Listeria monocytogenes intracellular or extracellular

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Listeria monocytogenes hijacks host actin to promote its intracellular motility and intercellular spread. While L. monocytogenes virulence hinges on cell-to-cell spread, little is known about the dynamics of bacterial spread in epithelia at a population level. Here, we use live microscopy and statistical modeling to demonstrate that L. monocytogenes cell-to-cell spread proceeds anisotropically in an epithelial monolayer in culture. We show that boundaries of infection foci are irregular and dominated by rare pioneer bacteria that spread farther than the rest. We extend our quantitative model for bacterial spread to show that heterogeneous spreading behavior can improve the chances of creating a persistent L. monocytogenes infection in an actively extruding epithelium. Thus, our results indicate that L. monocytogenes cell-to-cell spread is heterogeneous, and that rare pioneer bacteria determine the frontier of infection foci and may promote bacterial infection persistence in dynamic epithelia.

Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed [see decision letter].

Eating food that has been contaminated with bacteria called Listeria monocytogenes can result in life-threatening infections. The bacteria first invade the epithelial cells that line the small intestine. After this, L. monocytogenes can move from one host cell to another, which allows the infection to reach other organs.

Most studies into how L. monocytogenes infections spread have focused either on how single bacterial cells move from one host cell to the next, or on how millions of bacteria damage host tissues. Little was known about the intermediate steps of an infection, where the bacteria start to colonize the small intestine.

To investigate, Ortega et al. recorded videos of L. monocytogenes spreading between epithelial cells grown on a glass coverslip, and developed computer simulations to try to reproduce how the bacteria spread. This revealed that the bacteria do not all move in the same way. Instead, less than 1% of the bacteria move in ‘steps’ that are up to 10 times longer than those taken by the others. Ortega et al. named these bacteria ‘pioneers’.

Ortega et al. propose that the pioneers form long protrusions that allow them to spread directly from an infected cell to a non-neighboring cell. By taking these large steps, the pioneers may increase the chances that the bacteria will cause a long-lasting infection.

Future research will be needed to answer further questions about the pioneers. For example, how do the pioneer bacteria differ from the majority of bacterial cells? Would targeting anti-bacterial treatments at pioneers make it easier to treat infections? It also remains to be seen if other types of bacteria also show this pioneer behavior.

The widely studied foodborne pathogen Listeria monocytogenes has served as a model system to study cytoskeletal dynamics [Theriot et al., 1992; Welch, 1998], epithelial cell biology [Pentecost et al., 2010], and host-pathogen interactions [Kocks et al., 1995; Mengaud et al., 1996]. This ubiquitous Gram-positive bacterium can invade and replicate within non-phagocytic cells and, importantly, use a form of actin-based motility to spread directly from the cytoplasm of an infected host cell into the cytoplasm of another host cell without exposure to the extracellular milieu [Tilney and Portnoy, 1989]. This process, known as cell-to-cell spread, enables L. monocytogenes to breach and colonize the intestinal epithelium and to subsequently reach distant organs including the liver and brain in immunocompromised patients [Ghosh et al., 2018] and the placenta in pregnant women [Faralla et al., 2016]. Indeed, compared to wild-type L. monocytogenes, mutant strains incapable of undergoing cell-to-cell spread are three orders of magnitude less virulent in murine models [Domann et al., 1992].

L. monocytogenes infections begin in the intestinal epithelium, a tissue made up of polarized epithelial cells connected to each other by cell-cell junctions [Hartsock and Nelson, 2008]. L. monocytogenes preferentially adheres to and invades an epithelium at the tips of intestinal villi [Pentecost et al., 2006], where epithelial cells are actively extruded and shed [Sancho et al., 2004]. Upon bacterial invasion, L. monocytogenes spreads to neighboring host cells, which can allow bacteria to move away from the tip of a villus before the next host cell extrusion event terminates the infection. Therefore, understanding L. monocytogenes virulence requires a quantitative grasp of the spatiotemporal dynamics of cell-to-cell spread.

To initiate cell-to-cell spread, L. monocytogenes uses the protein ActA to polymerize actin at its surface and create an actin comet tail [Pistor et al., 1994]. Actin polymerization generates a propulsive force that allows the bacterium to move within the host cytoplasm. Upon contact with the donor host cell membrane, the intracellular bacterium creates a protrusion that can extend into the cytoplasm of a recipient host cell [Robbins et al., 1999]. Although cell-to-cell spread has been primarily studied as a mechanism of bacterial dissemination between adjacent host cells, it is well established that L. monocytogenes can create protrusions more than ten microns long [Pust et al., 2005], which could, in principle, mediate bacterial spread between two non-adjacent host cells. To complete cell-to-cell spread, the recipient cell engulfs the bacterium-containing protrusion, thus giving L. monocytogenes access to the recipient host cell’s cytoplasm. After escaping the double-membrane vacuole, L. monocytogenes rebuilds the actin comet tail and restarts intracellular motility [Gedde et al., 2000].

Particular attention has been paid to bacterial and host cell proteins that mediate cell-to-cell spread. The bacterial protein internalin C helps L. monocytogenes to relax cortical tension and increase the rate of bacterial-mediated protrusion formation to promote spread [Rajabian et al., 2009]. From the perspective of the host cell, it has been shown that TIM4 allows the host to sense bacterial-mediated membrane damage, which then triggers a repair mechanism that L. monocytogenes exploits to promote spread [Czuczman et al., 2014]. The diaphanous-related formins [Fattouh et al., 2015] and members of the ERM protein family [Pust et al., 2005] have been shown to localize to bacteria-containing protrusions and inhibition of their activity decreases the efficiency of cell-to-cell spread. This cell biological approach has been useful in creating a mechanistic understanding of how individual spreading events occur. However, our larger scale understanding of how a population of bacteria spreads through tissue remains poorly developed.

Here, we combine live microscopy and statistical modeling to study the dynamics of a population of L. monocytogenes as it spreads through a polarized epithelial monolayer. We simulate cell-to-cell spread as an isotropic random walk because the movement of L. monocytogenes is directionally persistent over short distances but shows no preferred orientation over long distances. Our experimental and computational results indicate that L. monocytogenes cell-to-cell spread includes a majority of local-spreading bacteria but is dominated by rare pioneers, which determine the shape of infection foci. Importantly, we find that pioneers alter the kinetics of spread in a way that might promote bacterial persistence in a dynamic epithelium where cells are actively extruded, as at the tip of an intestinal villus.

Listeria monocytogenes cell-to-cell spread has been primarily studied in two ways. First, plaque assays have been used to study late stages of infection where a few millions of bacteria have created plaques—sites of host cell death in cultured epithelial monolayers. The size of the plaque correlates to the efficiency of spread [Van Langendonck et al., 1998]. Second, individual bacteria have been carefully observed by light and electron microscopy to provide information about the kinetics of protrusion formation and uptake [Robbins et al., 1999]. In both cases, the identification of host and bacterial proteins has helped elucidate possible molecular mechanisms that facilitate L. monocytogenes spread [Rajabian et al., 2009; Chong et al., 2011; Czuczman et al., 2014]. We were interested in bridging the gap between millions of bacteria creating millimeter-sized plaques and single bacteria creating micron-sized protrusions by studying cell-to-cell spread at a population level, while tracking individual bacteria at the frontier of the infection focus, with the goal of learning about both the collective and single-cell intercellular spreading behavior of L. monocytogenes.

We initially predicted that the spatial distribution of bacteria as a function of time would follow that of a random walk, a model characterized by isotropic, uncorrelated directions and normally distributed displacements [Berg, 1993]. We developed this null hypothesis because [1] there is no evidence in the literature to suggest that intracellular L. monocytogenes motility has directionality, [2] late stages of L. monocytogenes cell-to-cell spread create circular plaques [Van Langendonck et al., 1998], and [3] MDCK cells form compact and relatively homogeneous monolayers in culture [Mays et al., 1995]. Our high-resolution video microscopy assay, however, showed that a small number of bacteria spread farther than the rest and caused the infection focus boundary to become irregular. We propose that these pioneer bacteria spread by creating extracellular protrusions [Pust et al., 2005], that can reach and be taken up by recipient host cells that are not in direct contact with the donor cell.

Through simulations, we have found that allowing each bacterium to choose between two behaviors, far-reaching pioneer and local non-pioneer, approximated the shape of the experimental data better than simulating a single behavior of spreading bacteria [Figure 3]. Simulated foci became less circular when the ratio Dfast/Dslow was high and the total number of pioneer bacteria in the infection focus was very small [yet non-zero]. While high numbers of pioneers are expected to increase the overall spreading rate of an infection, the anisotropic non-circular shapes of observed foci imply that the infection boundary is determined by rare events. Because pioneer bacteria themselves are assumed to not have a preferred spreading direction, small absolute numbers of pioneers are required in order to generate non-circular foci shapes as a result of stochastic fluctuations.

While we cannot rule out the possibility that infection focus anisotropy can also be attributed in part to host cell heterogeneity, the L. monocytogenes GRR mutant data suggest that properties of the bacteria themselves contribute significantly to this phenomenon. L. monocytogenes GRR mutants produce infection foci that are more circular than those produced by wild-type bacteria. Given that changing directional persistence in our simulations had little to no effect on circularity, but that changing Dfast/Dslow did, it is likely that the GRR mutant L. monocytogenes creates shorter extracellular protrusions, that is GRR pioneers take shorter steps than wild-type pioneers. However, with our current experimental setup, we cannot determine whether these long protrusions seen in cultured cells [Pust et al., 2005] occur in the apical side of the monolayer, the basal side, or between cell-cell junctions in MDCK monolayers. Whereas at least a few of the pioneer events that we directly observed in MDCK cells appear to involve long apical protrusions, in the intestinal epithelium, which is characterized by a dense and highly organized apical brush border [Crawley et al., 2014], it is probably more likely that pioneers would spread either laterally between cells in the epithelium or basally at the junction between the enterocytes and the subjacent basement membrane. Indeed, L. monocytogenes’ ability to cross the basal membrane of an epithelium via an actin-dependent process has been well-characterized [Faralla et al., 2018]. The key feature of these protrusions, however, is that they enable an L. monocytogenes bacterium to bypass several host cells on its way to the more distant recipient cell. This model explains L. monocytogenes’ ability to seemingly spread across two host cells in less than 30 min, even though the formation, uptake, and resolution of a single intercellular protrusion can take up to 45 min in MDCK cells [Robbins et al., 1999].

An extension of our model then argues that intracellular pathogens that spread from cell to cell without making extracellular protrusions would be expected to spread via a process resembling a random walk. The Gram-negative bacillus Burkholderia thailandensis is an example of a bacterial pathogen that spreads intercellularly primarily by inducing the cytoplasmic fusion of two neighboring host cells. Indeed, consistent with our pioneer model, infection foci created by B. thailandensis in mammalian host cells are significantly more isotropic than those created by wild-type L. monocytogenes [French et al., 2011].

This type of dual spreading behavior is not uncommon in other organisms. For example, the rice-water weevil, Lissorhoptrus orzyzophilus, migrates by both crawling and flying [Shigesada et al., 1995]. If relatively few beetles migrate by flying, then early migration would be dominated by short steps and later migration would be dominated by longer steps. This ecological model parallels our heterogeneous L. monocytogenes cell-to-cell spread model given that: [1] the mean squared displacement accelerates with time in both rice-water weevil migration pattern data and L. monocytogenes cell-to-cell spread live microscopy assays [Shigesada et al., 1995], [2] the migration boundary of this organism deviates from a circle similar to bacterial infection foci [Andow et al., 1990], and [3] the rice-water weevil bimodal migration mechanism resembles that of bacterial local spread versus pioneer spread in long protrusions. In another ecological example, the population of European starlings, Sturnus vulgaris, is made up of short-distance and long-distance migrants. The latter of the two groups was able to establish colonies that helped to promote survival of the species [Shigesada et al., 1995]. Indeed, we argue that heterogeneous spread increases the chance of L. monocytogenes survival in an actively extruding tissue [Figure 5 and discussed below].

Dual spreading behavior can also occur in a variety of other microbial pathogens. Vaccinia virus undergoes intercellular cell-to-cell spread via an actin-mediated process resembling that of L. monocytogenes. In addition, vaccinia virus can accelerate its own rate of spread by inducing the expression of viral proteins on the surface of an infected host cell. Upon encountering those proteins, new incoming viral particles are repelled from the already-infected host cells by actin projections and encouraged to infect virus-free host cells. The repulsion of superinfecting virions thus creates ‘viral superspreaders,’ whose spreading behavior resembles that of L. monocytogenes pioneers. Both viral superspreaders and bacterial pioneers skip host cells on their way to a recipient uninfected host cell, create anisotropic infection foci, and accelerate the pathogen’s rate of spread [Doceul et al., 2010].

Even though mixing two random walks recapitulated the decrease in circularity seen in experimental data, we cannot rule out alternative spread models. For example, a well-characterized mathematical model is the Lévy flight, a random walk model where the step sizes are drawn from a heavy-tailed distribution instead of a normal distribution, thus ensuring a non-trivial fraction of arbitrary long steps [Dubkov et al., 2008]. Lévy flights are used to model animals foraging for food: animals will take short steps as they are feeding and long steps as they are searching for the next feeding ground [Viswanathan et al., 2008]. For L. monocytogenes cell-to-cell spread, a Lévy flight would indicate that at any given point, all bacteria have the ability to spread as either a pioneer or as a non-pioneer. With a Lévy flight, however, it is more difficult to mechanistically explain what allows a bacterium to become a pioneer. On the other hand, in our heterogeneous spread model, bacteria interconvert between two spread behaviors and retain that behavior until their next replication event. We designed the simulation this way to resemble a bacterium creating either a short protrusion or a straight, long protrusion, and replicating once they have broken out of the double-membrane vacuole in the cytoplasm of the recipient cell. The increase in directional persistence in pioneers is equivalent to a larger diffusion coefficient at long times.

Given that it is a foodborne pathogen, L. monocytogenes infections begin in the host’s alimentary canal. In this work, we propose that L. monocytogenes may have evolved the ability to spread via host cell skipping to maximize its chances of surviving, replicating, and spreading in a host’s actively extruding intestinal epithelium. Under the conditions set by our single random walk simulations, L. monocytogenes’ ability to establish a stable steady state was attained by only a narrow set of effective diffusion coefficients [Figure 5C]. Also, in this model, an individual effective diffusion coefficient usually led to an all-or-nothing outcome. If the step sizes were too small, then the infection was cleared 100% of the time. If the step sizes were too large, then the infection got out of control and caused uncontrolled growth 100% of the time. If the step sizes fell in a narrow range in between, the bacterium was able to successfully extrude many bacteria while sustaining the infection 100% of the time. This stable steady state is a desirable outcome for both bacteria and host: L. monocytogenes can promote the extrusion of its offspring, which can either [1] exit the animal via feces and infect other host animals, or [2] escape the extruded host cell and try to re-invade a different villus. This second point is important because our simulation considers a single villus only, even though the mammalian small intestine contains millions of individual villi [Guyton and Hall, 2006], each of which is a potential site of infection.

Given our findings, it was important to include pioneers in the host extrusion simulations. Simulating an effective diffusion coefficient that previously led to bacterial clearance and mixing it with larger effective diffusion coefficients allowed L. monocytogenes to attain a stable steady state [Figure 5D]. Even though heterogeneous spread did not lead to 100% stable steady state, 10–15% of stable steady state infections become significant in the context of the millions of villi that make up the intestinal epithelium. Under these conditions, bacterial clearance was the most likely outcome, and uncontrolled growth remained low, unlike in the case of the random walk [Figure 5C]. It is critical for bacteria to avoid uncontrolled growth in any single villus site as this could result in death of the host animal, which harms both the host and the pathogen since the pathogen can no longer replicate and spread to other hosts [Falkow, 2006]. Importantly, high but not 100% of bacterial clearance allows L. monocytogenes to extrude more offspring while being able to achieve a stable steady state in a smaller fraction of villi. Finally, given that step size is a function of nutrient availability, temperature, and monolayer age, among other factors, our model predicts that heterogeneous spread widens the range of biological conditions that L. monocytogenes can explore to create a stable steady state. This is an import host-pathogen relationship because it does not harm the host and promotes pathogenic success. Beyond the gut, it is also possible that pioneers may be more successful at reaching distant organs within the host animal. In fact, it has been shown that a very small number of founder L. monocytogenes bacteria can spread from the gut to organs such as the spleen and gall bladder, a process that leads to bottlenecking [Zhang et al., 2017].

In our current model of an actively extruding epithelium, host cell extrusion occurs at regular intervals and is not influenced by bacterial load. An alternate mechanism that would also be expected to lead to a stable steady state would be forcing extrusion to occur after a preset number of bacteria is reached. Uncontrolled growth is inhibited since bacteria are extruded as soon as the number gets too high, and bacterial clearance does not occur since extrusion stops if bacterial counts get low. However, this alternative mechanism would require that the host cell be able to sense the number of intracellular bacteria and specifically alter its behavior accordingly. Our model, in contrast, presents a simple physical mechanism by which steady state can be achieved without additional sensing capabilities on the part of the host cell.

In addition to L. monocytogenes, other pathogens have evolved strategies to create persistent infections in their hosts. For example, the lambda phage induces expression of the λ repressor to change its gene expression profile from an active host-killing lytic state to a dormant lysogenic state. During the lysogenic state, the lambda phage integrates its genome into the bacterial chromosome, which is then inconspicuously replicated by the host’s DNA replication machinery [Ptashne, 2006]. The lambda phage stays dormant until environmental conditions, such as host bacteria availability, indicate that it is safe to kill the donor and spread to recipient hosts. Just like pioneer L. monocytogenes behavior, spontaneous induction from a lysogenic to lytic state is rare, a characteristic that promotes phage replication [Little et al., 1999]. A continuous lytic state, similar to L. monocytogenes taking large steps in an extruding monolayer, would cause indiscriminate host death, thus harming both host and pathogen. Indeed, strategies that help establish persistent infections are critical in creating stable host pathogen interactions that have evolved over millions of years.

All data generated or analyzed during this study are included in the manuscript. MatLab code for simulations is available at //github.com/Fabianjr90/Listeria_spread_simulations [copy archived at //github.com/elifesciences-publications/Listeria_spread_simulations].

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[Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed.]

Thank you for submitting your article "Listeria monocytogenes cell-to-cell spread in epithelia is heterogeneous and dominated by rare pioneer bacteria" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: James Slauch [Reviewer #2]; Babak Momeni [Reviewer #3]. Reviewer #1 remains anonymous.

The Reviewing Editor has highlighted the concerns that require revision and/or responses, and we have included the separate reviews below for your consideration. If you have any questions, please do not hesitate to contact us.

Overall, this submission was well received by all three reviewers. The reviewers highlight that your work provides important insight into the infection process. All three reviewers feel that more explanation is needed to more clearly understand not only your experiments but also the conclusions that are drawn from them. We highly recommend that you address these issues brought up by all the reviewers when submitting your revised manuscript. This will allow the readers to fully understand and appreciate your scientific study on the concept of rare "pioneer" bacteria that are important for spread during an infection, in this case for Listeria.

Thank you for taking part in this form of peer review. We look forward to your revised manuscript.

Separate reviews [please respond to each point]:

Reviewer #1:

I don't have any major concerns.

Minor Comments:

This is a very nice manuscript that details an interesting phenomenon that is likely to have impact on a number of both intracellular and extracellular pathogens. I only have a few minor comments.

1] The authors should note that the idea of a pioneer leading a charge that ends up in a different tissue site is a variation of their model, that leads to bottlenecking. In Listeria, this spreading of a few founder bacteria from the intestine into deeper tissue sites was first shown by Waldor and coworkers [Zhang [PMID: 28559314].

2] Figure 3D is really the heart of the paper and shows that the simulations have predictive value. Unfortunately, the measurement parameters of circularity have to be defined better within the manuscript for the nonmathematicians because this turns out to be the single most important measurement parameter in the manuscript. The concept is intuitive, but better definitions within the body of the results are necessary to explain what deviation from circularity means.

3] Subsection “Simulations predict that heterogeneous spread increases the chance of a persistent Listeria monocytogenes infection in the intestinal epithelium”: I don't like the term death to the animal, since during growth in tissue, death may occur at much lower loads of bacteria than what the authors predict for death. Uncontrolled tissue growth is more appropriate. Their definition of death is a blackening out of the movie images.

4] Figure 5D is quite important, but there is no definition of what the colors mean. I think that Red: Bacterial clearance; Green: host animal death; Blue: stable steady state, with blue stable when the Df/Ds ratio is stable

Reviewer #2:

Ortega et al. combine modeling and experimentation to promote the concept that rare "pioneer" bacteria, which spread beyond the neighboring host cells, are important in the biology of Listeria. This is an interesting and very well written paper that provides important insight into the infection process. It builds on quantitative knowledge gained over many years to inform their models. I have only minor comments on presentation.

Although generally clear, throughout the paper and in the figure legends, the authors should strive to acknowledge which parameters are based on assumptions and/or simplifications, and which are based on experimental data.

Minor Comments:

1] Figure 1C. Y axis. Average fluorescence intensity of what?

2] Subsection “Allowing simulated bacteria to interconvert between pioneer and non-pioneer behavior recapitulates the non-circular phenotype of experimental foci” and Figure 3D. As stated, the circularity of the simulations is dependent on time, but it is not clear how this time relates to the experimental results being used as the benchmark. Please comment.

3] Figure 5D. You need to label the lines. After I stared at it a little while, I realized that the "outcomes" colors are indicated under 5C, but it was not immediately obvious.

4] Subsection “Allowing simulated bacteria to interconvert between pioneer and non-pioneer behavior recapitulates the non-circular phenotype of experimental foci” and Figure 5C. You carefully discuss the fact that clearance versus death is dependent on the rate of host cell extrusion, but it is not clear what times you used for extrusion relative to replication, for example. Minutes, hours, days…?

5] I also wonder if you considered having extrusion dependent on the number of bacteria in the host cell instead of time.

6] Some people might read primarily the discussion. Redefine or just spell out "MSD" in paragraph five of the Discussion section.

Additional data files and statistical comments:

Seems more than thorough.

Reviewer #3:

The authors use modeling to infer the mechanism of cell-to-cell spread of L. monocytogenes in epithelial cells. They start by observing that despite expecting each infection to be clonal, the spread appears anisotropic. They then create simple models of random walk [continuum and agent-based] and note that their model predictions do not match experimental observations. Based on insights on known mechanisms of spread, they propose a different model in which a small fraction of progeny spread over longer distances, and examine whether such a model describes the observations properly.

The manuscript has a logical flow, is easy to follow, and has a nice combination of theoretical modeling and supporting experiments. It is generally well-written and contains useful and interesting information. I find the premise interesting and important, but I feel some of the results could benefit from additional explanation to clarify the rationale for some of the assumptions made in the model.

Main concerns:

1] One concern regards the construction of the model. The observed parameter that the authors have used to fit the parameters is "circularity", and their model they introduce two additional degrees of freedom [Dfast/Dslow and P], both of which affect circularity. When fitting the parameters of their model, they assume Dfast/Dslow = 100, and sweep over values of P [in Figure 3D] to find a P value for which their simulations match the experimental data. Since circularity is the only parameter they are using for fitting, it leaves the question open if with other values of these parameters the same outcome can be achieved [say, Dfast/Dslow = 10 and P = 1e-4]. I suspect the authors have already examined this when constraining their model parameters, but I think it helps to explicitly mention their process of eliminating the alternative possibilities in their manuscript.

2] When examining the GRR mutant, the authors have mentioned that "This mutant… is less persistent than wild-type bacteria and it is therefore likely to enter protrusions at a lower frequency than wild-type bacteria and to form protrusions that are less straight". First, the authors should clarify what they mean by "less persistent", and why persistence matters.

Second, if I understand correctly, the reference they cite mentions that cell-to-cell spread is less efficient with GRR mutant and the trajectory of protrusions is less straight. I expected the authors to quantitatively assess the hypothesis that the spread of GRR mutant still follows their model [with different P and Dfast/Dslow parameters that they estimate]. In my opinion, this is a missed opportunity in the current paper. The experiments are already done by the authors and there are some estimates of the difference between WT and GRR mutants in the reference they have cited. Thus, the only remaining part is making quantitative predictions based on the model about how the spread is expected to be and comparing those predictions with experimental data. If the results match, that would re-enforce the model, and if they don't, perhaps they can speculate what other factors might be involved.

3] In the last section of the Results: "Simulations predict that heterogeneous spread increases the chance of a persistent Listeria monocytogenes infection in the intestinal epithelium", in my opinion, there are aspects that need to be clarified. What is the significance of a stable steady state infection per infection site/villus? Based on the discussions in the paper, one can consider the overall infection as a metapopulation of several sites/villi. In this context, a steady-state infection would be important in the overall infection, not at each site/villus, since unstable sites/villi can still persist at the metapopulation level. This is because infections that spread too quickly are still primarily contained within that site [conceptually similar to coexistence with spatial refuge in a prey-predator ecological model] and the ones that are cleared can still infect other sites [dispersal-clearance balance, conceptually similar to mutation-selection balance in evolutionary theory]. Without a more thorough investigation, it is not obvious to me why the special case of stable steady state would be the best strategy for maintaining an infection compared to these alternatives. I suggest de-emphasizing stable steady state infection at each site as the "evolutionarily preferred" solution for persistent infection.

Minor comments:

1] In determining the circularity, the authors mention that they use the smallest circle that contains all the points. How do you find this circle? Maybe I am overthinking it, but to me this is not trivial.

2] The choice of P = 0.01 is justified in Figure 3D, but it's not clear to me how the choice of Dfast/Dslow = 100 was made. Would you please elaborate?

3] Is the "effective" diffusion coefficient in Figure 5C the same as Dslow? I think it is and it helps to mention it explicitly.

4] I think it would have helped to include the spread for another microbe that does not have extracellular protrusions [no pioneer cells, as a point of reference], as a negative control. Would you observe a homogeneous plaque shape in these cases? If not, what are the parameters involved, and do they contribute to the heterogeneous spread observed for L. monocytogenes? I suggest this only as an optional addition, if such a system already exists.

Reviewer #1:

I don't have any major concerns.

Minor Comments:

This is a very nice manuscript that details an interesting phenomenon that is likely to have impact on a number of both intracellular and extracellular pathogens. I only have a few minor comments.

1] The authors should note that the idea of a pioneer leading a charge that ends up in a different tissue site is a variation of their model, that leads to bottlenecking. In Listeria, this spreading of a few founder bacteria from the intestine into deeper tissue sites was first shown by Waldor and coworkers [Zhang [PMID: 28559314].

Thank you for the suggestion. We have added a few sentences about bottlenecking in paragraph ten of the Discussion and included a citation of Zhang et al., 2017.

2] Figure 3D is really the heart of the paper and shows that the simulations have predictive value. Unfortunately, the measurement parameters of circularity have to be defined better within the manuscript for the nonmathematicians because this turns out to be the single most important measurement parameter in the manuscript. The concept is intuitive, but better definitions within the body of the results are necessary to explain what deviation from circularity means.

We agree that we should have explained the concept of circularity better. In the Results section, we have added the following sentence: “For a perfect circle, this metric would be equal to 1, and for a square, this metric would be equal to 2/pi [Zheng et al., 2015].” This added citation also describes circularity in more detail.

3] Subsection “Simulations predict that heterogeneous spread increases the chance of a persistent Listeria monocytogenes infection in the intestinal epithelium”: I don't like the term death to the animal, since during growth in tissue, death may occur at much lower loads of bacteria than what the authors predict for death. Uncontrolled tissue growth is more appropriate. Their definition of death is a blackening out of the movie images.

This is a good point. We have changed all instances of “host animal death” to “uncontrolled growth.” We made these changes in the text; Figure 5 and Figure 5—figure supplement 1 [previously Figure S6A]; and Video 9.

4] Figure 5D is quite important, but there is no definition of what the colors mean. I think that Red: Bacterial clearance; Green: host animal death; Blue: stable steady state, with blue stable when the Df/Ds ratio is stable

We apologize for the oversight. The figure legend is the same as Figure 5C. We have added the labels to Figure 5D.

Reviewer #2:

Ortega et al. combine modeling and experimentation to promote the concept that rare "pioneer" bacteria, which spread beyond the neighboring host cells, are important in the biology of Listeria. This is an interesting and very well written paper that provides important insight into the infection process. It builds on quantitative knowledge gained over many years to inform their models. I have only minor comments on presentation.

Although generally clear, throughout the paper and in the figure legends, the authors should strive to acknowledge which parameters are based on assumptions and/or simplifications, and which are based on experimental data.

Minor Comments:

1] Figure 1C. Y axis. Average fluorescence intensity of what?

Thank you for catching this. This was meant to be “Total bacterial fluorescence intensity.” We have changed the axis label and figure legend accordingly.

2] Subsection “Allowing simulated bacteria to interconvert between pioneer and non-pioneer behavior recapitulates the non-circular phenotype of experimental foci” and Figure 3D. As stated, the circularity of the simulations is dependent on time, but it is not clear how this time relates to the experimental results being used as the benchmark. Please comment.

The times are comparable as 1 simulation step is, on average, equivalent to 1.7 minutes. Therefore, 800 steps, when simulations were stopped, are roughly equivalent to 1360 minutes, which is approximately the last time point of the experimental data. We have included this information in the figure legend.

3] Figure 5D. You need to label the lines. After I stared at it a little while, I realized that the "outcomes" colors are indicated under 5C, but it was not immediately obvious.

This was also noted by reviewer #1 [minor point 4]. We have added the label to Figure 5D.

4] Subsection “Allowing simulated bacteria to interconvert between pioneer and non-pioneer behavior recapitulates the non-circular phenotype of experimental foci” and Figure 5C. You carefully discuss the fact that clearance versus death is dependent on the rate of host cell extrusion, but it is not clear what times you used for extrusion relative to replication, for example. Minutes, hours, days…?

We agree that it is useful to clarify the correspondence between times and distances in the simulation with real times and distances. The bacterial replication rate of 1 is equivalent to 0.006 replication events per minute. This was calculated by normalizing the simulation steps by the doubling time of intracellular L. monocytogenes, which is approximately 120 minutes in MDCK cells. Then, the period of host cell extrusion ranges from a period of 0.10 [i.e. 10 simulation steps] to 0.50 [i.e. 50 simulation steps]. These translate to approximately 17 to 85 minutes respectively. The radius of a human enterocyte is approximately 7 µm [Ho et al., 2017], which scales the effective diffusion coefficients to speeds from 0 to 0.2 µm/sec, which is the average speed of intracellular L. monocytogenes [Robbins et al., 1999]. We have added this information to the legend of Figure 5.

5] I also wonder if you considered having extrusion dependent on the number of bacteria in the host cell instead of time.

Thank you for raising this possibility. This kind of mechanism would be another way of enabling establishment of a stable steady state; by forcing extrusion to occur after a preset number of bacteria is reached, one effectively inhibits both uncontrolled growth [cannot get to high numbers since the host cell will then be immediately extruded] and bacterial clearance [since extrusion stops if bacterial counts get low]. This scenario would require that the host cell be able to measure the number of intracellular bacteria in some way. Our model demonstrates an alternate physical mechanism by which steady state can be achieved without additional sensing capabilities on the part of the host cell. We have included a brief comment on this in the Discussion paragraph ten.

6] Some people might read primarily the discussion. Redefine or just spell out "MSD" in paragraph five of the Discussion section.

We have now fixed that.

Additional data files and statistical comments:

Seems more than thorough.

Reviewer #3:

The authors use modeling to infer the mechanism of cell-to-cell spread of L. monocytogenes in epithelial cells. They start by observing that despite expecting each infection to be clonal, the spread appears anisotropic. They then create simple models of random walk [continuum and agent-based] and note that their model predictions do not match experimental observations. Based on insights on known mechanisms of spread, they propose a different model in which a small fraction of progeny spread over longer distances, and examine whether such a model describes the observations properly.

The manuscript has a logical flow, is easy to follow, and has a nice combination of theoretical modeling and supporting experiments. It is generally well-written and contains useful and interesting information. I find the premise interesting and important, but I feel some of the results could benefit from additional explanation to clarify the rationale for some of the assumptions made in the model.

Main concerns:

1] One concern regards the construction of the model. The observed parameter that the authors have used to fit the parameters is "circularity", and their model they introduce two additional degrees of freedom [Dfast/Dslow and P], both of which affect circularity. When fitting the parameters of their model, they assume Dfast/Dslow = 100, and sweep over values of P [in Figure 3D] to find a P value for which their simulations match the experimental data. Since circularity is the only parameter they are using for fitting, it leaves the question open if with other values of these parameters the same outcome can be achieved [say, Dfast/Dslow = 10 and P = 1e-4]. I suspect the authors have already examined this when constraining their model parameters, but I think it helps to explicitly mention their process of eliminating the alternative possibilities in their manuscript.

We included these data in what was previously Figure S5F [and has now been moved to Figure 3D]. In this figure, we spanned a range of values of Dfast/Dslow and showed that larger values of Dfast/Dslow make foci less circular. We have expanded the text to make this more clear [subsection “Decreasing the persistence of bacterial motility leads to more circular infection foci”].

2] When examining the GRR mutant, the authors have mentioned that "This mutant… is less persistent than wild-type bacteria and it is therefore likely to enter protrusions at a lower frequency than wild-type bacteria and to form protrusions that are less straight". First, the authors should clarify what they mean by "less persistent", and why persistence matters.

We have expanded our explanation of directional persistence in subsection “Decreasing the persistence of bacterial motility leads to more circular infection foci”.

Second, if I understand correctly, the reference they cite mentions that cell-to-cell spread is less efficient with GRR mutant and the trajectory of protrusions is less straight. I expected the authors to quantitatively assess the hypothesis that the spread of GRR mutant still follows their model [with different P and Dfast/Dslow parameters that they estimate]. In my opinion, this is a missed opportunity in the current paper. The experiments are already done by the authors and there are some estimates of the difference between WT and GRR mutants in the reference they have cited. Thus, the only remaining part is making quantitative predictions based on the model about how the spread is expected to be and comparing those predictions with experimental data. If the results match, that would re-enforce the model, and if they don't, perhaps they can speculate what other factors might be involved.

Thank you for this interesting suggestion. We ran additional simulations to test what feature of the change in behavior of the GRR mutant is most likely to allow the bacteria to make more circular foci. In these simulations, we varied both the directional persistence and the Dfast/Dslow ratio. The data show that persistence has little effect on circularity, particularly in the presence of pioneers [Dfast/Dslow > 1]. Specifically, when bacteria are perfectly persistent, circularity actually increases in a random walk [at this artificial extreme, essentially all the bacteria simply move straight outward from the center]. However, when pioneers are present, changing the persistence alone ends up having very little effect on circularity over a very large range of possible values. Given that changes in persistence over a degree quantitatively much greater than the actual persistence change for the GRR mutant has no effect on circularity in the simulations, but that changing Dfast/Dslow does, we conclude that the GRR mutant Lm likely is deficient in making long extracellular protrusions, i.e. GRR pioneers take shorter steps than wild-type pioneers. These new results are summarized in Figure 4—figure supplement 1 and discussed in more detail in the fourth paragraph of the Discussion.

3] In the last section of the Results: "Simulations predict that heterogeneous spread increases the chance of a persistent Listeria monocytogenes infection in the intestinal epithelium", in my opinion, there are aspects that need to be clarified. What is the significance of a stable steady state infection per infection site/villus? Based on the discussions in the paper, one can consider the overall infection as a metapopulation of several sites/villi. In this context, a steady-state infection would be important in the overall infection, not at each site/villus, since unstable sites/villi can still persist at the metapopulation level. This is because infections that spread too quickly are still primarily contained within that site [conceptually similar to coexistence with spatial refuge in a prey-predator ecological model] and the ones that are cleared can still infect other sites [dispersal-clearance balance, conceptually similar to mutation-selection balance in evolutionary theory]. Without a more thorough investigation, it is not obvious to me why the special case of stable steady state would be the best strategy for maintaining an infection compared to these alternatives. I suggest de-emphasizing stable steady state infection at each site as the "evolutionarily preferred" solution for persistent infection.

This is an important point to clarify. At the level of the bacterial population infecting any single villus, it is most important for bacteria to avoid uncontrolled growth in any single site as this could result in tissue damage and possible death of the animal. But, if every infection site in the entire intestine were cleared, there would be no persistent infection to result in fecal shedding of replicated bacteria that can go on to infect other hosts. In this context, the observation that the “blue” range in Figure 5C is so narrow for the model based on a simple random walk suggests that it would be very difficult to hit this “sweet spot” for the whole metapopulation in the intestine, as the most likely outcomes at any given cell extrusion rate would always be either complete clearance at every site or rampant uncontrolled growth. At the level of the metapopulation, then, our results suggest that the “best” option for the bacteria to most likely persist at some few sites would be to have a fairly large range of parameters where both clearance [red] and stable steady state [blue] are possible outcomes for the bacterial population in any single villus. Figure 5D indicates that this is exactly the result of adding pioneer behavior to the cell extrusion simulations. So, having the expanded “blue” range in Figure 5D, i.e. an expanded regime to attain a stable steady state, means that not all bacteria were cleared and also that uncontrolled growth at any single site within the entire intestinal metapopulation could be avoided. This specifically is the outcome that we argue is most favorable for the bacteria. We have included a more complete explanation in paragraph eleven of the Discussion section.

Minor comments:

1] In determining the circularity, the authors mention that they use the smallest circle that contains all the points. How do you find this circle? Maybe I am overthinking it, but to me this is not trivial.

Indeed you are right, this is not trivial. The detailed explanation for this algorithm can be found in Zheng and Hryciw, 2015, which we have now cited in subsection “Stochastic simulations of cell-to-cell spread via random walks are inconsistent with observed shapes of infection foci” and the Materials and methods section. In short, once the boundary of the focus is calculated using MATLAB’s boundary[] function, the two farthest vertices are determined, which set the diameter of a temporary circle. If all the other points are contained within the circle, then the algorithm stops. If not, then the point that lies farthest outside of the circle is used to draw a new a circle [including the first two original points. The algorithm proceeds iteratively until the smallest circumscribing circle is determined.

2] The choice of P = 0.01 is justified in Figure 3D, but it's not clear to me how the choice of Dfast/Dslow = 100 was made. Would you please elaborate?

We chose the value of Dfast/Dslow to be 100 because the observed size of jumps for individual pioneer events are approximately 10 times longer than for non-pioneers, and the effective step size scales as the square root of the diffusion coefficient. We explain this in paragraph four of subsection “Allowing simulated bacteria to interconvert between pioneer and non-pioneer behavior

recapitulates the non-circular phenotype of experimental foci”.

3] Is the "effective" diffusion coefficient in Figure 5C the same as Dslow? I think it is and it helps to mention it explicitly.

Figure 5C describes a simple random walk with only a single effective diffusion coefficient. We have added “random walk simulations with only a single effective diffusion coefficient [no pioneers]” to the figure legend.

4] I think it would have helped to include the spread for another microbe that does not have extracellular protrusions [no pioneer cells, as a point of reference], as a negative control. Would you observe a homogeneous plaque shape in these cases? If not, what are the parameters involved, and do they contribute to the heterogeneous spread observed for L. monocytogenes? I suggest this only as an optional addition, if such a system already exists.

Thank you for the suggestion. Among bacterial pathogens that use this form of actin-based motility to spread from cell to cell, we do not know of any pathogens that are unable to make extracellular protrusions. However, in addition to being able to make protrusions [both intracellular and extracellular], Burkholderia thailandesis spreads primarily by inducing the fusion of neighboring host cells [French et al., 2011]. If most spread occurs by fusion with neighboring cells, we would expect that this kind of spread would yield more circular foci than those dominated by pioneer behavior such as we have described here for L. monocytogenes. Indeed, B. thailandesis generates infectious foci that are indeed significantly more isotropic than those created by L. monocytogenes [French et al., 2011]. We have added a new paragraph in the Discussion section describing this.

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