were supplemented with the following antibiotics used at the indicated concentrations: chloramphenicol (Cm) 20 μg/ml; tetracycline (Tc) 20 μg/ml and kanamycin (Km) 30 μg/ml. The MGΔrecA derivatives, JMG and JMG, were constructed by P1vir-mediated transduction [49] of the ΔrecA alleles from the SMR Δ(srlR-recA)::Tn10 [50] and MS6 ΔrecAkan [51] strains respectively. ELE1 is the E.coli K Hfr strain P4X [52] with the ΔfhuD::kan marker, transferred by P1vir transduction from the JW strain obtained from the E. coli K Genobase library Keio collection of NARA Institute [53]. The recA and lexA1 phenotypes were checked by UV sensitivity. Induction of the SOS system was assessed with strains GW [40] and GW Δ(srlR-recA)::Tn10 as described [39]. All plasmids were obtained from the ASKA library of the NARA Institute [36]. The plasmid pRecA-Htg harbors an E. coli recA wild-type gene encoding an N-terminal histidine-tagged RecA protein (termed here as RecA-Htg variant), whereas the pRecA-GFP plasmid harbors this modified recA gene fused with the gfp gene (green fluorescent protein) that renders a RecA-GFP protein fusion. Expression of the genes encoding the RecA-Htg or RecA-GFP proteins is under the control of an IPTG- (isopropyl beta-D-thiogalactoside)-inducible promoter, which is strictly repressed by the LacI repressor expressed from the PlacIq promoter. Plasmid pCA24N was the cloning vector, encoding a non-fused GFP protein. A detailed construction of those plasmids has been described previously [36].

Conjugation experiments

Matings were performed in LB at 37°C for 1 h with donor ELE1 (Hfr ΔfhuD::kan) and the receptor strains harboring pCA24N (CmR) plasmid or its derivatives. Transconjugants were selected on LB plates supplemented with chloramphenicol and kanamycin.

Determination of growth curves

Strains were cultured overnight in LB. A 1/ dilution was carried out in fresh LB and incubated in well plates. The plates were incubated at 37°C with agitation in an Infinite M multiwell fluorimeter (Tecan, Switzerland). Optical density at nm was recorded every 10 min for 10 h. Each point of the curve represents the average value of four measurements in four different parallel experiments.

Viability assays

Wild-type and ΔrecA cells were isolated from swarming plates and stained with the Live/Dead BacLight Kit according to the manufacturer's instructions (Molecular Probes, Eugene, Oreg).

Motility assays

Overnight cultures of the different strains were grown in LB and inoculated with a sterile toothpick on swimming and swarming plates. The swimming motility plates were prepared with % "Difco" agar, % "Difco" Bacto-tryptone, % "Difco" Yeast Extract and % NaCl [7]. The swarming motility plates were prepared with % "Difco" agar, % "Difco" Bacto-tryptone, % "Difco" Yeast Extract, % NaCl and % D-(+)-glucose. The plates were dried for 5–6 h at room temperature before being inoculated and were photographed after a h incubation at 37°C. When the assays were performed with strains carrying pCA24N or its derivatives, the plates contained chloramphenicol.

Flagella visualization

Cells isolated from the periphery of a swarming colony were deposited onto a microscope slide with a 10 μl LB drop and covered with a coverslip. Flagella staining was carried out as described [54]. The slide was propped vertically and 10 μl of dye was applied to the top edge of the coverslip to stain the sample by capillary action. A total of well-stained cells from each strain were observed by means of an Olympus BX61 microscope under 1, × magnification. Images were recorded with the image capture software of the DP70 CCD camera coupled to the microscope.

Transcriptome analysis of swarm cells

The cells from the periphery of six wild-type and ΔrecA colonies grown on swarming plates were collected, resuspended in saline solution, and harvested by centrifugation. Total-RNA was isolated with the RNeasy mini kit (QIAGEN, Valencia, CA) and Dnase-I digestion was performed on-column. RNA samples were quantified, checked by gel electrophoresis and stored at °C until further use. RNA preparations from each sample were used as templates for cDNA synthesis by employing 10 μg of total RNA as template for reverse transcriptase to produce cDNA labeled with either Cy3- or Cy5-dCTP (Superscript™ Indirect cDNA labeling System). Two different array hybridizations were performed using RNA samples extracted from six independent swarming colonies for the wild-type and the ΔrecA under analogous conditions. To correct for possible differences in Cy3 and Cy5 dye incorporation, the cDNAs were labeled with Cy3 dye in two hybridizations and with Cy5 dye in the other two (dye swapping). Thus, four independent array hybridizations were performed and analyzed as described.

The E. coli DNA microarrays, containing more than 95% of the 4, ORFs identified in the E. coli K12 genome, were purchased at the Wisconsin Gene Expression Center, (University of Wisconsin). Prehybridization was performed at 42°C for 30–45 min in 6 × SSC, % SDS and 1% BSA, and slides were rinsed five times with distilled water. Cy5 and Cy3 cDNA probes were mixed ( pmol of each label) in a final volume of 70 μl of hybridization buffer (50% formamide, 3 × SSC, 1% SDS, 5 × Denhardt's and 5% Dextransulphate). The probe was denatured at 95°C for 5 min and applied to the slide using a LifterSlip (Erie Scientific, Portsmouth, NH). Slides were then incubated at 42°C for 16 h in hybridization chambers (Array-It) and subsequently washed sequentially in the following solutions: twice in × SSPE, % TWEEN20, twice in × SSPE and finally in × SSPE for 5 min each. Slides were dried by centrifugation at g for 1 min before scanning. Images from Cy3 and Cy5 channels were equilibrated and captured with a GenePix B (Axon, Molecular Devices, Union City, CA) and spots quantified using GenPix software (Axon). The data from each scanned slide were first escalated and normalized using the Lowess method, and then log-transformed. The mean of the log-ratio intensities and the standard deviation among replicates were generated. Two statistical approaches were used to identify differentially regulated genes: a t-test and a z-score. A strong stringency was used. Only genes with a gene signal >50, p-value < , Z-score > or < and fold change >3 or < -3 were considered differentially expressed.

Survival following UV irradiation

A fresh overnight culture was evenly applied with a loop on the surface of a square 12 cm plate containing Luria-Bertani medium harnessed with agar (15 g/L) supplemented with chloramphenicol and % D-(+)-glucose. The plate was partially covered by a sheet of paper foil and placed under a UV (ultraviolet light) lamp ( nm). The foil was progressively retracted following three exposures of 60 J/m2. The irradiated plate was incubated in the dark at 37°C for 24 h.

SOS induction assays

SOS induction was assessed by measuring the norfloxacin-mediated SOS induction of a transcriptional dinB::lacZ gene fusion, as previously described [39].

Time-lapse videos

The leading edges of the JMGΔrecA (pCA24N); JMGΔrecA (pRecA-Htg) and JMGΔrecA (pRecA-GFP) colonies grown on swarming plates were captured. Magnification, 1, ×; frame capture rate, 3 frames/s taken with a CCD DP70 camera coupled to a BX61 Olympus microscope.

Protein purification

E. coli MG cells bearing either pRecA-Htg or pRecA-GFP plasmids were used to overexpress the RecA-Htg and RecA-GFP proteins, respectively. E. coli MG cells bearing the pRecA-Htg plasmid were grown at 37°C to middle exponential phase and the expression of the RecA-Htg protein was induced for min by adding IPTG. Cells were harvested, resuspended in buffer A (50 mM potassium phosphate pH , 10 mM β-mercaptoehanol, 10% glycerol) containing 50 mM NaCl, disrupted by sonication and centrifuged. The RecA protein supernatant was loaded onto a Q-sepharose column (Pharmacia, Sweden) equilibrated with the same buffer. The flow-through was then loaded onto a Niquel column (Qiagen, Valencia, CA) equilibrated with buffer A containing 50 mM NaCl. The Niquel column was washed with a column volume of buffer A containing 1 M NaCl and 20 to 90 mM imidazol. The RecA-Htg protein was eluted with buffer A containing mM NaCl and a to mM imidazol gradient. Fractions containing RecA-Htg protein were recovered and loaded onto a Hydroxyapatite (HA) column (BioRad, Hercules, CA) equilibrated with the same buffer containing mM NaCl. The column was washed with buffer A and eluted with a linear to mM K+ phosphate gradient containing mM NaCl. Fractions containing RecA-Htg were recovered and dialyzed against buffer B (50 mM Tris-HCl pH, 50% glycerol) containing mM NaCl and stored at °C. E. coli MG cells bearing pRecA-GFP plasmid were grown to middle exponential phase at 30°C and expression of the RecA-GFP protein was induced for min by adding IPTG. Cells were harvested, resuspended in buffer A (50 mM K+ phosphate pH , 10 mM β-mercaptoethanol, 10% glycerol) containing 1 M NaCl, disrupted by sonication and centrifuged. The fraction of the RecA-GFP protein found in the supernatant was loaded onto a Niquel column equilibrated with the same buffer containing 1 M NaCl. The Niquel column was washed with a column volume of buffer A containing 1 M NaCl and 20 mM imidazol and eluted with buffer A containing 1 M NaCl and a 50 to 80 mM imidazol gradient. Fractions containing the RecA-GFP protein were dialyzed against buffer B containing 1 M NaCl and loaded onto a HA column equilibrated with the same buffer. The RecA-GFP was eluted with buffer B containing 50 mM potassium phosphate and 1 M NaCl. Fractions containing RecA-GFP were recovered and dialyzed against buffer B containing mM NaCl and stored at °C. The wild-type RecA protein was purchased from Amersham Biosciences (GE, Sweden). Protein concentration was determined by the Bradford method and expressed as mol of protein monomers.

ATPase activity measurement

Standard reactions were incubated for an indicated time at 37°C in buffer C (50 mM (Tris-HCl pH, 10 mM magnesium acetate, 5% glycerol) containing 20 mM NaCl with circular 3,nucleotide pGEM ss-DNA (10 μM in nucleotides) and the indicated amounts of RecA-Htg or RecA-GFP proteins in a volume of 20 μl. Incubations were performed at 37°C at different time intervals and in 20 μl total volume. Different ATP concentrations were used, containing 20 nM of [γ-32P]-ATP (,) for 15 min at 37°C in buffer C. The ATPase activity was determined by thin-layer chromatography, as previously described [55].

DNA three-strand exchange reaction

The 3,bp KpnI-cleaved pGEM ds-DNA (20 μM in nucleotides), and homologous circular 3,nucleotide pGEM ss-DNA (10 μM in nucleotides) were incubated with RecA or RecA-GFP (2 μM) and SSB (1 μM) in buffer C containing 40 mM NaCl and 1 mM ATP 60 min at 37°C. The samples were deproteinized as described [56, 57], and fractionated through % AGE with Ethidium Bromide. The signal was quantified using a Phosphorimager (Amersham Biosciences-GE, Sweden).

Electron microscopy

Circular pGEM ss-DNA (10 μM) was pre-incubated with various amounts of RecA, RecA-Htg or RecA-GFP for 20 min at 37°C in buffer C containing 20 mM NaCl and 1 mM ATPγS, and incubated for 40 min at 37°C. DNA-protein complexes were visualized by negative staining with 1% uranyl acetate [58].

References

  1. 1.

    Harshey RM: Bacterial motility a surface: many ways to a common goal. Annu Rev Microbiol. , /annurev.micro

    ArticleCASPubMed Google Scholar

  2. 2.

    Soutourina OA, Bertin PN: Regulation cascade of flagellar expression in Gram-negative bacteria. FEMS Microbiol Rev. , /S(03)

    ArticleCASPubMed Google Scholar

  3. 3.

    Stock JB, Surette MG: Chemotaxis. Escherichia coli and Salmonella: Cellular and Molecular Biology. Edited by: Neidhardt FC, Curtis III R, Ingraham JL, Lin ECC, Low KBB, Magasanik W S, Reznikoff M, Riley M, Schacchter, Umbarger HE. , Washington, DC: American Society for Microbiology, 2

    Google Scholar

  4. 4.

    Baker MD, Wolanin PM, Stock JB: Signal transduction in bacterial chemotaxis. Bioessays. , /bies

    ArticleCASPubMed Google Scholar

  5. 5.

    Harshey RM, Matsuyama T: Dimorphic transition in E. coli and S. typhimurium : surface-induced differentiation into hyperflagellate swarmer cells. Proc Natl Acad Sci USA. , /pnas

    PubMed CentralArticleCASPubMed Google Scholar

  6. 6.

    Burkart M, Toguchi A, Harshey RM: The chemotaxis system, but not chemotaxis, is essential for swarming motility in Escherichia coli . Proc Natl Acad Sci USA. , /pnas

    PubMed CentralArticleCASPubMed Google Scholar

  7. 7.

    Zorzano M-P, Cuevas M-T, Hochberg D, Gómez-Gómez J-M: Reaction-diffusion model for pattern formation in E. coli swarming colonies with slime. Phys Rev E. , /PhysRevE

    Article Google Scholar

  8. 8.

    Daniels R, Vanderleyden J, Michiels J: Quorum sensing and swarming migration in bacteria. FEMS Microbiol Rev. , /j.femsre

    ArticleCASPubMed Google Scholar

  9. 9.

    Fraser GM, Hughes C: Swarming motility. Curr Opin Microbiol. , 2: /S(99)

    ArticleCASPubMed Google Scholar

  10. Belas R: Proteus mirabilis and other swarming bacteria. Bacteria as Multicellular Organisms. Edited by: Sharpio JA, Dworkin M. New York Oxford University Press,

  11. Eberl L, Søren M, Givskov M: Surface motility of Serratia liquefaciens MG1. J Bacteriol. ,

    PubMed CentralCASPubMed Google Scholar

  12. Kearns DB, Losick R: Swarming motility in undomesticated Bacillus subtilis . Mol Microbiol. , /jx.

    ArticleCASPubMed Google Scholar

  13. Köhler T, Curty LK, Barja F, van Delden C, Pechère J-C: Swarming of Pseudomonas aeruginosa is dependent on cell-to-cell signaling and requires flagella and pili. J Bacteriol. , /JB

    PubMed CentralArticlePubMed Google Scholar

  14. Rashid MH, Kornberg A: Inorganic polyphosphate is needed for swimming, swarming, and twitching motilities of Pseudomonas aeruginosa . Proc Natl Acad Sci USA. , /pnas

    PubMed CentralArticleCASPubMed Google Scholar

  15. Toguchi AM, Siano M, Burkart M, Harshey RM: Genetics of swarming motility in Salmonella enterica serovar Typhimurium: critical role for lipopolysaccharide. J Bacteriol. , /JB

    PubMed CentralArticleCASPubMed Google Scholar

  16. Adler J: The effect of environmental conditions on the motility of Escherichia coli . J Gen Microbiol. ,

    ArticleCASPubMed Google Scholar

  17. Amsler CD, Cho M, Matsumura P: Multiple factors underlying the maximum motility of Escherichia coli as cultures enter post-exponential growth. J Bacteriol. ,

    PubMed CentralCASPubMed Google Scholar

  18. Maurer LM, Yohannes E, Bondurant SS, Radmacher M, Slonczewski JL: pH regulates genes for flagellar motility, catabolism, and oxidative stress in Escherichia coli K J Bacteriol. , /JB

    PubMed CentralArticleCASPubMed Google Scholar

  19. McCarter LL: Regulation of flagella. Curr Opin Microbiol. , 9: /j.mib

    ArticleCASPubMed Google Scholar

  20. Storz GT, Hengge-Aronis R, Eds: Bacterial Stress Responses. , Washington, DC: American Society for Microbiology

    Google Scholar

  21. Walker GC, Smith BT, Sutton MD: The SOS response to DNA damage. Bacterial Stress Responses. Edited by: Stortz G, Hengge-Aronis R. , Washington, DC: American Society for Microbiology,

    Google Scholar

  22. Kowalczykowski SC, Dixon DA, Eggleston AK, Lauder SD, Rehrauer WM: Biochemistry of homologous recombination in Escherichia coli . Microbiol Rev. ,

    PubMed CentralCASPubMed Google Scholar

  23. Kuzminov A: Recombinational repair of DNA damage in bf Escherichia coli and bacteriofage lambda. Microbiol Molec Biol Rev. ,

    CAS Google Scholar

  24. Lusetti SL, Cox MM: The bacterial RecA protein and the recombinational DNA repair of stalled replication forks. Ann Rev Biochem. , /annurev.biochem

    ArticleCASPubMed Google Scholar

  25. Courcelle J, Hanawalt PC: RecA-dependent recovery of arrested DNA replication forks. Annu Rev Genet. , /annurev.genet

    ArticleCASPubMed

Sours: https://bmcbiol.biomedcentral.com/articles//

Motile is e coli

Transcriptional control of motility enables directional movement of Escherichia coli in a signal gradient

Abstract

Manipulation of cellular motility using a target signal can facilitate the development of biosensors or microbe-powered biorobots. Here, we engineered signal-dependent motility in Escherichia coli via the transcriptional control of a key motility gene. Without manipulating chemotaxis, signal-dependent switching of motility, either on or off, led to population-level directional movement of cells up or down a signal gradient. We developed a mathematical model that captures the behaviour of the cells, enables identification of key parameters controlling system behaviour, and facilitates predictive-design of motility-based pattern formation. We demonstrated that motility of the receiver strains could be controlled by a sender strain generating a signal gradient. The modular quorum sensing-dependent architecture for interfacing different senders with receivers enabled a broad range of systems-level behaviours. The directional control of motility, especially combined with the potential to incorporate tuneable sensors and more complex sensing-logic, may lead to tools for novel biosensing and targeted-delivery applications.

Introduction

Cellular motility is a key microbial behaviour with a broad range of functions in natural systems, including navigation of the environment1, biofilm formation2, and control of biodiversity in consortia3. Bacteria move in a self-propelled manner by drawing energy from their surroundings and have developed mechanisms to effectively navigate their environments. Bacteria also monitor their environment and respond to changes therin4, 5. Controlling cellular motility in response to an external signal can facilitate the development of biosensors6,7,8 or micromachines that use microbes to enable movement in microfluidic environments9, 10, with potential applications as targeted-delivery agents.

Escherichia coli swim through their environment powered by the rotation of their flagella11. The flagella are self-assembled structures made up of a hook, filament and motor12. The hook is flexible while the filament is rigid and its shape is determined by the direction of flagellar rotation. The motor is powered by a proton gradient that generates the torque required for flagellar rotation13, 14. In the absence of attractants or repellents to guide the direction of movement, bacteria follow a random walk pattern involving a series of runs and tumbles determined by the direction of flagellar rotation1. Chemoreceptors bind to attractants resulting in a change in the phosphorylation state of proteins that control the direction of flagellar rotation15, 16, reducing the tumbling frequency of cells, and allowing cells to run in more direct paths towards attractants17, 18.

Different strategies have been pursued to engineer motility in E. coli in response to target signal molecules19. Several efforts have used E. coli strains rendered non-motile via deletion of motility proteins and then restored motility via inducible expression of the deleted gene from a plasmid20, 21. For example, control of motility was achieved in a cheZ-deletion E. coli strain by using a theophylline-sensitive riboswitch to control expression of CheZ, a protein that controls cellular tumbling rate22. Control of directional motility in response to target compounds has been achieved by engineering E. coli chemoreceptors to recognize target compounds via directed evolution23, rational design of the chemoreceptor specificity24, and designing hybrid chemoreceptors consisting of an E. coli signalling domain and a sensory domain from other species that recognizes a target compound25. While such strategies for controlling directional movement targeting E. colis chemotactic network have led to some success, the limited number of natural chemoreceptor scaffolds imposes constraints on ligands that can be targeted. Such engineering challenges have led to alternative approaches, such as converting the desired target to a compound recognized by E. colis native chemotactic machinery26.

Engineering directional motility in response to signal molecules non-native to E. coli’s sensing machinery, without manipulation of chemotaxis, would expand the use of bacteria in sensing and actuation applications. Interestingly, some enzymes have been observed to exhibit an increase in diffusivity that correlates to increasing concentrations of their substrate. The substrate concentration-dependent enhancement in diffusivity enables directional movement of the enzymes up gradients of their cognate signals26. For example, urease exhibits an increase in diffusivity with increasing concentrations of its substrate urea, and this enables directional movement of urease up a substrate gradient27, 28. Similar directional migration was observed with catalase molecules in a hydrogen peroxide gradient28. This ability of enzymes to enable directed self-propulsion has been harnessed to drive polystyrene beads coated with urease or catalase up the gradients of their cognate substrates29. This mechanism of directional movement resulting from substrate concentration-dependent enhanced diffusivity could be applied to engineer directional movement of cells in a signal gradient by enhancing cellular diffusivity in the presence of a signal. We hypothesized that transcriptional control of a key motility gene in response to a signal would allow signal-dependent manipulation of cellular diffusivity and enable population-level directional movement of cells in a signal gradient.

Natural quorum sensing (QS) systems enable cell density-dependent control of gene expression in bacteria based on the production and detection of QS signal molecules30, 31. QS systems have been used by synthetic biologists for tuneable transcriptional control of gene expression32, 33, and the expression of a QS-signal synthase in E. coli has been shown to generate a signal gradient across a petri dish34. QS systems have been widely used for construction of genetic circuits in individual cells35, 36 and to enable communication in synthetic consortia37, 38. Previous work has used QS regulatory elements to control motility in E. coli strains lacking cheZ20 or motB21, where the missing motility gene was expressed from a QS-signal inducible promoter. The ability to reliably control gene expression and manipulate cells in cell-generated gradients make QS regulatory components ideal tools for examining transcriptional control of motility in E. coli in a signal gradient.

Here, we engineered E. coli strains where motility is tightly regulated by transcriptional control of the motor protein, MotA, and is induced by a QS signal molecule. We demonstrate robust directional control of motility in the engineered ‘receiver’ cells that was not only achieved in a gradient of exogenously added signal but also in a bio-generated gradient of the signal produced by ‘sender’ cells. We show that our sender-receiver architecture is modular and can be used to generate a range of sensitivity and responses to the signal. Further, we describe a mathematical model that provides insight into key aspects of system behaviour and enables predictive-design of motility-based pattern formation by cells.

Results

Design and characterization of signal-molecule dependent motility in E. coli

To build a system where the motility of E. coli is transcriptionally regulated by QS components, we used the esa QS system to control expression of MotA in an E. coli motA deletion strain (∆motA)39. MotA is a motor protein that provides a channel for the proton gradient required for generation of torque40. ∆motA strains can build flagella but are non-motile because they are unable to generate the torque required for flagellar rotation14. Expression of motA from a plasmid has been shown to restore motility in ∆motA strains41. Previous efforts to regulate motility using the activation-based lux QS system faced challenges in achieving tight regulation of the target gene and basal expression of the gene was sufficient to restore motility in the absence of the signal21. The esa QS system is from the plant pathogen Pantoea stewartii42, and has been shown to provide tight regulation of genes downstream of the esaR promoter (PesaR)43. The QS regulator EsaR represses PesaR expression by binding to the promoter. The addition of acyl-homoserine lactone QS signal molecule, 3-oxo-hexanoyl homoserine lactone (3OC6HSL)43, induces gene expression from PesaR by triggering EsaR to release the promoter and allow RNA polymerase to bind and initiate transcription. Here, we constructed a two-plasmid system in ∆motA cells consisting of an EsaR-expression plasmid and a plasmid in which motA is placed under the control of PesaR. As shown in Fig. 1a, expression of motA is repressed by the transcriptional repressor (EsaR) in the absence of 3OC6HSL. In the presence of 3OC6HSL, EsaR is expected to dissociate from the promoter triggering expression of motA, thereby restoring motility in ∆motA cells. The green fluorescent protein was also placed downstream of PesaR to allow characterization of expression from PesaR, if motA expression was not sufficient to provide detectable motility in our assays. This strain is designated as the Communication-dependent Motility (CoMot) strain.

Engineered CoMot strains display 3OC6HSL-dependent motility: (a) Illustration of 3OC6HSL-dependent motA expression in the CoMot strain. Expression of motA is under the control of the PesaR promoter. esaR is constitutively expressed from a σ70-dependent promoter and represses PesaR. In the absence of 3OC6HSL, motA expression is repressed and the cell is non-motile. Following addition of 3OC6HSL, PesaR is de-repressed and motA is expressed. MotA generates the torque required to rotate the flagella and cellular motility is restored. (b) Plates were inoculated with (i) ΔmotA, (ii) ΔmotA transformed with plasmids containing PesaR-motA (iii) CoMot cells (ΔmotA transformed with plasmids containing PesaR-motA and Pσ70-esaR) on plates without 3OC6HSL and (iv) CoMot cells on plates with 1 μM 3OC6HSL and incubated at 30 °C for 36 h. Representative plate images are shown. (c) Migration radius was measured as the distance between the inoculation point and the visible edge of migration of cells on the plate for cases (i–iv). Error bars represent one standard deviation from the mean migration radius of three biological replicates. (d) Plates with 3OC6HSL concentration ranging from 0 to 10 μM were inoculated with CoMot or CoMot+ (ΔmotA transformed with plasmids containing PesaR-motA and Pσ70-esaR-D91G). The migration radius was measured after 36 h at 30 °C. Error bars represent one standard deviation from the mean migration radius of three biological replicates.

Full size image

A motility assay using semi-solid agar plates that allow cells, which were inoculated by stabbing 1 μL of cells into the agar, to migrate through the media, was used to quantify motility. The migration radius was measured as the distance between the inoculation point and the visible edge of the migrating cells on the plate. As expected, migration was not observed on plates inoculated with ∆motA cells (Fig. 1b and c; case i). A migration radius of 35 mm was observed on plates inoculated with ∆motA cells transformed with a plasmid containing PesaR-motA (case ii), indicating that constitutive expression of MotA from PesaR was sufficient to restore motility in ∆motA cells. To assess if 3OC6HSL-inducible motility could be achieved in CoMot cells, they were inoculated on plates with and without 3OC6HSL. As seen in case iii, a migration radius of only 5 mm was observed in the absence of 3OC6HSL. This was comparable to the migration radius of ∆motA cells, demonstrating that motility in the absence of 3OC6HSL is minimal. A seven-fold increase in the migration radius exhibited by CoMot cells was observed in the presence of micromolar concentrations of 3OC6HSL (case iv), indicating that engineered cells exhibit signal-molecule dependent motility.

CoMot cells were inoculated on plates with 0, 10, 50, , , , , and  nM 3OC6HSL, to assess their sensitivity to the signal molecule. As shown in Fig. 1d, an increase in migration radius was observed with increasing 3OC6HSL concentrations, where  nM 3OC6HSL was required to observe a migration radius larger than the background migration radius observed in the absence of 3OC6HSL (p = ). To increase the 3OC6HSL sensitivity of the cells, we replaced the transcriptional repressor EsaR with a variant, EsaR-D91G and designated this strain as CoMot+. E. coli cells with EsaR-D91G have been reported to display a fold higher sensitivity to 3OC6HSL compared to wild-type EsaR in a luminescence-based promoter assay43. As seen in Fig. 1d, CoMot+ cells required 50 nM 3OC6HSL to display a migration radius above background (p = ), demonstrating that the CoMot+ strain does exhibit increased sensitivity to 3OC6HSL. In addition,  nM of 3OC6HSL was required for CoMot cells to reach the edge of the plate in 36 hours, while only  nM was required for CoMot+ cells (Fig. 1d).

Characterization of directional movement of the CoMot variants in a 3OC6HSL gradient

To assess if CoMot and CoMot+ cells display directional movement in a signal gradient,  μmoles of 3OC6HSL was added on a membrane (3OC6HSL source), placed  cm from the edge of the plate, and allowed to diffuse and establish a gradient for 8 h prior to inoculation of cells at the centre of the plate. 1 μM of 3OC6HSL would be the final concentration if the  μmoles diffused uniformly through the 25 mL plate. As shown in Fig. 2, both CoMot variants reached the edge of the plate (40 mm) in the forward direction towards the 3OC6HSL source by 36 h. Here, we define forward migration distance as distance between the inoculation point and the visible edge of cells that have migrated up the signal gradient. The reverse migration distance (distance between the inoculation point and the visible edge of cells that have migrated down the signal gradient) was 10 mm for CoMot and 18 mm for CoMot+. Therefore, directional movement of both CoMot variants up the 3OC6HSL gradient was observed. Directional movement was not displayed by ∆motA cells that contain PesaR-motA and constitutively express motA (Fig. 2), indicating that regulation by EsaR or EsaR-D91G in the 3OC6HSL gradient is required for directional movement.

CoMot and CoMot+ cells in a 3OC6HSL gradient show directional movement towards the 3OC6HSL source: A 3OC6HSL gradient was established by adding  μmoles of 3OC6HSL to a Whatmann membrane and allowing it to diffuse for 8 h. 1 μM of 3OC6HSL would be the final concentration if  μmoles of 3OC6HSL diffused uniformly through the plate. CoMot, CoMot+ or cells that constitutively express motA (∆motA transformed with a plasmid containing PesaR-motA) were then inoculated at the centre of the plate. Images were obtained following 0, 18, 24 and 36 h of incubation at 30 °C. The assay was run in triplicate for each strain and representative images are shown.

Full size image

We then examined the sensitivity of CoMot and CoMot+ cells in gradients established using varying amounts of 3OC6HSL. As seen in Fig. 3a, both CoMot variants showed an increase in the forward migration distance with increasing 3OC6HSL concentrations. Similar to the uniform 3OC6HSL-titration results, a lower 3OC6HSL concentration was required to observe forward migration distances greater than background levels with the CoMot+ ( nM) than CoMot cells ( nM). Both strains displayed directional movement towards the 3OC6HSL source (Fig. 3b). We also observed that the forward migration distance decreased when cells were inoculated at increasing distances from the 3OC6HSL source indicating that motility response is affected by the spatial arrangement of the signal and cells (Supplementary Fig. S1).

Motility assays and simulations show 3OC6HSL-dependent directional movement of cells in a 3OC6HSL gradient: (a) 3OC6HSL gradients were established by adding 0– μmoles of 3OC6HSL on a Whatmann membrane and allowing it to diffuse for 8 h. CoMot and CoMot+ cells were then inoculated at the centre of the plate. The forward migration distance was measured as the distance between the inoculation point and the visible edge of migration of cells up the signal gradient after 24 h of incubation at 30 °C. Error bars represent one standard deviation from the mean forward migration distance of three biological replicates. (b) Representative images of plates inoculated with CoMot+ cells. (c) Results from simulation of the migration response of cells in a similar set up as in (b). Signal gradients were simulated by using 0, 17 or  μmoles/m2 of the signal near the edge of the plate. *107 CoMot+ cells/m2 was used as the inoculum at the centre of the plate. The log10 of the total cell concentration after a simulation time of 24 h is shown in the images.

Full size image

Modelling of signal molecule-guided bacterial motility

To gain insight into the key parameters controlling motility in the engineered strains and to identify factors contributing to the observed directional movement, we developed a mathematical model. The distribution of CoMot cells in response to the signal molecule was modelled using Equations (1–3). We used a Michaelis Menten-type term (term III) to model the rate of switching from static (s) to motile cells (m) in the presence of the signal molecule (A) and an inhibition-kinetics equation to capture the switching from motile to static cells (term IV). Parameters k1 and γ capture the maximum rate of switching from static to motile and motile to static, respectively. K2 and K4 define the sensitivity of cells to A. The displacement of motile cells is modelled via a diffusion term (term I), where Dm is the effective diffusivity of cells. We used Monod kinetics to capture the exponential growth of cells. λ represents growth rate in term II. Diffusion of the signal molecule is captured in term V, where Da represents the diffusivity of A. A two-dimensional version of the experiment was modelled using geometry (plate size and location of signal and cells) similar to the experimental setup. The model was simulated using parameters estimated experimentally or from the literature (Supplementary Table S1). When experimental quantification was not possible and when quantitative values were unavailable in literature parameter values were chosen based on educated guesses in biologically feasible regimes. These values were then tuned to fit experimental findings when required.

$$\frac{\partial m}{\partial t}=\,\mathop{\overbrace{{D}_{m}(\frac{{\partial }^{2}m}{\partial {x}^{2}}+\frac{{\partial }^{2}m}{\partial {y}^{2}})}}\limits^{I}+\mathop{\overbrace{\lambda m}}\limits^{II}\,+\mathop{\overbrace{(\frac{{k}_{1}A\,}{{K}_{2}+A})s}}\limits^{III}-\mathop{\overbrace{(\frac{\gamma }{1+\frac{A}{{K}_{4}}})m}}\limits^{IV}$$

(1)

$$\frac{\partial s}{\partial t}=\mathop{\overbrace{\lambda s}}\limits^{II}-\mathop{\overbrace{(\frac{{k}_{1}A\,}{{K}_{2}+A})s}}\limits^{III}+\mathop{\overbrace{(\frac{\gamma }{1+\frac{A}{{K}_{4}}})m}}\limits^{IV}$$

(2)

$$\frac{\partial A}{\partial t}=\mathop{\overbrace{{D}_{a}(\frac{{\partial }^{2}A}{\partial {x}^{2}}+\frac{{\partial }^{2}A}{\partial {y}^{2}})}}\limits^{V}$$

(3)

We started by simulating the migration response of CoMot+ cells in gradients established using 0, 17 or  µmole/m2 of the signal molecule. In the motility assays,  μmoles of 3OC6HSL were added to the membrane and used to generate a gradient equivalent to 1 μM. In the 2D simulations,  μmoles was initially distributed across the area of the membrane (*10−4 m2). Therefore, an initial signal concentration of  μmoles/m2 ( μmoles/*10−4 m2) on the membrane was used, where  μmoles/m2 would be the final concentration if the signal diffused uniformly across the simulated area of the plate. Images after a simulated time of 24 h are shown in Fig. 3c. In the absence of the signal, simulated cells remained at the inoculation point. Similar to experimental observations (Fig. 3b), an increase in movement of simulated cells towards the signal source was observed with increase in signal concentration. To model the difference in 3OC6HSL sensitivity of CoMot and CoMot+ cells, we increased the sensitivity parameter, K2 from 1 to nmole/cm2. Time course simulations of CoMot and CoMot+ cells in a gradient established using  µmole/m2 of the signal are shown in Supplementary Fig. S2. CoMot+ cells displayed approximately 2-fold higher forward migration distance than CoMot cells after a simulation time of 24 h, where a cell concentration ≥108 was used as the cut off for the migration distance in the simulations. Thus, our model is representative of the system and captures key system properties - signal-molecule dependent directional movement in a gradient and the difference in the 3OC6HSL sensitivity between CoMot and CoMot+.

To understand the effect of the two switching rates on system behaviour, we varied k1 and γ and plotted the ratio of motile to static cells (Fig. 4a and supplementary Fig. S3a). An increase in the motile to static cell ratio was observed with increasing k1 and decreasing γ. The increase in this ratio leads to an increase in both the forward and reverse migration distances. Thus, varying the switching rates allow for tuning of the magnitude of motility response of the cells. Our simulations showed that m/s increases as cells move towards the 3OC6HSL source, indicating that motile cells dominate the population up the signal gradient and static cells dominate down the gradient. Thus, a cell once motile, though capable of moving in any random direction, remains motile if it happens to move up the gradient, but switches to static if it migrates down the gradient and into a region of low signal. This static population continues to accumulate and grow. Directional movement then results from population-level movement of motile cells towards the signal source.

The rate of switching from static to motile (k1) and the diffusivity of the signal (Da) are keys parameters controlling migration of cells: In the simulations, the gradient was established using 85 μmoles/m2 of the signal near edge of the plate (migration distance = 4 cm). *107 static cells/m2 was used as the inoculum at the centre of the plate (migration distance = 0 cm). (a) k1 was varied from – h−1 while all other parameters were held constant at values defined in the base parameter set. For each value of k1, the ratio of motile to static cells (m/s) across the diameter of the plate (migration distance = −4 to 4 cm) was plotted after a simulation time of 24 h. The ratio was only calculated at points with a total cell concentration ≥108 cells/m2. (b) Simulations were run varying Da from −10 cm2/h while holding all other parameters constant. Forward and reverse migration distances were measured as distances from the inoculation point (migration distance = 0) towards and away from the signal source at which a total cell concentration ≥108 cells/m2 was observed after a simulation time of 24 h.

Full size image

Simulations varying the signal diffusivity (Da) were run to assess its effect on the established gradient on migration response. The signal concentration across the plate (Supplementary Fig. S3b) and the forward and reverse migration distances were examined (Fig. 4b) for each simulated Da. Directional movement, as indicated by a greater forward compared to reverse migration distance, was only observed with Da sufficient to establish a gradient (Da <  cm2/h). It is thus evident that the established signal gradient drives the directional movement of the cells.

K2 and K4 capture the 3OC6HSL-sensitivity of cells when switching from static to motile and motile to static, respectively. Our simulations showed that a 104-fold increase in K2 resulted in a fold increase in the signal concentration required to achieve a forward migration distance equivalent to reaching the edge of the plate in 24 hours, while a 108-fold increase in K4 resulted in only a fold increase (Supplementary Fig. S3c,d). Thus, the sensitivity of the cells to 3OC6HSL when switching from static to motile has a larger effect on overall system behaviour than the sensitivity when switching from motile to static. At high K4 (K4 >  nmole/cm2), where switching from motile to static becomes independent of 3OC6HSL, directional movement of cells was still observed. Here, dilution of MotA as cells grow and divide leads to switching from motile to static if the local concentration of 3OC6HSL is not sufficient to induce additional motA expression. However, in simulations with low K4 (K4 <  nmole/cm2), directional behaviour was not observed (Supplementary Fig. 3e,f). In this case, cells never switch back to static once they become motile and thus continue migrating across the plate. Overall, these simulations indicate that while it is essential that cells are able to switch from motile to static (term IV), observed experimental behaviour and directional movement can be captured if term IV is modelled as dependent or independent of signal concentration.

We observed that both the forward and reverse migration distances increased when the effective diffusivity of cells (Dm) was increased (Supplementary Fig. S3g). Directional movement towards the signal was not observed at values of Dm fold greater than the experimentally estimated  cm2/h. This could be because in the simulated geometry-scale at high Dm, once motile, the diffusivity of cells was sufficient to keep them motile regardless of the local signal concentration. These studies have shown that the distribution of cells depends both on the parameters regulating motility and the established gradient.

Model-guided pattern formation

We next explored if our model can be used to predict cell distribution patterns formed in response to changing the spatial arrangement of the signal and cells. Representative patterns of cell distribution that were simulated under different spatial arrangements of signal and cells and tested in similar experimental set ups are shown in Fig. 5. In case (i), the pattern predicted to be formed by cells inoculated diametrically opposite to each other in a plate with a signal source at its centre is in agreement with the pattern experimentally observed on a petri dish with CoMot+ cells inoculated in a similar set up. In case (ii), we were able to successfully predict the pattern formed by cells inoculated at the centre of a square petri dish with signal sources along a diagonal. We were thus able to predict the distribution of cells in response to different initial arrangements of the signal and cells, thereby enabling predictive-design of pattern formation by cells.

Predictive design and experimental verification of motility-induced pattern formation: (a) Results from simulation of the patterns formed by cells in response to different spatial arrangements of signals and starting inoculum of cells. (i) *107 CoMot+ cells/m2 were used as the inoculum at opposite edges of a circular plate and a gradient was established using 85 μmoles/m2 of the signal at the centre of the plate. 85 μmoles/m2 of the signal is equivalent to an experimental 3OC6HSL concentration of  nM. Images were obtained after a simulation time of 36 h. (ii) *107 CoMot+ cells/m2 were used as the inoculum at the centre of a square plate in which the signal gradient was established using two signal sources along a diagonal. 85 μmoles/m2 of the signal was used for each source to establish the gradient. Images were obtained after a simulation time of 36 h. (b) Experimental verification of patterns predicted by the model. (i) The equivalent of  nM of 3OC6HSL was added on a membrane 12 h prior to inoculation of CoMot+ cells at opposite edges of the plate. (ii) Membranes with the equivalent of  nM of 3OC6HSL were placed along the diagonal of the plate 12 h prior to inoculation of CoMot+ cells at the centre. Each pattern was experimentally verified in triplicate and representative images obtained after 36 h of incubation at 30 °C are shown.

Full size image

Engineering CoMot variants to migrate down a 3OC6HSL gradient

We sought to engineer a strain that migrates down a 3OC6HSL gradient. The PesaS promoter, which is activated by EsaR in the absence of 3OC6HSL44, was used to control expression of MotA. We predicted that this new strain, CoMot-S, would express MotA and be motile in the absence of 3OC6HSL and that motility would decrease with increasing 3OC6HSL due to dissociation of 3OC6HSL-bound EsaR from PesaS (Fig. 6a). CoMot-S and CoMot-S+ cells with wild-type EsaR and EsaR-D91G, respectively, were inoculated on plates with uniform 3OC6HSL concentrations ranging from 0–10 µM (Supplementary Fig. S4). In the absence of 3OC6HSL, migration was observed for both strains. Approximately 1 µM of 3OC6HSL was required to observe a migration radius significantly smaller (CoMot p = , CoMot+ p = ) than that observed in the absence of 3OC6HSL after 24 h of incubation at 30 °C. However, no difference in the migration radius of CoMot-S cells was observed in the presence or absence of 3OC6HSL after 36 h of incubation. The migration radius of CoMot-S+ cells, on the other hand, decreased fold in the presence of 10 μM 3OC6HSL. Leaky expression of motA from the activation-based PesaS-controlled system was likely sufficient to restore motility in the CoMot-S strains even in the presence of 3OC6HSL. Despite the incomplete control of motility, we decided to assess whether we would observe directional motility of CoMot-S and CoMot-S+ down a 3OC6HSL gradient. We inoculated CoMot-S strains on plates with gradients established using 3OC6HSL concentrations equivalent to 0, , , , 1, and 10, nM. As seen in Fig. 6b, in the absence of 3OC6HSL, the forward and reverse migration distances are similar for the CoMot-S strains. In the presence of 3OC6HSL, migration to the edge of the plate away from the source (reverse migration distance) was observed under all conditions except for CoMot-S+ at 10 μM 3OC6HSL. A decrease in the forward migration distance with increasing 3OC6HSL concentration was observed with CoMot-S+, while 10 µM 3OC6HSL was required to see a decrease in forward migration distance with CoMot-S cells after 24 h of incubation (Fig. 6c). The directional movement away from the source was observed even after 36 h of incubation despite the previously observed migration of the CoMot-S strains in the uniform 3OC6HSL motility assays. The CoMot-S strains are capable of migrating down a 3OC6HSL gradient, thereby adding to our repertoire of ways to control the directional motility of E. coli.

Characterization of the migration response of CoMot-S and CoMot-S+ cells in a 3OC6HSL gradient shows directional movement of cells away from a 3OC6HSL source: (a) Illustration of 3OC6HSL-dependent motA expression in the CoMot-S strain. Expression of motA is under the control of the PesaS promoter. esaR is constitutively expressed from a σ70-dependent promoter and activates expression from PesaS. In the absence of 3OC6HSL, motA is expressed and motility is restored in the ΔmotA cells. Following addition of 3OC6HSL, EsaR unbinds from PesaS. motA expression decreases and the cells become non-motile. (b) 3OC6HSL gradients were established by adding 0–53 μg of 3OC6HSL on a Whatmann membrane and allowing it to diffuse for 8 h. 10 μM of 3OC6HSL would be the final concentration if 53 µg of 3OC6HSL diffused uniformly through the plate. CoMot-S (ΔmotA transformed with plasmids containing PesaS-motA and Pσ70-esaR) or CoMot-S+ (ΔmotA transformed with plasmids containing PesaS-motA and Pσ70-esaR-D91G) cells were then inoculated at the centre of the plate. Representative plate images obtained after 24 h of incubation at 30 °C. (c) Forward migration distance was measured as the distance between the inoculation point and the visible edge of migration of cells up the signal gradient after 24 h of incubation at 30 °C. Error bars represent one standard deviation from the mean forward migration distance of three biological replicates.

Full size image

Design and characterization of a sender-receiver system

To test if motility in CoMot strains can be regulated in a 3OC6HSL gradient generated by a second population of cells, we engineered non-motile E. coli sender strains that constitutively express a 3OC6HSL synthase (EsaI). Sender strains that produce different amounts of 3OC6HSL were designed by modifying the strength of the ribosome-binding site (RBS) upstream of esaI. We used two luminescent E. coli reporter strains with different dynamic ranges to quantify 3OC6HSL production by each sender strain. We observed that the sender strain with a weak RBS (weak-sender) produced approximately 10 µM 3OC6HSL and strain with the strong RBS (strong-sender) produced approximately  µM (Supplementary Fig. S5).

We used motility assays to assess if the different senders are able to induce motility in the CoMot and CoMot-S strains (receivers). Sender strains were added on a membrane, placed on the plate surface, and incubated at 30 °C for 8 h prior to inoculation of a CoMot variant. As shown in Fig. 7a, the forward migration distance of CoMot cells inoculated onto plates with control cells that do not produce EsaI was comparable to levels of background migration observed in the absence of 3OC6HSL. CoMot cells inoculated onto plates with either sender strain were observed to be motile, and the forward migration distance with the strong-sender cells was significantly higher (p = ) than with the weak-sender cells. As expected, the more-sensitive CoMot+ cells showed a greater forward migration distance in response to both senders than the CoMot cells (Fig. 7b). No significant difference between the forward migration distances of CoMot+ cells was observed in response to the two sender cell strains. However, a higher density of CoMot+ cells was observed in response to the strong senders, indicating that motility was likely turned on in a larger population of CoMot+ cells (Fig. 7a). With the CoMot-S+ strain, migration to the edge of the plate was observed with the control cells. A decrease in forward migration distance was observed in the presence of both senders, where the decrease in forward migration distance was significantly higher in the presence of the strong-sender cells. With the weak senders, CoMot cells displayed a fold (p = ) and CoMot+ cells a fold (p = ) higher forward migration distance than reverse migration distance (distance between the inoculation point and the visible edge of cells that have migrated away from the senders). Similarly, CoMot and CoMot+ cells displayed fold (p = ) and fold (p = ) higher forward than reverse migration distance with the strong senders. On the other hand, CoMot-S+ cells displayed a fold (p = ) lower forward migration distance than reverse migration distance in response to the weak senders and fold lower (p = ) with the strong senders. These observations indicate directional movement of CoMot and CoMot+ cells towards the senders and movement of CoMot-S+ away from the senders. The 3OC6HSL-insensitive strains (ΔmotA transformed with plasmids containing PesaR-motA or PesaS-motA) did not display directional movement (Supplementary Fig. S6). Further, the modularity of the engineered sender-receiver architecture enables tuning of the 3OC6HSL gradient, motility response and the overall direction in which the population of cells migrates.

CoMot, CoMot+ and CoMot-S+ display directional movement in 3OC6HSL gradients generated by sender strains: (a) Control cells with no plasmid for EsaI expression, or sender cells where EsaI expression is controlled by a weak or strong RBS were used. Control, weak-sender and strong-sender cells were added on a Whatmann membrane and the plates were incubated for 8 h at 30 °C. CoMot, CoMot+ or CoMot-S+ were then inoculated at the centre of the plate and incubated at 30 °C for 36 h. Representative plate images are shown. (b) Plot of forward (solid bars) and reverse (open bars) migration distances for each sender/receiver combination. Error bars represent one standard deviation from the mean forward migration distance of three biological replicates.

Full size image

Discussion

We have engineered QS signal-dependent motility in E. coli via transcriptional control of the motor protein, motA. In the absence of a gradient, the migration of cells was positively affected by the 3OC6HSL concentration and directional movement towards the signal source was observed in the presence of a signal gradient. Simulations of the migration response of CoMot cells further indicate that a gradient is essential for directional behaviour. The enhanced migration exhibited by cells in a signal gradient is remarkably similar to the behaviour of enzyme-based systems in their substrate gradients. For example, the increase in diffusivity of urease with increasing urea concentrations enables directed-propulsion of urease-coated beads up a gradient27,28,29. In our system, the motility of CoMot cells increases with increasing 3OC6HSL concentrations leading to enhanced diffusivity of cells that migrate to locations with signal concentrations sufficient to induce motA expression. This enhanced diffusivity enables population-level movement of cells up a signal gradient.

We also engineered CoMot-S cells that move down a 3OC6HSL gradient by changing the promoter controlling motA expression from the PesaR to PesaS. Such a system, where cells can be manipulated to move up or down a signal gradient by switching one regulatory element, would be difficult to engineer with chemotaxis-based control, where a signal typically behaves as either an attractant or repellant45, 46. Although chemotaxis was not manipulated in CoMot cells, it is possible that chemotaxis enhances their directional behaviour in our experimental setup. At the beginning of the motility assays, any cell leaving the point of inoculation will move to a region of the plate that is nutrient rich. However, as the population distributes itself up and down the 3OC6HSL gradient, the CoMot cells that move up the 3OC6HSL gradient will continue to enter nutrient-rich regions, while those migrating down the gradient may enter regions potentially depleted of nutrients by the accumulated static cells. Therefore, chemotaxis may enhance migration towards nutrient-rich regions that are available up the gradient and increase the forward migration distance.

While chemotaxis-enabled directional movement of cells occurs at the individual-cell level15, the directional movement of CoMot cells occurs at the population-level. Further studies are required to explore if CoMot cells will exhibit observable levels of enhanced-diffusivity up a 3OC6HSL gradient at the microscopic-level and whether this type of control can be used for applications in microfluidic devices. The time required for non-uniform distribution of CoMot cells in signal gradient, which requires transcription, translation and cell movement, is also expected to be much longer than a chemotaxis-based system, which requires only protein phosphorylation47, 48. These two time-scales may be advantageous for different applications, and could potentially be combined for precise cellular movement in response to signal molecules. Previously, a population of Serratia cells was attached to a 10 μm-sized piece of polymer to generate a micro-bio-robot. The motility of the cells was used to propel the robot. However, the direction of movement was controlled using external magnetic fields49. An understanding of the behaviour of CoMot cells under microfluidic conditions could enable their use as actuators of both motion and direction for micro-bio-robots or targeted-delivery systems.

Pairing our QS-responsive receiver strains, CoMot, CoMot+ and CoMot-S+, with 3OC6HSL-producing sender strains demonstrated that our motility-control system is both modular and tuneable. As described above, the use of EsaR variants and esa promoter variants enabled the engineering of receiver strains that display a range of behaviours in terms of signal sensitivity, whether motility is turned on or off in the presence of 3OC6HSL, and the directionality of movement in a signal gradient. The amount of 3OC6HSL produced by the sender cells was tuned by varying the strength of the RBS upstream of esaI. The ability to manipulate motility in CoMot cells using sender cells could enable the use of our sender-receiver system to detect other signals of interest. Detection of target signals using the sender-receiver system can be achieved by swapping the constitutive promoter controlling esaI expression to one regulated in response to any target signal of interest. Advantages of this architecture for biosensing-system design include modularity and signal amplification via QS50. Further, logic gate-type behaviours in the receivers may enable more complex sensing systems51, 52. For example, multiple senders engineered to detect different target molecules and produce 3OC6HSL could be combined to generate a modular, tuneable OR-type sensor system that is turned on by any one of the target molecules. Multiple intermediary QS signals could be used to generate a broader range of signal integration circuits. These systems can be applied for biosensing and bioactuation in complex environments, such as a tumour, soil or the human gastrointestinal tract, where recognition, integration and reporting of multiple signal inputs is advantageous.

We have demonstrated that transcriptional regulation of a motility gene allows for control of cell motility and enables directional movement in gradients generated by exogenously added signal or in-situ bio-production. The strategy used here presents a robust mechanism for controlling the directional movement of a population of cells without the manipulation of chemotaxis. We anticipate that these engineered cells will find application as actuators for micro-bio-robots and as drivers of motility-dependent pattern formation. The modular sender-receiver architecture, combined with the population-level control of directional movement, sets the stage for development of biosensing frameworks where senders are engineered to detect target stimuli and produce a QS molecule, and cellular motility in the receivers serves as novel biosensing output.

Methods

Plasmid Construction and strains

The motA-deletion strain (∆motA), E. coli RP39, was used in this study. Plasmids and primers used in this study are listed in Supplementary Tables S2 and S3. Sequences of the promoters and ribosome binding sites (RBS) used in this study are provided in Supplementary Table S4 and S5. To engineer the receiver strains, we constructed a two-plasmid system consisting of a motA-expression plasmid and an esaR-expression plasmid. pCS-PesaR-motA-gfp and pCS-PesaS-motA-gfp were used for expression of motA. To construct pCS-PesaR-motA-gfp, the PesaR promoter, motA and gfp were cloned between the XhoI and NotI sites of a low-copy, pCS26 plasmid. We tuned the strength of the RBS upstream of motA and gfp using the RBS calculator53, 54 and the 5′ primer was used to add the modified RBSs to the genes. motA was PCR-amplified from pDFB3641 using the primers 5′-SMotA-KpnI and 3′-MotA-BamHI. PesaR was PCR-amplified from pCS-PesaR-gfp using the primers 5′-PesaR-XhoI and 3′-PesaR-KpnI-SMotA. The amplified PesaR and motA were assembled using assembly PCR with the primers 5′-PesaR-XhoI and 3′-MotA-BamHI. gfp was PCR-amplified from pCS- Pσ70-gfp using the primers 5′-NotI-SGFP and 3′-BglII. To construct pCS-PesaS-motA-gfp, the PesaS promoter was PCR-amplified from pCS-PesaS-lux55 using the primers ZEO5 and 3′-KpnI-PesaS. The amplified product was digested with XhoI and KpnI and ligated into XhoI and KpnI-digested pCS-PesaR-motA-gfp. pAC- Pσ70-esaR and pAC- Pσ70-esaR-D91G43 were used as esaR-expression plasmids. In these plasmids, a Pσ70-dependent promoter and either esaR or esaR-D91G genes were cloned between the XbaI and BamHI sites of the medium-copy, pACYC plasmid. To construct the CoMot and CoMot+ strains, ∆motA competent cells were transformed with pCS-PesaR-motA-gfp and pAC- Pσ70-esaR or pAC- Pσ70-esaR-D91G respectively. Similarly, to construct the CoMot-S and CoMot-S+ strains, pCS-PesaS-motA-gfp and pAC- Pσ70-esaR or pAC- Pσ70-esaR-D91G were transformed into ∆motA cells.

For construction of the sender strains, two esaI expression plasmids, pAC-Plac-(RBSweak)esaI or pAC-Plac-(RBSstrong)esaI, were constructed. To construct pAC-Plac-(RBSstrong)esaI, esaI with the strong-RBS was amplified from pAC-Plac-esaR-esaI55 using the primers 5′-KpnI-esaI and 3′-BamHI-esaI. The amplified product was digested with KpnI and BamHI and ligated into KpnI and BamHI-digested pAC-Plac-esaR43. To construct pAC-Plac-(RBSweak)esaI, the Plac promoter was amplified from pAC-Plac-esaR-esaI55 using the primers 5′-pAC-promseq and 3′-Plac-BamHI. The amplified product was digested with XbaI and BamHI and ligated into XbaI and BamHI-digested pAC-Pσ70-esaR-esaI56. For construction of the sender strains, ∆motA competent cells were transformed with pCS26 and an esaI expression plasmids. For construction of control strains, ∆motA competent cells were transformed with pCS26 and pACYC E. coli DH5α strain was used in all cloning procedures.

Overnight cultures

Overnight cultures were made by inoculating single colonies of strains picked from Luria Broth (LB) agar plates in 5 mL of LB media with chloramphenicol (50 μg/mL) and kanamycin (50 μg/mL). The cultures were incubated overnight at 37 °C with shaking ( rpm).

Motility assays

Semi-solid media consisted of 1% tryptone, % NaCl and % agar with chloramphenicol (25 μg/mL) and kanamycin (25 μg/mL). For assays requiring a uniform 3OC6HSL concentration across the plate, 3OC6HSL was directly added to the media before pouring into petri dishes. To establish a 3OC6HSL gradient, to  µmoles of 3OC6HSL was added to a Whatmann membrane (Grade 3–6 µm, diameter:  cm). 10 μM of 3OC6HSL would be the final concentration if  µmoles of 3OC6HSL diffused uniformly through the plate. The centre of the membrane was placed  cm from the edge of the plate and 3OC6HSL was allowed to diffuse from the membrane into the media at room temperature for 8 h prior to inoculation unless otherwise indicated. In the sender-receiver motility assays, overnight cultures of the sender strains were concentrated fold by centrifuging the cultures and re-suspending them in LB. 10 μL of the concentrated cultures were added to a Whatmann membrane, which was placed  cm from the edge of the plate and incubated at 30 °C for 8 h prior to inoculation with the receiver cells. For all motility assays, receiver cells were inoculated using a pipette tip containing 1 μL of overnight culture. The tip was inserted at the centre of the plate, approximately 3 mm below the surface of the media, and the cells were ejected as the tip was pulled up through the media. To assess migration, images were obtained up to 48 h after incubation at 30 °C.

Quantitative characterization of 3OC6HSL produced by sender strains

Overnight cultures of sender strains were diluted fold in 5 mL LB with appropriate antibiotics and grown at 37 °C with shaking ( rpm) for 8 h. 1 mL of the culture was centrifuged at 16, rcf for 3 minutes. The supernatant was filter sterilized using a  μM polyether sulfone filer. Two E. coli reporter strains (DH5α cells transformed with (i) pCS-PesaR-lux and pAC-Pσ70-esaR-I70V/D91G (ii) pCS-PesaR-lux and pAC- Pσ70-esaR43) that luminesces in a 3OC6HSL concentration-dependent manner were used for quantification of 3OC6HSL produced by the sender cells, by comparing luminescence in response to 3OC6HSL in the supernatants to the response to known amounts of 3OC6HSL. The quantitative characterization of 3OC6HSL using the reporter strain was performed as described by Shong et al.43.

Statistical analysis

Two-tailed paired t-tests were applied to evaluate significance when required. p values are reported in the text for each statistical test.

Modelling of signal molecule-guided bacterial motility

All simulations were run in COMSOL Multiphysics version A 2-dimensional version of the experimental set up was simulated in the model. Circular, 8 cm-diameter plates were used in all simulations except in pattern-formation simulations. In pattern-formation simulations, either circular 15 cm-diameter plates or 13 × 13 cm square plates were used. Neumann boundary conditions are imposed on Equations (1) and (3) to ensure that motile cells and the signal molecule do not diffuse beyond the boundaries. All simulations were started with *107 static cells/m2 (equivalent of cells) as the inoculum and  μmoles/m2 of the signal as the signal source and run for a simulation time of 24 h unless otherwise noted. The location of the cells (inoculation point) and signal source were similar to the experimental set up. When reported, forward and reverse migration distances were measured as distances from the inoculation point towards and away from the signal source where a total cell concentration (m + s) ≥ 108 cells/m2 was observed. In all simulated plate images log (total cell concentration) is shown. To simulate the migration response of CoMot and CoMot+ cells K2 values of and 1 nmoles/cm2 were used respectively. In the studies where simulations were run varying the value of each parameter, the gradient was established using 85 μmoles/m2 of the signal near edge of the plate (migration distance = 4 cm).

References

  1. 1.

    Sourjik, V. & Wingreen, N. S. Responding to chemical gradients: bacterial chemotaxis. Curr. Opin. Cell Biol.24, – ().

    CASArticlePubMed Google Scholar

  2. 2.

    Guttenplan, S. B. & Kearns, D. B. Regulation of flagellar motility during biofilm formation. FEMS Microbiol. Rev.37, – ().

    CASArticlePubMed CentralPubMed Google Scholar

  3. 3.

    Kerr, B., Riley, M. A., Feldman, M. W. & Bohannan, B. J. M. Local dispersal promotes biodiversity in a real-life game of rock-paper-scissors. Nature, – ().

    ADSCASArticlePubMed Google Scholar

  4. 4.

    Salis, H., Tamsir, A. & Voigt, C. In Bacterial Sensing and Signaling16, – (KARGER, ).

  5. 5.

    Tecon, R. & van der Meer, J. R. Bacterial biosensors for measuring availability of environmental pollutants. Sensors8, – ().

    CASArticlePubMed CentralPubMed Google Scholar

  6. 6.

    Voigt, C. A. Genetic parts to program bacteria. Curr. Opin. Biotechnol.17, – ().

    CASArticlePubMed Google Scholar

  7. 7.

    Sinha, J., Reyes, S. J. & Gallivan, J. P. Reprogramming bacteria to seek and destroy an herbicide. Nat. Chem. Biol.6, – ().

    CASArticlePubMed CentralPubMed Google Scholar

  8. 8.

    Tien, S.-M. et al. Engineering bacteria to search for specific concentrations of molecules by a systematic synthetic biology design method. PLoS One11, e, doi/journal.pone ().

    ArticlePubMed CentralPubMed Google Scholar

  9. 9.

    Weibel, D. B. et al. Microoxen: microorganisms to move microscale loads. Proc. Natl. Acad. Sci. USA, – ().

    ADSCASArticlePubMed CentralPubMed Google Scholar

  10. Steager, E. B., Julius, A. A., Kim, M., Kumar, V. & Pappas, G. J. Modeling, control and experimental characterization of microbiorobots. Int. J. Rob. Res.30, – ().

    Article Google Scholar

  11. Berg, H. C. The rotary motor of bacterial flagella. Annu. Rev. Biochem.72, 19–54 ().

    CASArticlePubMed Google Scholar

  12. Li, H. & Sourjik, V. Assembly and stability of flagellar motor in Escherichia coli. Mol. Microbiol.80, – ().

    CASArticlePubMed Google Scholar

  13. Fahrner, K. A., Block, S. M., Krishnaswamy, S., Parkinson, J. S. & Berg, H. C. A mutant hook-associated protein (HAP3) facilitates torsionally induced transformations of the flagellar filament of Escherichia coli. J. Mol. Biol., – ().

    CASArticlePubMed Google Scholar

  14. Kojima, S. & Blair, D. F. Conformational Change in the Stator of the Bacterial Flagellar Motor. Biochemistry40, – ().

    CASArticlePubMed Google Scholar

  15. Sourjik, V. & Berg, H. C. Functional interactions between receptors in bacterial chemotaxis. Nature, – ().

    ADSCASArticlePubMed Google Scholar

  16. Parkinson, J. S. cheA, cheB, and cheC genes of Escherichia coli and their role in chemotaxis. J. Bacteriol., – ().

    CASPubMed CentralPubMed Google Scholar

  17. Baker, M. D., Wolanin, P. M. & Stock, J. B. Signal transduction in bacterial chemotaxis. Bioessays28, 9–22 ().

    CASArticlePubMed Google Scholar

  18. Kuo, S. C. & Koshland, D. E. Roles of cheY and cheZ gene products in controlling flagellar rotation in bacterial chemotaxis of Escherichia coli. J. Bacteriol., – ().

    CASArticlePubMed CentralPubMed Google Scholar

  19. Mishler, D. M., Topp, S., Reynoso, C. M. K. & Gallivan, J. P. Engineering bacteria to recognize and follow small molecules. Curr. Opin. Biotechnol.21, – ().

    CASArticlePubMed CentralPubMed Google Scholar

  20. Liu, C. et al. Sequential establishment of stripe patterns in an expanding cell population. Science, – ().

    ADSCASArticlePubMed Google Scholar

  21. Weiss, L. E. et al. Engineering motility as a phenotypic response to LuxI/R-dependent quorum sensing in Escherichia coli. Biotechnol. Bioeng., – ().

    CASArticlePubMed Google Scholar

  22. Topp, S. & Gallivan, J. P. Guiding bacteria with small molecules and RNA. J. Am. Chem. Soc., – ().

    CASArticlePubMed CentralPubMed Google Scholar

  23. Derr, P., Boder, E. & Goulian, M. Changing the specificity of a bacterial chemoreceptor. J. Mol. Biol., – ().

    CASArticlePubMed Google Scholar

  24. Bi, S. et al. Discovery of novel chemoeffectors and rational design of Escherichia coli chemoreceptor specificity. Proc. Natl. Acad. Sci. USA, – ().

    ADSCASArticlePubMed CentralPubMed Google Scholar

  25. Bi, S., Pollard, A. M., Yang, Y., Jin, F. & Sourjik, V. Engineering hybrid chemotaxis receptors in bacteria. ACS Synth. Biol.5, – ().

    CASArticlePubMed Google Scholar

  26. Ma, X., Hortelão, A. C., Patiño, T. & Sánchez, S. Enzyme catalysis to power micro/nanomachines. ACS Nano10, – ().

    CASArticlePubMed CentralPubMed Google Scholar

  27. Muddana, H. S., Sengupta, S., Mallouk, T. E., Sen, A. & Butler, P. J. Substrate catalysis enhances single-enzyme diffusion. J. Am. Chem. Soc., – ().

    CASArticlePubMed CentralPubMed Google Scholar

  28. Sengupta, S. et al. Enzyme molecules as nanomotors. J. Am. Chem. Soc., – ().

    CASArticlePubMed Google Scholar

  29. Dey, K. K. et al. Micromotors powered by enzyme catalysis. Nano Lett.15, – ().

    ADSCASArticlePubMed Google Scholar

  30. Ng, W.-L. & Bassler, B. L. Bacterial quorum-sensing network architectures. Annu. Rev. Genet.43, – ().

    CASArticlePubMed CentralPubMed Google Scholar

  31. Papenfort, K. & Bassler, B. L. Quorum sensing signal–response systems in Gram-negative bacteria. Nat. Rev. Microbiol.14, – ().

    CASArticlePubMed CentralPubMed Google Scholar

  32. Choudhary, S. & Schmidt-Dannert, C. Applications of quorum sensing in biotechnology. Appl. Microbiol. Biotechnol.86, – ().

    CASArticlePubMed Google Scholar

  33. Garg, N., Manchanda, G. & Kumar, A. Bacterial quorum sensing: circuits and applications. Antonie Van Leeuwenhoek, – ().

    ArticlePubMed Google Scholar

  34. Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature, –4 ().

    ADSCASArticlePubMed Google Scholar

  35. Anderson, J. C., Clarke, E. J., Arkin, A. P. & Voigt, C. A. Environmentally controlled invasion of cancer cells by engineered bacteria. J. Mol. Biol., –27 ().

    CASArticlePubMed Google Scholar

  36. Moon, T. S., Lou, C., Tamsir, A., Stanton, B. C. & Voigt, C. A. Genetic programs constructed from layered logic gates in single cells. Nature, – ().

    ADSCASArticlePubMed CentralPubMed Google Scholar

  37. Hennig, S. et al. Artificial cell-cell communication as an emerging tool in synthetic biology applications. J. Biol. Eng.9, 13 ().

    ArticlePubMed CentralPubMed Google Scholar

  38. Wang, Z. et al. Artificially constructed quorum-sensing circuits are used for subtle control of bacterial population density. PLoS One9, e, doi/journal.pone ().

    ADSArticlePubMed CentralPubMed Google Scholar

  39. Garza, A. G., Bronstein, P. A., Valdez, P. A., Harris-Haller, L. W. & Manson, M. D. Extragenic suppression of motA missense mutations of Escherichia coli. J. Bacteriol., – ().

    CASArticlePubMed CentralPubMed Google Scholar

  40. Garza, A. G., Harris-Haller, L. W., Stoebner, R. A. & Manson, M. D. Motility protein interactions in the bacterial flagellar motor. Proc. Natl. Acad. Sci. USA92, – ().

    ADSCASArticlePubMed CentralPubMed Google Scholar

  41. Blair, D. F. & Berg, H. C. Restoration of torque in defective flagellar motors. Science, – ().

    ADSCASArticle

Sours: https://www.nature.com/articles/s
Bacteria under the Microscope (E. coli and S. aureus)

Don't stop. Miriam laughed quietly, triumphantly, sliding her beak a little deeper and squeezing Lara's body with her only free hand until it. Crunched in the ribs. Their bodies were brought together by this force, like two sheets of paper under pressure.

Similar news:

As if with the flow. YES. - I have good natural physical characteristics, not thinking at all that I, as an ugly duckling, have any physical disabilities, from which. I would suffer and would like to change for the better.



1325 1326 1327 1328 1329