High-throughput imaging and quantitative analysis uncovers the nature of plasmid positioning by ParABS

  1. Robin Köhler
  2. Eugen Kaganovitch
  3. Seán M Murray

  • Curated by eLife

    eLife logo

    eLife assessment

    This study provides new experimental data and detailed modeling of the partitioning of low copy plasmids under the control of the ParABS system in bacteria. The dynamics of the partition complex is tracked over many generations, providing useful data to constrain the models. The authors propose a model which can manifest either regular positioning or oscillations depending on the model parameters. The research will be of interest to biologists and biophysicists interested in cellular dynamics and internal organization in bacteria.

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The faithful segregation and inheritance of bacterial chromosomes and low-copy number plasmids requires dedicated partitioning systems. The most common of these, ParABS, consists of ParA, a DNA-binding ATPase and ParB, a protein that binds to centromeric-like parS sequences on the DNA cargo. The resulting nucleoprotein complexes are believed to move up a self-generated gradient of nucleoid-associated ParA. However, it remains unclear how this leads to the observed cargo positioning and dynamics. In particular, the evaluation of models of plasmid positioning has been hindered by the lack of quantitative measurements of plasmid dynamics. Here, we use high-throughput imaging, analysis and modelling to determine the dynamical nature of these systems. We find that F plasmid is actively brought to specific subcellular home positions within the cell with dynamics akin to an over-damped spring. We develop a unified stochastic model that quantitatively explains this behaviour and predicts that cells with the lowest plasmid concentration transition to oscillatory dynamics. We confirm this prediction for F plasmid as well as a distantly-related ParABS system. Our results indicate that ParABS regularly positions plasmids across the nucleoid but operates just below the threshold of an oscillatory instability, which according to our model, minimises ATP consumption. Our work also clarifies how various plasmid dynamics are achievable in a single unified stochastic model. Overall, this work uncovers the dynamical nature of plasmid positioning by ParABS and provides insights relevant for chromosome-based systems.

Article activity feed

  1. Version published to 10.7554/elife.78743 on eLife
  2. eLife

    Author Response

    Reviewer #1 (Public Review):

    Kohler and Murray present high-throughput image-based measurements of how low-copy F plasmids move (segregate) inside E. coli cell. This active segregation ensures that each daughter cell inherit equal share of the plasmids. Previous work by different labs has shown that faithful F-plasmid segregation (as well as segregation of many other low-copy plasmids, segregation of chromosomes in many bacterial species and segregation of come supramolecular complexes) require ParA and ParB proteins (or proteins similar to them) and is achieved by an active transport mechanism. ParB is known to bind to the cargo (plasmid) and ParA forms a dimer upon ATP binding that binds to DNA (chromosome) non-specifically and also can bind to ParB (associated with cargo). After ATP hydrolysis (stimulated by the interaction with ParB), ParA dimer dissociates to monomers and from ParB and the chromosome. While different mechanisms of the ParA-dependent active transport had been proposed, recently two mechanisms become most popular - one based on the elastic dynamics of the chromatin (Lim et al. eLife 2014, Surovtsev PNAS 2016, Hu et al Biophys.J 2017, Schumaher Dev.Cell 2017) and the other based on a theoretically-derived "chemophoretic" force (Sugawara & Kaneko Biophysics 2011, Walter et al. Phys.Rev.Lett. 2017).

    It is a minor comment, but we would like to point out that we do not consider these two model types as alternatives but rather as models with different levels of coarse-graining. Our interest is in the molecular-level (stochastic) models (Lim et al. eLife 2014, Surovtsev PNAS 2016, Hu et al PNAS 2015, Hu et al Biophys.J 2017, Schumacher Dev.Cell 2017).

    The authors start by following motion of F plasmid with one or two plasmids per cell and by analyzing plasmid spatial distribution, plasmid displacement (referred to as velocity) as a function of their relative position, and autocorrelations of the position and the displacement. They concluded that these metrics are consistent with 'true positioning' (i.e. average displacement is biased toward the target position - center for one plasmid and 1/4 and 3/4 positions for two plasmids ) but not with 'approximate positioning' (i.e. when plasmid moves around target position, for example, in near-oscillatory fashion). This 'true positioning' can be described as a particle moving on the over-dampened spring. They reproduce this behavior by expanding the previous model for 'DNA-relay' mechanism (Lim et al. eLife 2014, Surovtsev PNAS 2016), in which plasmid is actively moved by the elastic force from the chromosome and ParA serves to transmit this force from the chromosome to the plasmid. Now, the authors explicitly consider in the model that the chromosome-bound ParA can diffuse (which the authors refer as 'hopping') and this allows the model to achieve 'true plasmid positioning' for some combination of model parameters in addition to oscillatory dynamics reported in the original paper (Surovtsev PNAS 2016).

    Based on their computational model, the authors proposed that two parameters, diffusion scale of ParA = 2(2Dh/kd)1/2/L (typical length diffused by ParA before dissociation) and ratio of ParB-dependent and independent hydrolysis rates = kh/kd are key control parameters defining what qualitative behavior is observed - random diffusion, near-oscillatory behavior, or overdamped spring ('true positioning'). They vary this two parameters ~30- fold and ~200-fold range by changing Dh and kh respectively, to illustrate how dynamics of the system changes between these 3 modes of motion. While these parameters clearly play important role, the drawback is that the authors did not put either theoretical reasoning why these parameters are truly governing or showed it by varying other model parameters (kh, number of ParA NParA, spring constant of chromosome k, diffusion coefficient of the plasmid Dp) to show that only these combinations define the type of the system behavior. The authors qualitative analysis on importance of relies on the steady state solution for the diffusion equation for ParA. It is really unfortunate that no ParA distribution was measured simultaneously with the plasmid motion, as this would allow to compare experimental ParA profiles to expected quasi-steady-state solutions.

    We spend almost an entire section and a figure explaining the theoretical reasoning behind the identification of the $\lambda=s/(L/2n)$ as an important system parameter (section “Hopping of ParA-ATP on the nucleoid as an explanation of regular positioning” and Figure 2) and predicted that regular positioning could only occur for $\lambda>1$. This was confirmed by parameter sweeps for the cases of 1 (Figure 3I) and multiple plasmids (Figure 5-figure supplement 1), indicating that $\lambda$ is indeed an important system parameter and that our conceptual understanding of this aspect of the system is correct. This point has now been made clearer.

    However, we agree that the reasoning for $\epsilon$ (varied through the hydrolysis rate $k_h$) was not clear. It was chosen to allow us to modulate the ParA concentration at the plasmid compared to elsewhere, motivated by the differences between different ParABS systems. We originally had also considered a third quantity related to the number of nucleoid-bound ParA but we found that this had little effect on the nature of the dynamics. All three quantities describe how the timescale of a reaction/process (ParA hopping/diffusion across the nucleoid, ParB induced hydrolsysis, ParA association to the nucleoid) compares to the timescale of basal hydrolysis, which we use as a reference timescale.

    We have now made this clearer as well as adding supplementary figures showing the effect of varying other system parameters at several locations in the phase diagram (Figure 3-figure supplement 3 and 4). These sweeps justify our identification of $\epsilon$ and $\lambda$ as a useful/important set of quantities for determining the dynamics of the system.

    Additionally, we now add example kymographs showing the ParA distribution (Figure 3-figure supplement 2C).

    The authors also show by simulations that overdamped spring dynamics can transition into oscillatory behavior when decreases, for example by cell growth. Indeed, they observed more oscillatory behavior when they compared single-plasmid dynamics in the longer cells compared to the shorter cells. This was not the case in double-plasmid cells, in eprfect agreement with their analysis. They also calculated ATP consumption in the model and concluded that the system operates close but below (perhaps, "above" should be used as it refers to bigger ) the threshold to oscillatory regime which minimize ATP consumption. While ATP consumption analysis is very intriguing, this statement (Abstract Ln24-25) seems at odds with the authors own analysis that another ParA-dependent plasmid system, pB171, operates mostly in oscillatory regime, and it is actually for this regime the authors' analysis suggest minimal ATP-consumption (Fig. 8).

    To clarify, we found that pB171 (which in our hands has a copy number of 2-3 in the SR1 reduced-copy-number strain) is only clearly oscillatory in cells with a single plasmid (and only mildly so in cells with two plasmids). Otherwise, it behaves very similarly to F plasmid. We therefore believe that these two distantly related ParABS systems exhibit, overall, similar dynamics and differ only in how close the systems are to the threshold of oscillatory instability. This was not clear as we did not specify the copy number of pB171. We now provide this in Figure 7–figure supplement 1.

    We refer to these systems as lying just below, rather than above, the threshold of the oscillatory instability because, on average, plasmids do not oscillate but only do so in cells with the lowest plasmid concentration.

    I think the real strength of the paper is that it can potentially to show that if one considers that the intracellular cargo can be moved by the fluctuating chromosome via ParA-mediated attachments, then various dynamics can be achieved depending on combinations of several control parameters (plasmid diffusion coefficient, ParA diffusion coefficient, rate of hydrolysis and so on) including previously reported 'oscillations' (Surovtsev PNAS 2016), 'local excursions' (Hu et al Biophys.J 2017) and 'true positioning' (Schumaher Dev.Cell 2017). The main drawback (in this reviewer opinion) that this is obscured by the current presentation and discussion of this work and previous modelling work on ParA-dependent systems. For example, instead of using "unifying" potential of the presented model, yet another name 'relay and hopping' is used in addition to previously used 'DNA-relay', 'Brownian ratchet', 'Flux-based positioning', …

    In the abstract and discussion, we already refer to developing a “unified” model (p1 L21, p15 L22 of the original manuscript) and in the discussion we explain how our model contains other models as limiting cases. But we agree with this recommendation - the unifying nature of our model is its main strength. We now emphasise this more.

    Regarding the model name, we felt obliged to refer to the previous named models (DNA-relay and Brownian ratchet) and simply gave our model a name to avoid confusion when making comparisons. We have now removed almost all mention of ‘hopping and relay’ and just refer to ‘our model’. However, our gitlab repository with the code must have a name and therefore is still called ‘Hopping and relay’ and so the same term is used in Table 3.

    … and it appears that the presented model is an alternative to these previously published work. And only in model description (in Methods section) one can find that the "... model is an extension of the previous DNA-relay model (Surovtsev et al., 2016a) that incorporates hopping and basal hydrolysis of ParA and uses analytic expressions for the fluctuations rather than a second order approximation"(p.17, ln15-17).

    We are sorry that this reviewer felt that the fact that our model is an extension of DNA relay is hidden in the methods. However, we wrote in the main text:

    “Motivated by the previous discussion, we decided to develop our own minimal molecular model (‘hopping and relay’) of ParABS positioning, taking the DNA relay model as a starting point … The original scheme is as follows… We supplemented this scheme with two additional components: diffusion (hopping) of DNA-bound ParA-ATP dimers across the nucleoid (with diffusion coefficient Dh, where the subscript indicates diffusion of the home position) and plasmid-independent ATP hydrolysis and dissociation (with rate kd). See Material and Methods for further details of the model. “

    We now make this clearer.

    However, we would argue that as models of the same system, there are naturally overlaps and the models of Hu et al and Schumacher et al could also be thought of as extensions of the DNA relay model.

    While it is of course the authors right to decide how to name their model, it should be explicitly clear to the reader what is a real conceptual difference between presented and previous models from the abstract, introduction and discussion section of the paper, not from the "fine-print" details in the supplementary materials.

    The main conceptual difference is that we have identified the importance of having a finite diffusive length scale for ParA diffusion/hopping on the nucleoid. This allows both oscillations and regular positioning to occur for biologically relevant parameter values and reproduces the length dependent transition from mid-cell positioning to confined oscillations that we observe for F plasmid. The DNA relay model does not have this behaviour as the ParA diffusive length scale in zero while it is infinite in the models of Ietswaart et al 2014 and Schumacher et al 2017. The model of Hu et al 2017 does have a finite length scale but the authors appear not to have realised its importance and never discovered the regular positioning regime at \lambda >1. While we make these points in the discussion in the context of Figure 8A, where we compare our model to the others, we agree with this reviewer that we should have been more explicit in the abstract and introduction. We have now corrected this.

    This would allow to avoid unnecessary confusion (especially for the readers not directly involved into the modelling of ParA/B system) and clarify that all these models rely on the elastic behavior of fluctuating chromosome to drive active transport of the cargo. This reviewer believes that more explicit discussion on the models (one from the authors and previously published) differences and similarities will help with our understanding of how ParA-dependent system operate. This discussion should also include works on PomXYZ system, in which it was shown that similar dynamic system can lead to specific positioning within the cell (Schumaher Dev.Cell 2017, Kober et al. Biophys.J 2019). This will may it explicit that the models results have direct impact beyond the ParA-dependent plasmid segregation.

    To further clarify the differences between the models (beyond the second and third sections of the main text and the discussion), we have now added a section to the methods and a new table (Table 3). We have also included the mentioned PomXYZ model. However, we would like this was not the first stochastic model to have ‘true’ positioning as this reviewer cites above. Though they did not include the mechanism of force generation, the model of Ietswaart et al 2014 produces regularly positioned plasmids and is referenced repeatedly in Schumacher et al. 2017.

    I think that expanded parameter analysis, and explicit model comparison/discussion will make the contribution of this work to the field more clear and with the potential to advance our general understanding of how the same underlying mechanism can lead to various modes of intracellular dynamics and patterning depending on parameters combination.

    Reviewer #2 (Public Review):

    The work presented in this manuscript details an analysis of the partitioning of low copy plasmids under the control of the ParABS system in bacteria. Using a high throughput imaging set up they were able to track the dynamics of the partition complex of one to a few plasmids over many cell cycles. The work provides an impressive amount of quantitative data for this chemo-mechanical system. Using this data, the paper sought to clarify whether the dynamics of plasmids is due to regular positioning or noisy oscillations around a mean position. They supplement their experimental work with an intuitive model that combines elements of previous modelling efforts. Their model relies on diffusion of the ParA substrate on the nucleoid with the dynamics of the ParB partition complex being driven by the underlying elastic force due to the nucleoid on which the substrate is tethered. Their model dynamics depend on two parameters, the ratio of the length over which the substrate can explore to the characteristic length of the space and the ratio of stimulated to non-stimulated hydrolysis rates of the substrate. If the length ratio is large, ParA can fully explore the space before interacting with the ParB complex leading to balanced fluxes and regular positioning. If it gets reduced, for example by lengthening the cell, oscillations can emerge as fluxes of substrates become imbalanced and a net force can pull the partition complex.

    Strengths:

    Given the large amount of data, the observations unambiguously show that one particular ParABS system under the conditions studied is carrying out regular positioning of plasmids. The model synthesizes prior work into a nice intuitive picture. These model parameters can be fit to the data leading to estimates of molecular kinetic parameters that are reasonable and in line with other observations. Lining up the experimental observations with the phase space of the model suggests that the system is poised on the edge of oscillations, allowing for the system to have regular positioning with low resource consumption.

    Weaknesses:

    However, despite the correspondence of the simulated results with the experimental findings, other explanations are not completely ruled out. The paper emphasizes that ParA diffusion/hopping on the nucleoid is essential for the establishment of regular positioning and that without it, only oscillations were possible. Prior simulation efforts, that the paper cites, which include ParA diffusion and mixing in the cytosol but no diffusion on the nucleoid have shown that regular positioning is possible and that oscillations could get triggered as the system lengthened. Thus ParA hopping is not a necessity for regular positioning (as claimed in the paper), but very well might be needed for the given kinetic parameters of the system studied here.

    We now comment on this result. In short, we believe that the mentioned model/regime is not relevant due to stochastic effects. We are not able to produce, with biological relevant parameters, regular positioning without ParA hopping.

    The paper also presents experimental results for a second ParABS system (pB171) that is more likely to show oscillations. They attribute the greater likelihood of oscillations for pB1717 being due to ParA exploring a smaller space than the F plasmid system that showed regular positioning. This is pure conjecture and the paper does not provide any evidence that this is the reason. Thus it is hard to conclude if oscillations may not be due to other factors.

    We do not explicitly make that claim. We did have a point in the phase diagram of Figure 8A representing pB171 with a lower value of lambda than F plasmid and stated “The location of pB171 is an estimate based on a qualitative comparison of its dynamics”. We agree this was unclear.

    We now indicate the region that has oscillations with roughly the same period as single plasmids of pB171. We also make it clear that we speculate, but have not shown, that the length scale of ParA hopping is smaller than for F plasmid.

    An important point here is that we can explain both oscillations and regular positioning in the same model with the same kinetic parameters, the regimes being determined by the cell length and plasmid number in a manner consistent with experimental observations.

  3. eLife

    eLife assessment

    This study provides new experimental data and detailed modeling of the partitioning of low copy plasmids under the control of the ParABS system in bacteria. The dynamics of the partition complex is tracked over many generations, providing useful data to constrain the models. The authors propose a model which can manifest either regular positioning or oscillations depending on the model parameters. The research will be of interest to biologists and biophysicists interested in cellular dynamics and internal organization in bacteria.

  4. eLife

    Reviewer #1 (Public Review):

    Kohler and Murray present high-throughput image-based measurements of how low-copy F plasmids move (segregate) inside E. coli cell. This active segregation ensures that each daughter cell inherit equal share of the plasmids. Previous work by different labs has shown that faithful F-plasmid segregation (as well as segregation of many other low-copy plasmids, segregation of chromosomes in many bacterial species and segregation of come supramolecular complexes) require ParA and ParB proteins (or proteins similar to them) and is achieved by an active transport mechanism. ParB is known to bind to the cargo (plasmid) and ParA forms a dimer upon ATP binding that binds to DNA (chromosome) non-specifically and also can bind to ParB (associated with cargo). After ATP hydrolysis (stimulated by the interaction with ParB), ParA dimer dissociates to monomers and from ParB and the chromosome. While different mechanisms of the ParA-dependent active transport had been proposed, recently two mechanisms become most popular - one based on the elastic dynamics of the chromatin (Lim et al. eLife 2014, Surovtsev PNAS 2016, Hu et al Biophys.J 2017, Schumaher Dev.Cell 2017) and the other based on a theoretically-derived "chemophoretic" force (Sugawara & Kaneko Biophysics 2011, Walter et al. Phys.Rev.Lett. 2017).

    The authors start by following motion of F plasmid with one or two plasmids per cell and by analyzing plasmid spatial distribution, plasmid displacement (referred to as velocity) as a function of their relative position, and autocorrelations of the position and the displacement. They concluded that these metrics are consistent with 'true positioning' (i.e. average displacement is biased toward the target position - center for one plasmid and 1/4 and 3/4 positions for two plasmids ) but not with 'approximate positioning' (i.e. when plasmid moves around target position, for example, in near-oscillatory fashion). This 'true positioning' can be described as a particle moving on the over-dampened spring. They reproduce this behavior by expanding the previous model for 'DNA-relay' mechanism (Lim et al. eLife 2014, Surovtsev PNAS 2016), in which plasmid is actively moved by the elastic force from the chromosome and ParA serves to transmit this force from the chromosome to the plasmid. Now, the authors explicitly consider in the model that the chromosome-bound ParA can diffuse (which the authors refer as 'hopping') and this allows the model to achieve 'true plasmid positioning' for some combination of model parameters in addition to oscillatory dynamics reported in the original paper (Surovtsev PNAS 2016).

    Based on their computational model, the authors proposed that two parameters, diffusion scale of ParA = 2(2Dh/kd)1/2/L (typical length diffused by ParA before dissociation) and ratio of ParB-dependent and independent hydrolysis rates = kh/kd are key control parameters defining what qualitative behavior is observed - random diffusion, near-oscillatory behavior, or overdamped spring ('true positioning'). They vary this two parameters ~30- fold and ~200-fold range by changing Dh and kh respectively, to illustrate how dynamics of the system changes between these 3 modes of motion. While these parameters clearly play important role, the drawback is that the authors did not put either theoretical reasoning why these parameters are truly governing or showed it by varying other model parameters (kh, number of ParA NParA, spring constant of chromosome k, diffusion coefficient of the plasmid Dp) to show that only these combinations define the type of the system behavior. The authors qualitative analysis on importance of relies on the steady state solution for the diffusion equation for ParA. It is really unfortunate that no ParA distribution was measured simultaneously with the plasmid motion, as this would allow to compare experimental ParA profiles to expected quasi-steady-state solutions.

    The authors also show by simulations that overdamped spring dynamics can transition into oscillatory behavior when decreases, for example by cell growth. Indeed, they observed more oscillatory behavior when they compared single-plasmid dynamics in the longer cells compared to the shorter cells. This was not the case in double-plasmid cells, in eprfect agreement with their analysis. They also calculated ATP consumption in the model and concluded that the system operates close but below (perhaps, "above" should be used as it refers to bigger) the threshold to oscillatory regime which minimize ATP consumption. While ATP consumption analysis is very intriguing, this statement (Abstract Ln24-25) seems at odds with the authors own analysis that another ParA-dependent plasmid system, pB171, operates mostly in oscillatory regime, and it is actually for this regime the authors' analysis suggest minimal ATP-consumption (Fig. 8).

    I think the real strength of the paper is that it can potentially to show that if one considers that the intracellular cargo can be moved by the fluctuating chromosome via ParA-mediated attachments, then various dynamics can be achieved depending on combinations of several control parameters (plasmid diffusion coefficient, ParA diffusion coefficient, rate of hydrolysis and so on) including previously reported 'oscillations' (Surovtsev PNAS 2016), 'local excursions' (Hu et al Biophys.J 2017) and 'true positioning' (Schumaher Dev.Cell 2017). The main drawback (in this reviewer opinion) that this is obscured by the current presentation and discussion of this work and previous modelling work on ParA-dependent systems. For example, instead of using "unifying" potential of the presented model, yet another name 'relay and hopping' is used in addition to previously used 'DNA-relay', 'Brownian ratchet', 'Flux-based positioning', and it appears that the presented model is an alternative to these previously published work. And only in model description (in Methods section) one can find that the "... model is an extension of the previous DNA-relay model (Surovtsev et al., 2016a) that incorporates hopping and basal hydrolysis of ParA and uses analytic expressions for the fluctuations rather than a second order approximation"(p.17, ln15-17). While it is of course the authors right to decide how to name their model, it should be explicitly clear to the reader what is a real conceptual difference between presented and previous models from the abstract, introduction and discussion section of the paper, not from the "fine-print" details in the supplementary materials. This would allow to avoid unnecessary confusion (especially for the readers not directly involved into the modelling of ParA/B system) and clarify that all these models rely on the elastic behavior of fluctuating chromosome to drive active transport of the cargo. This reviewer believes that more explicit discussion on the models (one from the authors and previously published) differences and similarities will help with our understanding of how ParA-dependent system operate. This discussion should also include works on PomXYZ system, in which it was shown that similar dynamic system can lead to specific positioning within the cell (Schumaher Dev.Cell 2017, Kober et al. Biophys.J 2019). This will may it explicit that the models results have direct impact beyond the ParA-dependent plasmid segregation.

    I think that expanded parameter analysis, and explicit model comparison/discussion will make the contribution of this work to the field more clear and with the potential to advance our general understanding of how the same underlying mechanism can lead to various modes of intracellular dynamics and patterning depending on parameters combination.

  5. eLife

    Reviewer #2 (Public Review):

    The work presented in this manuscript details an analysis of the partitioning of low copy plasmids under the control of the ParABS system in bacteria. Using a high throughput imaging set up they were able to track the dynamics of the partition complex of one to a few plasmids over many cell cycles. The work provides an impressive amount of quantitative data for this chemo-mechanical system. Using this data, the paper sought to clarify whether the dynamics of plasmids is due to regular positioning or noisy oscillations around a mean position. They supplement their experimental work with an intuitive model that combines elements of previous modelling efforts. Their model relies on diffusion of the ParA substrate on the nucleoid with the dynamics of the ParB partition complex being driven by the underlying elastic force due to the nucleoid on which the substrate is tethered. Their model dynamics depend on two parameters, the ratio of the length over which the substrate can explore to the characteristic length of the space and the ratio of stimulated to non-stimulated hydrolysis rates of the substrate. If the length ratio is large, ParA can fully explore the space before interacting with the ParB complex leading to balanced fluxes and regular positioning. If it gets reduced, for example by lengthening the cell, oscillations can emerge as fluxes of substrates become imbalanced and a net force can pull the partition complex.

    Strengths:
    Given the large amount of data, the observations unambiguously show that one particular ParABS system under the conditions studied is carrying out regular positioning of plasmids. The model synthesizes prior work into a nice intuitive picture. These model parameters can be fit to the data leading to estimates of molecular kinetic parameters that are reasonable and in line with other observations. Lining up the experimental observations with the phase space of the model suggests that the system is poised on the edge of oscillations, allowing for the system to have regular positioning with low resource consumption.

    Weaknesses:

    However, despite the correspondence of the simulated results with the experimental findings, other explanations are not completely ruled out. The paper emphasizes that ParA diffusion/hopping on the nucleoid is essential for the establishment of regular positioning and that without it, only oscillations were possible. Prior simulation efforts, that the paper cites, which include ParA diffusion and mixing in the cytosol but no diffusion on the nucleoid have shown that regular positioning is possible and that oscillations could get triggered as the system lengthened. Thus ParA hopping is not a necessity for regular positioning (as claimed in the paper), but very well might be needed for the given kinetic parameters of the system studied here.

    The paper also presents experimental results for a second ParABS system (pB171) that is more likely to show oscillations. They attribute the greater likelihood of oscillations for pB1717 being due to ParA exploring a smaller space than the F plasmid system that showed regular positioning. This is pure conjecture and the paper does not provide any evidence that this is the reason. Thus it is hard to conclude if oscillations may not be due to other factors.

  6. Version published to 10.1101/2022.03.29.486196v1 on bioRxiv