Speed variations of bacterial replisomes

  1. Deepak Bhat
  2. Samuel Hauf
  3. Charles Plessy
  4. Yohei Yokobayashi
  5. Simone Pigolotti

  • Curated by eLife

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    Evaluation Summary:

    This manuscript uses experiments and theory to characterize the variations in replication speed of E.coli throughout cell cycle. The authors developed a theory to account for fluctuations in the replication velocity as well as a cell-cycle-dependent speed, and by using sequencing data they analyzed the variations in the speed for E. coli. They found that replication speed increases with increasing temperature, and also observed oscillatory patterns in the speed of the replisome, consistent with variations in mutation rate (accuracy) across the genome. These observations suggest a tradeoff between replication speed and accuracy in E.coli.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

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Abstract

Replisomes are multi-protein complexes that replicate genomes with remarkable speed and accuracy. Despite their importance, their dynamics is poorly characterized, especially in vivo. In this paper, we present an approach to infer the replisome dynamics from the DNA abundance distribution measured in a growing bacterial population. Our method is sensitive enough to detect subtle variations of the replisome speed along the genome. As an application, we experimentally measured the DNA abundance distribution in Escherichia coli populations growing at different temperatures using deep sequencing. We find that the average replisome speed increases nearly fivefold between 17 °C and 37 °C. Further, we observe wave-like variations of the replisome speed along the genome. These variations correlate with previously observed variations of the mutation rate, suggesting a common dynamical origin. Our approach has the potential to elucidate replication dynamics in E. coli mutants and in other bacterial species.

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  1. Version published to 10.7554/elife.75884 on eLife
  2. eLife

    Author Response:

    Reviewer #2 (Public Review):

    The paper deals with experiments and theory for the variations in replication speed throughout the cell cycle. It is known that due to the structure of the bacterial cell cycle the frequencies of different loci in the genomes are different (with genes closer to the origin of replication appearing more frequently). This has been taken into advantage experimentally in previous works. Here, the authors extend the theory to account for fluctuations in the replication velocity as well as a cell-cycle-dependent speed, and analyze using sequencing data the variations in the speed for E. coli, showing interesting oscillatory patterns in the speed. The work is elegant and nicely executed.

    Comments:

    - The interpretation in terms of a speed-error trade-off is rather speculative and perhaps less emphasis should be placed on it (e.g. in the abstract and the top of p.9).

    We agree with the Reviewer that, strictly speaking, this interpretation is speculative, although the degree of correlation between mutation experiments and the speed oscillations makes a rather compelling case. In the revised version, we place less emphasis on this interpretation as requested.

    - The idea of using the frequency inferred from sequencing was also used in: Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples, Korem et al. Science. (2015)
    Are the oscillations also observed in those measurements? If so, is there information which could be gleaned from them?

    We thank the Reviewer for pointing out this interesting reference. Following this suggestion, we reanalyzed the DNA abundance from the E. coli sequencing data by Korem et al.. We found that this dataset is characterized by a much smaller coverage than our experiment. As a result, the DNA abundance distribution is too noisy to infer replisome speed variations, see Fig.1 in this document. In any case, in the Introduction of the revised version we cite this reference as another important application of the DNA abundance distribution.

    - Is it obvious a priori that Eqs. 7-9 are correct, since they do not account for the age-structure within the population? (i.e. genomes do not have a "rate" to switch to another state). The derivation in the appendix which accounts for this appears to me more systematic and compelling.

    We agree that the rates k, α and β should in principle depend on the age structure. However, it can be shown that such age-structured model becomes equivalent to our simple model if one is interested in the exponential regime. We prove this fact in a new Appendix 6 of the revised manuscript and refer to it in the Results. We are thankful to the reviewer for suggesting this idea that, we believe, further supports robustness of our model.

    - There is a systematic difference in the dependence of speed and growth rate on temperature, which the authors discuss. What is the expected change in cell size if the Cooper-Helmstetter model is correct? Should it be observable experimentally? Is it?

    Assuming perfect DNA–protein homeostasis, the expected change in cell size should be proportional to the DNA content as shown in Appendix 2, Fig. 1b. We are not aware of recent systematic studies of the dependence of cell size on temperatures. Trueba et al. (1982) suggest a moderate increase of the cell size with the growth rate, which seems compatible with our theory. However, this dependence strongly depends on the choice of the medium and the paper only reports a few data points. A systematic study of this interesting issue would require additional experiments, which are beyond the scope of our work.

    In the revised version, we clarify our prediction on the cell size behavior on temperature, and comment more extensively on its implications in the discussion section.

    - Lines 131-133: why is the average DNA per cell the product of the two other averages? Is this an approximation or are the two other variables uncorrelated?

    We thank the Reviewer for this observation. Our model of genome dynamics embodied in Eqs. (3, 4,

    1. assumes, for simplicity, that genomes evolve independently. Because of this assumption, the two averages factorize. We clarify this point in the revised manuscript.

    - This study was done in the regime of fast growth. It is known that for E. coli there are many changes in the cell cycle properties when the doubling time (at 37 Celcius) exceeds 60 minutes (i.e. the regime where there are no overlapping replication forks). How do the results change in slow growth conditions?

    We thank the Reviewer for this comment. In the revised version, we present an additional analysis of sequencing data from E.coli growing in a minimal medium (data from Midgley-Smith et al., 2018), see Figure 3- Figure supplement 4. We did not observe appreciable speed oscillations in this case. This result suggests that oscillations are linked with the multiple forks regime and disappear when the cell cycle is slowed down by either reducing temperature or nutrient composition. As discussed in the revised manuscript, this result supports the hypothesis that the cause of the oscillations might be competition among replisomes.

  3. eLife

    Evaluation Summary:

    This manuscript uses experiments and theory to characterize the variations in replication speed of E.coli throughout cell cycle. The authors developed a theory to account for fluctuations in the replication velocity as well as a cell-cycle-dependent speed, and by using sequencing data they analyzed the variations in the speed for E. coli. They found that replication speed increases with increasing temperature, and also observed oscillatory patterns in the speed of the replisome, consistent with variations in mutation rate (accuracy) across the genome. These observations suggest a tradeoff between replication speed and accuracy in E.coli.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 agreed to share their name with the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    In their manuscript, Bhat et al present a sequencing based method to analyze the dynamic activity of the replisome to replicate DNA. They studied particularly the speed of replication for steadily growing E. coli cells, growing in rich media (LB) at different temperatures. DNA replication is a most essential cellular process which needs to be coupled to other cellular processes to allow growth and replication. A better quantification of the replisome speed is thus important to derive a more integrative understanding of the microbial cell. The method introduced by the authors is a nice approach to infer the speed of replication depending on the position of the replisomes along the chromosome and can thus largely improve our understanding of replication in vivo. The authors particularly report that replication speeds are varying with temperature and speeds further oscillate with the position of the replisome. Particularly the oscillations appear to be important to think about when aiming for a more mechanistic understanding of growth. However, repeats to confirm the results are missing, the manuscript would benefit from a better comparison with published approaches, and results could be put into a better physiological context.

  5. eLife

    Reviewer #2 (Public Review):

    The paper deals with experiments and theory for the variations in replication speed throughout the cell cycle. It is known that due to the structure of the bacterial cell cycle the frequencies of different loci in the genomes are different (with genes closer to the origin of replication appearing more frequently). This has been taken into advantage experimentally in previous works. Here, the authors extend the theory to account for fluctuations in the replication velocity as well as a cell-cycle-dependent speed, and analyze using sequencing data the variations in the speed for E. coli, showing interesting oscillatory patterns in the speed. The work is elegant and nicely executed.

    Comments:

    - The interpretation in terms of a speed-error trade-off is rather speculative and perhaps less emphasis should be placed on it (e.g. in the abstract and the top of p.9).

    - The idea of using the frequency inferred from sequencing was also used in: Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples, Korem et al. Science. (2015)
    Are the oscillations also observed in those measurements? If so, is there information which could be gleaned from them?

    - Is it obvious a priori that Eqs. 7-9 are correct, since they do not account for the age-structure within the population? (i.e. genomes do not have a "rate" to switch to another state). The derivation in the appendix which accounts for this appears to me more systematic and compelling.

    - There is a systematic difference in the dependence of speed and growth rate on temperature, which the authors discuss. What is the expected change in cell size if the Cooper-Helmstetter model is correct? Should it be observable experimentally? Is it?

    - Lines 131-133: why is the average DNA per cell the product of the two other averages? Is this an approximation or are the two other variables uncorrelated?

    - This study was done in the regime of fast growth. It is known that for E. coli there are many changes in the cell cycle properties when the doubling time (at 37 Celcius) exceeds 60 minutes (i.e. the regime where there are no overlapping replication forks). How do the results change in slow growth conditions?

  6. eLife

    Reviewer #3 (Public Review):

    In this study, Bhat et al., characterize the effect of increasing temperature on replication rates in fast growing E. coli. For this, they develop a whole genome sequencing-based method to derive replication speed and position information from DNA abundance measurements. Using this assay, they find that replication speed increases with increasing temperature in a manner following the Arrhenius law. They also observe periodic fluctuations in the speed of the replisome, the pattern being consistent with the average mutation rate described previously. Based on this, the authors suggest that there is a tradeoff between replication speed and accuracy.

    Strengths:
    This study develops a quantitative method for inferring replisome dynamics from high throughput genome sequencing. This allows the authors to derive unique insights on the effects of temperature on replication rates (and cell cycle). A particularly interesting observation is with regards to the wave-like behaviour in replication speed that the authors compare to mutation rate patterns from Niccum et al., 2019.

    Limitations:
    The key conclusions of this work need to be better synthesized. There seems to be several observations made, but how they together inform about the impact of temperature on replisome dynamics remains unclear: Relationship between replication speed and temperature (and that it follows the Arrhenius law) is well-described in this study, but is not wholly unexpected. In addition, the authors suggest an increase in replisome number with increase in temperature but do not discuss the same. The authors further discuss their observations in light of the Cooper and Helmstetter model (L140-150), but this reviewer is left feeling confused about the context of the same and what it means for the understanding of temperature impact on replisome speed. Finally, how are the wave-like replisome dynamics generated? Are these driven by genome characteristics? If the pattern is reflective of regions of faster DNA synthesis (and hence increased mutation rate), then does the mutation rate itself increase with increasing temperature in a similar fashion. A better synthesis and discussion of the key results will help appreciate unique findings of this study.

  7. Version published to 10.1101/2021.10.15.464478v2 on bioRxiv
  8. Version published to 10.1101/2021.10.15.464478v1 on bioRxiv