1. Cell Biology
  2. Computational and Systems Biology

Extrusion-modulated DnaA activity oscillations coordinate DNA replication with biomass growth

  1. Dengjin Li
  2. Hai Zheng
  3. Yang Bai
  4. Zheng Zhang
  5. Hao Cheng
  6. Xiongliang Huang
  7. Ting Wei
  8. Matthew Chang
  9. Arieh Zaritsky
  10. Terence Hwa
  11. Chenli Liu  Is a corresponding author
  1. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
  2. University of Chinese Academy of Sciences, China
  3. NUS Synthetic Biology for Clinical and Technological Innovation and Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore; National Centre for Engineering Biology, Singapore
  4. Faculty of Natural Sciences, Ben-Gurion University of the Negev, Israel
  5. Department of Physics & Department of Molecular Biology, University of California at San Diego, United States
https://doi.org/10.7554/eLife.107214.3
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Cite this article
  1. Dengjin Li
  2. Hai Zheng
  3. Yang Bai
  4. Zheng Zhang
  5. Hao Cheng
  6. Xiongliang Huang
  7. Ting Wei
  8. Matthew Chang
  9. Arieh Zaritsky
  10. Terence Hwa
  11. Chenli Liu
(2025)
Extrusion-modulated DnaA activity oscillations coordinate DNA replication with biomass growth
eLife 14:RP107214.
https://doi.org/10.7554/eLife.107214.3
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eLife Assessment

This work provides high-precision single-cell data on the relationship between DnaA activity and cell size, offering important insights for the field of cell cycle control. These findings motivate a novel and intriguing hypothesis for DNA replication initiation -the "extrusion model"- in which DNA-binding proteins modulate free DnaA availability in response to biomass-DNA imbalance. While the current indirect evidence does not fully establish the model, an experimental perturbation involving H-NS offers convincing support for its plausibility, laying the groundwork for future investigation.

https://doi.org/10.7554/eLife.107214.3.sa0

Abstract

Robust control of DNA replication is fundamental to bacterial proliferation. In Escherichia coli, replication initiation is thought to be regulated by oscillations in DnaA activity, driven by DnaA-chromosome interactions that differ among leading models. However, direct evidence linking these oscillations to replication initiation has been lacking, and existing models fail to explain the observed decoupling of replication initiation from dnaA expression. Here, we establish a direct link between DnaA activity and replication initiation by demonstrating robust oscillations in DnaA activity, which peak precisely at replication initiation across diverse growth conditions and genetic perturbations. Notably, these oscillations persist even when dnaA transcription remains constant, suggesting a regulatory mechanism that modulates DnaA activity independently of its expression. Additionally, we propose an extrusion model in which DNA-binding proteins sense biomass-DNA imbalance and extrude DnaA from the chromosome to trigger replication, overcoming limitations of existing models. Consistent with this model, perturbation of the nucleoid-associated protein H-NS modulates DnaA activity and replication timing, supporting its mechanistic validity.

Introduction

Bacterial proliferation requires precise coordination between DNA replication and biomass growth to maintain cellular homeostasis. While biomass accumulates exponentially, DNA synthesis proceeds in a stepwise manner, creating temporal imbalances that demand continuous correction throughout the cell cycle (Figure 1A and B). Failure to synchronize these processes risks catastrophic outcomes, such as growth defects, incomplete replication, unequal genetic segregation, and cell death (Boye et al., 1996; Khodursky et al., 2015; Lu et al., 1994; Mäkelä et al., 2024; Zaritsky and Pritchard, 1971). Central to this synchronization in Escherichia coli is the initiator protein DnaA, a DNA-binding protein that binds specifically to an asymmetric 9 bp DnaA-box, whose consensus sequence is TTWTNCACA (Hansen and Atlung, 2018; Speck et al., 1999). Although DnaA binding to the replication origin (oriC) triggers replication initiation (Dong et al., 2023; Kaguni, 2011; Ohbayashi et al., 2020; Ozaki and Katayama, 2012; Sekimizu et al., 1987), how DnaA activity—defined as the capacity to initiate replication—is dynamically regulated to coordinate DNA synthesis with cellular growth, particularly when DNA content and biomass diverge, remains a fundamental unresolved question in cell cycle biology.

Figure 1
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Coordination of biomass growth and DNA replication through replication initiation and DnaA activity oscillations.

(A) Schematic representation of the discrepancy between biomass accumulation and DNA replication. While biomass grows exponentially, DNA synthesis progresses linearly, necessitating replication initiation events to maintain coordination. (B) Mechanistic model for biomass-DNA coordination in bacteria. A molecular sensor detects deviations between cell mass and DNA content, transmitting this information to regulatory controllers that compensate by either increasing DNA replication or restricting biomass accumulation. (C) Illustration of cyclic DnaA activity oscillations aligning with replication initiation to ensure precise cell cycle control.

Two competing models have dominated the field. The titration model posits that DnaA activity is governed by the availability of free DnaA, which accumulates until it surpasses the buffering capacity of chromosomal DnaA-boxes (Christensen et al., 1999; Hansen et al., 1991). Here, replication initiation resets the system by increasing DNA content (and thus DnaA-boxes), titrating excess DnaA. In contrast, the switch model emphasizes the ATP/ADP-bound states of DnaA: DnaA-ATP, synthesized during biomass growth, triggers initiation, while hydrolysis to DnaA-ADP following replication resets the cycle (Berger and Wolde, 2022; Donachie and Blakely, 2003; Fu et al., 2023; Hansen and Atlung, 2018; Katayama et al., 2017). Crucially, the titration model depends on ongoing DnaA synthesis to reflect biomass accumulation, whereas the switch model relies on nucleotide-state transitions, independent of total DnaA levels. Despite these mechanistic differences, both models consider that replication initiation is regulated by oscillations in DnaA activity, driven by DnaA-chromosome interactions (Figure 1C). However, direct evidence linking these oscillations to replication initiation remains lacking. Moreover, neither model fully explains recent experimental observations: replication initiation persists for multiple generations even when dnaA transcription is completely inhibited (Knöppel et al., 2023). This discrepancy suggests an uncharacterized mechanism that senses biomass-DNA imbalance and directly modulates DnaA activity.

Here, using a synthetic reporter coupled with single-cell mRNA fluorescence in situ hybridization (FISH), we demonstrate that DnaA activity—governed by free DnaA concentration, DnaA-ATP/-ADP ratio, and orisome assembly competence—peaks precisely at initiation across diverse growth conditions and genetic backgrounds, even when dnaA transcription is held constant. DnaA activity oscillations persist independently of transcriptional feedback, pointing to a posttranslational regulatory mechanism. We propose an extrusion model in which nucleoid-associated proteins (NAPs) sense biomass-DNA imbalance and dynamically extrude DnaA from the chromosome, liberating it to activate oriC. This model not only explains the decoupling of replication timing from dnaA expression but also integrates disparate experimental findings. Perturbations of the NAP, H-NS, for instance, modulate DnaA activity and replication timing, directly supporting the model’s predictions. By unifying regulatory principles across transcription, protein activity, and chromosome dynamics, our findings advance the paradigm for bacterial replication control.

Results

Development of a DnaA activity reporter system

It has been known that changing DnaA expression levels affects DnaA activity and further changes initiation mass (Berger and Wolde, 2022; Løbner-Olesen et al., 1989; Zheng et al., 2020). To investigate this further, we engineered a dnaA-titratable strain, where dnaA transcription is controlled by an inducible Ptet promoter (Lutz and Bujard, 1997; Figure 2A). This system enabled precise modulation of dnaA mRNA levels (0.25–6 times the wild-type level) via anhydrotetracycline (aTc) induction (0.75–20 ng∙ml⁻¹) without altering growth rate or cell size (Figure 2B–D). As the DnaA expression level increases, DnaA activity reaches the initiation threshold earlier. Given that cell mass remained nearly unchanged, this earlier initiation led to an increase in population-averaged cellular oriC numbers (Figure 2E). Concurrently, the initiation mass was reduced by 50%, and the period from initiation to division (C+D) was increased by ~60% (Figure 2F). Notably, the observed relationship between DnaA expression and initiation mass deviated significantly from both the titration and switch models (Figure 2G; Appendix 1—note 1), suggesting unaccounted regulatory mechanisms.

Figure 2
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Construction and characterization of the dnaA-titratable strain.

(A) Schematic of the dnaA-titratable strain. A dnaA gene under the control of the Ptet promoter was inserted near oriC and regulated by a Ptet-tetR feedback loop integrated at the intS locus, enabling fine-tuned expression control. The native dnaA gene was replaced with a kanamycin resistance cassette (kanr). (B–F) Characterization of dnaA-titratable cells (circle) and wild-type MG1655 cells (triangle) grown in rich defined medium with glycerol (M6) under varying aTc concentrations. Measured parameters include: (B) dnaA mRNA levels; (C) growth rate; (D) population-averaged cellular mass; (E) population-averaged oriC numbers; (F) initiation mass (red, left axis); and the initiation to division period (C+D) (blue, right axis). The dnaA mRNA levels were normalized to that in wild-type cells. Cellular mass was determined by OD₆₀₀ divided by cell number concentration. oriC copy numbers were measured using a run-out assay, and initiation mass was calculated as the ratio of cellular mass to oriC numbers. Data represent means ± SD (n = 5 biological replicates). (G) Relationship between relative initiation mass and relative dnaA mRNA levels, compared with predictions from the initiation titration model (blue line) and the switch model (purple line). The relative dnaA mRNA levels in experiments are compared to relative DnaA expression rate αA in models. Experimental data are overlaid for validation.

Figure 2—source data 1

Source data for Figure 2 showing the physiological characteristics and model predictions of the dnaA-titratable cells under different dnaA expression levels.

https://cdn.elifesciences.org/articles/107214/elife-107214-fig2-data1-v1.xlsx

To dissect DnaA activity dynamics, we constructed a library of synthetic promoters (n=67) by replacing tetO operators in Ptet with DnaA-boxes (Figure 3—figure supplement 1). To assess the degree of DnaA activity-mediated repression, we measured the fold change in GFP expression driven by each synthetic promoter under low and high levels of dnaA induction (Figure 3A). Six synthetic promoters exhibited over eightfold DnaA-mediated repression, whereas the promoter lacking DnaA-boxes (Pcon) showed none, and the well-characterized native dnaA promoter (Pnative) (Braun et al., 1985; Saggioro et al., 2013; Speck et al., 1999) displayed approximately twofold repression (Figure 3B). Psyn66, which contains three strong and one weak DnaA-box, exhibited >8-fold repression under high DnaA induction (Figure 3B). Psyn66’s design enables responsiveness to both free DnaA levels (via strong boxes) and the DnaA-ATP/DnaA-ADP ratio (via differential binding to the weak box) (Grimwade and Leonard, 2021; Katayama et al., 2017; Speck et al., 1999). Dose-response assays confirmed that Psyn66 was progressively repressed across a 40-fold range of dnaA expression (Figure 3C), with no interference from SeqA-mediated sequestration (Figure 3D and E). These results validated Psyn66 as a sensitive and specific reporter for DnaA activity.

Figure 3 with 1 supplement see all
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Development of a DnaA activity reporter system.

(A) Schematic of promoter design and screening. Sixty-seven synthetic promoters were constructed by inserting various DnaA-boxes around the promoter core to drive gfp expression in a dnaA-titratable strain, where dnaA expression was regulated by aTc concentration. After pre-cultivation, GFP fluorescence per OD₆₀₀ was measured using a microplate reader in cells grown under low (0.5 ng∙ml–1) or high (50 ng∙ml–1) aTc concentrations to determine repression fold-change. (B) Repression fold-change of synthetic promoters. Pcon (a promoter lacking DnaA-boxes) and Pnative (the endogenous dnaA promoter) served as negative and positive controls, shown in gray and yellow, respectively. (C) Response curves of three promoters to varying dnaA expression levels, with their promoter architectures shown on the right. Promoter activity was assessed by relative gfp mRNA levels, normalized to the lowest dnaA expression condition. Schematic (D) and response curves (E) of Psyn66 and Pnative responses to SeqA in the seqA-titratable strain. Promoter activity was quantified from gfp transcript levels in seqA-titratable cells containing the Psyn66-GFP plasmid, normalized to the lowest seqA expression level. All cells were grown in rich defined medium supplemented with glycerol across different aTc concentrations. Data represent mean ± SD from 3 biological replicates (B, C, E).

Figure 3—source data 1

Source data for Figure 3 showing the response characteristics of the synthetic promoter under different expression levels of DnaA and SeqA proteins.

https://cdn.elifesciences.org/articles/107214/elife-107214-fig3-data1-v1.xlsx

Decoupling DnaA activity oscillations from dnaA transcription fluctuations

To investigate DnaA activity dynamics throughout the cell cycle, we linked Psyn66 activity to cell size, a surrogate for cellcycle progression, using single-cell mRNA FISH (Skinner et al., 2013) to quantify lacZ mRNA levels in MG1655Δlac cells harboring the Psyn66-lacZ plasmid (Figure 4A). A control strain harboring a promoter-less lacZ construct (Pneg) showed undetectable fluorescence signals (Figure 4A), confirming the specificity of lacZ mRNA detection. Quantitative analysis revealed that Psyn66-driven mRNA levels fluctuated ∼3-fold over the cell cycle, whereas the DnaA-unresponsive constitutive promoter (Pcon) exhibited stable expression (Figure 4B). However, fluctuations in mRNA concentration may not directly correspond to changes in DnaA activity due to global factors such as plasmid copy number and RNA polymerase concentration (Balakrishnan et al., 2022; Bintu et al., 2005a; Bintu et al., 2005b). To account for these variables, we used a Pcon-lacZ plasmid to estimate global effects on mRNA concentration and calculated DnaA activity by assuming that it is inversely proportional to ksyn66=[mZ](Psyn66)[mZ](Pcon). In wild-type cells (MG1655Δlac) harboring the synthetic reporter system (Figure 4C), fluctuations in DnaA activity, as denoted by ksyn66−1, displayed approximately a 3-fold variation across the cell cycle (Figure 4D), underscoring the substantial cell cycle-dependent oscillations in DnaA activity.

Figure 4
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DnaA activity oscillations decoupled from dnaA transcription fluctuations.

(A) Representative lacZ mRNA fluorescence in situ hybridization (FISH) images of MG1655 ∆lac cells transformed with lacZ expression plasmids driven by Psyn66 (DnaA-boxes around promoter core), Pcon (no DnaA-box around promoter core), or Pneg (mutated promoter core). Yellow outlines indicate cell boundaries identified from phase-contrast images. (B) Relative lacZ mRNA concentrations driven by Psyn66 (left) and Pcon (right) across different cell volumes. Relative concentrations were determined from volume-specific lacZ mRNA fluorescence intensities, normalized to the population average. Volume-binned data for Psyn66 ([mZ](Psyn66)) and Pcon ([mZ](Pcon)) are shown as open circles and were used to calculate ksyn66. (C) Schematic of a strain with autoregulated dnaA transcription carrying a DnaA activity reporter plasmid. Cell cycle-dependent fluctuations in relative DnaA activity (D) and relative dnaA mRNA concentrations (E) in cells from panel C, grown in rich defined medium supplemented with glucose. Relative DnaA activity (ksyn66−1), calculated from volume-binned data in panel B, was smoothed and plotted as a red curve (D). Relative dnaA mRNA concentrations were determined from volume-specific dnaA mRNA fluorescence intensities, normalized to the population average, with volume-binned data shown as open circles (E). Dashed lines indicate the cell volume at peak DnaA activity (D) and the minimum dnaA mRNA content (E). (F–H) Same as panels C–E, but for cells with aTc induced dnaA transcription, grown in rich defined medium supplemented with glycerol under 2 ng∙ml–1 aTc induction. More than 8000 cells were analyzed per growth condition, with at least 150 cells per bin; all error bars correspond to standard error of the mean (SEM).

Figure 4—source data 1

Source data for Figure 4 showing the changes in lacZ mRNA concentration driven by the reporter promoter with cell size, and the cell cycle-dependent variations in DnaA activity and dnaA mRNA concentration.

https://cdn.elifesciences.org/articles/107214/elife-107214-fig4-data1-v1.xlsx

We then examined whether the observed fluctuations in DnaA activity were linked to oscillations in dnaA transcription. Comparing the dynamics of dnaA mRNA and DnaA activity, we found that the peak in DnaA activity coincided with the trough in dnaA mRNA levels (dashed line in Figure 4D and E). To test whether dnaA transcription drives these oscillations, we eliminated transcriptional fluctuations using the dnaA-titratable strain. Despite constant dnaA mRNA levels (2 ng∙ml⁻¹ aTc induction), DnaA activity was retained with consistent oscillations (Figure 4F–H), demonstrating the decoupling of DnaA activity oscillations from transcriptional fluctuations. Our data suggest that posttranslational regulation, not transcriptional feedback, governs the DnaA activity.

DnaA activity oscillations are tightly coupled to replication timing

To explore the relationship between DnaA activity oscillations and DNA replication initiation, we examined cells grown under various nutrient conditions supporting different doubling times (30–66 min). Significant fluctuations in DnaA activity were observed across all conditions (Figure 5A). We also estimated the cell volume at replication initiation (Vi) based on population-averaged cell volume and oriC number (Bremer et al., 1979; Si et al., 2017; Zheng et al., 2016; Figure 5—figure supplement 1A–C), marking this on the DnaA activity profiles. In a representative birth-to-division cell cycle (Pountain et al., 2024; Figure 5—figure supplement 1D), DnaA activity peaks consistently coincided with Vi, indicating a close correlation between DnaA activity and replication initiation (Figure 5A).

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Tight correlation between DnaA activity oscillations and DNA replication initiation.

(A) Cell cycle-dependent DnaA activity oscillations in wild-type cells across various growth conditions. DnaA activity is represented by ksyn66−1. Volume-binned DnaA activity (red circles) was smoothed and plotted as a red curve. The representative birth-to-division cell cycle, defined as the cell volume doubling interval containing the majority of cells, is shaded in gray. The vertical line indicates the cell volume at replication initiation (Vi). (B) Cell cycle-dependent DnaA activity oscillations in dnaA-titratable cells grown in M6 medium with varying aTc concentrations. More than 8000 cells were analyzed per growth condition, with at least 150 cells per bin; error bars show the mean ± SEM. (C) Correlation between V∗ and Vi in wild-type (squares) and dnaA-titratable (circles) cells. V∗ represents the cell volume at the peak of DnaA activity within the representative birth-to-division cell cycle. The black line indicates equivalence between V∗ and Vi.

Figure 5—source data 1

Source data for Figure 5 showing DnaA activity oscillations and DNA replication initiation in wild-type cells cultivated under various growth media and in dnaA-titratable cells cultivated under various induction levels.

https://cdn.elifesciences.org/articles/107214/elife-107214-fig5-data1-v1.xlsx

Further genetic perturbations, such as titrating DnaA expression, shifted DnaA activity fluctuations, yet the relationship between V* (the cell volume at maximal DnaA activity in a representative birth-to-division cell cycle) and Vi remained locked (Figure 5B). To quantify this relationship, we compared Vi with V*, revealing a strictly proportional relationship through the origin (slope = 1.0, R²=0.98), indicating equivalence between V* and Vi (Figure 5C). These results confirm that DnaA activity oscillations are tightly correlated with replication initiation, independent of transcriptional or nutrient perturbations.

An extrusion model reconciles paradoxical initiation events

Very recently, Knöppel et al. reported that multiple rounds of initiation occurred even after DnaA synthesis is shut down (Knöppel et al., 2023). In their experiment, DnaA was supplied in excess before expression ceased (Knöppel et al., 2023), potentially allowing continued replication initiation due to residual DnaA. To address this issue, we shut down DnaA expression from its native level using the deactivated miniature Un1Cas12f1 (dUn1Cas12f1) system, guided by sgRNA to repress dnaA transcription (Figure 6A). This system effectively diminished dnaA transcription to an extremely low level, yet total oriC numbers increased 4-fold within 90 min, consistent with two rounds of replication initiation (Figure 6B). These findings demonstrate that halting DnaA synthesis does not immediately abolish replication initiation.

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An extrusion model explains DnaA shutdown dynamics.

(A) Genetic circuit of the deactivated CRISPR-Cas system for dnaA transcription shutdown. dnaA gene is targeted by a constitutively expressed sgRNA, while dUn1Cas12f1 expression is inhibited by TetR repressor. These transcription units are separated by terminators. The cassette was integrated into the chromosome near the oriC locus. DnaA shutdown is induced by the addition of aTc. (B) Time course of relative dnaA mRNA levels (red line, left axis) and total oriC number (green line, right axis) following the addition of 50 ng∙ml–1 aTc at time 0 (dashed line). dnaA mRNA levels were normalized to wild-type levels, and oriC numbers were normalized to their initial values. Error bars indicate mean ± SD (n = 3 biologically independent experiments). (C) Predicted increases in total oriC number during dnaA transcription shutdown based on three models: the titration model, switch model, and extrusion model. Shutdown was simulated by setting dnaA transcription to zero at time 0 (dashed line). (D) Schematic of the extrusion model. The model introduces extruder(s) as additional regulators of biomass-DNA coordination, complementing the role of DnaA (left). Increased binding of the extruder to DNA promotes the release of DnaA from DnaA-boxes (right). (E) Comparison of the relationship between relative initiation mass and relative dnaA mRNA levels from experimental data (Figure 2F) and predictions of the extrusion model.

Figure 6—source data 1

Source data for Figure 6 showing changes in DNA replication initiation after dnaA shutdown, as well as the extrusion model prediction regarding the relationship between initiation mass and DnaA expression level.

https://cdn.elifesciences.org/articles/107214/elife-107214-fig6-data1-v1.xlsx

Both the titration and switch models predict a close relationship between DnaA activity oscillations and replication initiation (Berger and Wolde, 2022). However, they fail to account for the multiple rounds of initiation observed after DnaA synthesis was shut down. According to the titration model, initiation should cease immediately once DnaA expression is shut down, as the reduction in DnaA concentration would prevent it from reaching the threshold required to trigger new rounds of replication. In contrast, the switch model predicts infinite initiations following the cessation of DnaA expression (Figure 6C). This is because the cessation of DnaA synthesis only reduces the production rate of DnaA-ATP and has no effect on the conversion rates between DnaA-ATP and DnaA-ADP. As a result, the ratio of DnaA-ATP to DnaA-ADP would temporarily decrease, causing a brief lag before initiation resumes indefinitely (Figure 6C).

The failure of both models prompts us to revisit the regulatory mechanisms of replication initiation. In the titration model, a substantial fraction of DnaA proteins binds to DnaA-boxes along the chromosome, raising the possibility that these bound proteins serve as a reservoir of free DnaA, sustaining replication initiation even after DnaA synthesis ceases. We thus considered the possibility of some extruder(s) alongside DnaA to sense biomass-DNA deviation and flush DnaA proteins away from the DnaA-boxes, thus releasing free DnaA to dynamically restore DnaA activity, even in the absence of ongoing DnaA synthesis (Figure 6D).

In this proposed ‘extrusion model’, the extruder is constitutively expressed, synthesized at a constant rate throughout the cell cycle, accumulating in proportion to biomass growth, enabling replication initiation when free DnaA concentrations exceed the critical threshold, as described in the titration model (Hansen and Atlung, 2018; Hansen et al., 1991; Figure 6D). This model guarantees the long-term coordination of biomass growth and oriC abundance, as well as stable oscillations in free DnaA and oriC concentrations (Figure 6—figure supplement 1A). Notably, the extrusion model accounts for the persistence of replication initiation and free DnaA oscillations over multiple cell cycles after DnaA synthesis is halted (Figure 6C; Figure 6—figure supplement 1B). Moreover, since the extruder mitigates the impact of perturbations on DnaA synthesis, the model predicts a quantitative relationship between initiation mass and DnaA expression levels that aligns with experimental observations (Figure 6E; Appendix 1—note 1). Furthermore, a stochastic implementation of the extrusion model shows the lack of correlation between successive replication initiation events (Figure 6—figure supplement 1C), as observed in recent studies (Si et al., 2019; Witz et al., 2019).

Perturbation of H-NS validates the extrusion mechanism

The extrusion model could be validated by changing the expression level of the extruder, which modulates DnaA activity by releasing DnaA from DnaA-boxes. Given their DNA binding properties, NAPs (Dillon and Dorman, 2010) are likely candidates for this extruder role. Therefore, we selected H-NS (Dorman, 2004), a major NAP, which is capable of promoting the release of DnaA from bound DnaA-boxes in vitro (Figure 7—figure supplement 1), to experimentally assess the validity of the extrusion model. To this end, we constructed a hns-titratable strain, incorporating a reporting plasmid to capture changes in DnaA activity across various H-NS expression levels (Figure 7A). Under steady-state cultivation, increasing aTc concentrations from 0 to 50 ng·ml–1 resulted in hns mRNA levels ranging from 0.25 to 5 times the wild-type levels, without affecting growth rate (Figure 7—figure supplement 2A and B). Within this range, DnaA activity increased by approximately 60% (Figure 7B), while the initiation mass decreased by 30% (Figure 7C; calculated with Figure 7—figure supplement 2C and D), in qualitative agreement with the predictions of the extrusion model (Figure 7—figure supplement 2E and F).

Figure 7 with 2 supplements see all
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Titration of hns expression modulates DnaA activity and replication initiation.

(A) Genetic circuit of the hns-titratable strain. Expression of hns is controlled by a Ptet-tetR negative feedback loop integrated at the attB site, with the native hns coding sequence replaced by a kanamycin resistance gene. The plasmid containing Psyn66-mcherry and Pcon-gfp expression cassettes was used to assess DnaA activity. DnaA activity (B) and initiation mass (C) were characterized in M6 medium with varying hns expression levels during steady-state cultivation. (D) mRNA levels of hns (green circles) and dnaA (blue squares) relative to wild-type levels, along with DnaA activity (orange rhombus), were measured during hns shift-up. hns shift-up was induced by the addition of 50 ng∙ml–1 aTc at time 0 (dashed line), followed by steady-state cultivation in M6 medium without aTc. (E) Dynamics of population-averaged oriC number (blue rhombus, left axis) and cellular mass (gray rhombus, right axis) during hns shift-up. Data represent mean ± SD from 3 biological replicates (B–E). (F) Cell cycle-dependent DnaA activity oscillations in hns-titratable cells cultivated in M1 and M6 media under varying aTc concentrations. Relative hns mRNA levels are indicated for each condition. More than 8000 cells were analyzed for each condition, with at least 150 cells per bin; error bars show the mean ± SEM. (G) Correlation between the volume at maximal DnaA activity (V∗) and the volume at replication initiation (Vi) for hns-titratable cells grown in M1 (green down-pointing triangle) and M6 (red up-pointing triangle) media with varying hns expression levels. Data for dnaA-titratable cells (gray circles) and wild-type dnaA-autoregulated cells (Figure 5C) are included for comparison.

Figure 7—source data 1

Source data for Figure 7 showing the effect of hns expression level on DnaA activity and the timing of DNA replication initiation.

https://cdn.elifesciences.org/articles/107214/elife-107214-fig7-data1-v1.xlsx

Since H-NS is a global transcriptional regulator (Hommais et al., 2001), we wonder whether its effect on DnaA activity was mediated through dnaA transcription. To test this, we measured DnaA activity and dnaA mRNA levels during an H-NS shift-up. Upon addition of 50 ng∙ml–1 aTc, hns levels increased more than 20-fold within a short period, followed by approximately 80% increase in DnaA activity, with no significant change in dnaA mRNA levels (Figure 7D). Additionally, cellular oriC content increased significantly within 30 min, while cell mass remained unchanged (Figure 7E). These results suggest that the rapid increase in DnaA activity and accelerated replication initiation induced by H-NS upregulation is not driven by changes in dnaA transcription.

Leveraging the hns-titratable strain, we re-examined the tight correlation between DnaA activity oscillations and replication initiation. In two different growth media with varying hns levels, the peak of DnaA activity fluctuations (V*) shifted in concert with the independently measured volume at replication initiation (Vi) (Figure 7F). Plotting V* against Vi revealed the same equivalency observed in wild-type and dnaA-titratable cells (Figure 7G). Collectively, these results support the notion that DnaA activity oscillations, modulated by extrusion, coordinate DNA replication and biomass growth.

Discussion

In this study, we proposed chromosome-driven DnaA activity oscillations as a posttranslational ‘rheostat’ coordinating DNA replication with biomass growth in E. coli. By developing a SeqA-independent synthetic promoter reporter (Psyn66) on a plasmid, we circumvented confounding factors inherent to native systems (Pountain et al., 2024), thereby achieving the direct quantification of DnaA activity dynamics. Crucially, these oscillations persist when dnaA transcription is fixed, operate independently of SeqA-mediated repression, and peak precisely at replication initiation across all tested growth conditions. This reveals an essential paradigm: bacteria bypass transcriptional control to synchronize genome duplication with growth through regulation of DnaA-chromosome interactions.

Very recently, Iuliani et al. also reported DnaA activity oscillations (Iuliani et al., 2024), which is a significant milestone, innovatively using a chromosomal promoter reporter system and microfluidics for single-cell DnaA activity observations. They revealed key relationships between DnaA activity, cell size, and division. Our work, however, differs in multiple ways. Methodologically, we engineered a dnaA-titratable strain and synthetic promoters like Psyn66, enabling precise dnaA expression control and cleaner DnaA activity reporting. Our mRNA FISH approach uniquely compares DnaA activity and transcription at the single-cell level. Replication initiation volume scales proportionally with peak DnaA activity volume with a slope of 1.0 (R²=0.98, Figure 7G), indicating predictive correspondence rather than absolute coincidence. While population-level Vi estimation cannot resolve single-cell stochasticity, the consistent V∗:Vi relationship across 20 conditions suggests DnaA activity thresholds predict initiation timing within physiological error margins. Regarding research questions, while Iuliani et al. focused on DnaA-cell size-division relationships, we addressed the decoupling of replication initiation from dnaA expression with our novel extrusion model.

Recently, an integrated ‘titration-switch’ model has been proposed to improve replication cycle stability across different growth rates (Berger and Wolde, 2022; Fu et al., 2023). Although this integrated model predicts a relationship between initiation mass and dnaA mRNA levels that aligns closely with steady-state experimental data (Appendix 1—figure 1A), it fails to account for the additional replication initiation observed after dnaA shutdown (Appendix 1—figure 1B). By incorporating an extruder into this model, we successfully reproduced the additional replication initiation (Appendix 1—figure 1C), thus validating the extrusion concept and aligning with experimental observations. These findings highlight the critical role of extrusion in refining our mechanistic understanding of biomass-DNA coordination.

Mechanistically, we propose that NAPs sense biomass-DNA imbalances and extrude DnaA from chromosomal sites, converting physical displacement into biochemical activation, a spatial control strategy mirroring eukaryotic chromatin regulation. H-NS emerges as a prime extruder candidate given its DNA-condensing ability (Dame et al., 2000; Zimmerman, 2006) and replication-timing effects (Figure 7). This functional multiplicity likely enhances robustness, allowing cells to maintain replication homeostasis despite fluctuating environments, an evolutionary advantage paralleling eukaryotic checkpoint system.

These findings refine bacterial cell cycle control as a dynamic interplay between physical chromosome remodeling and metabolic sensing. By tethering DnaA activation to biomass accumulation through extrusion, cells achieve an important capability: noise suppression via spatial filtering of transcriptional fluctuations. Such mechanistic parsimony, using chromosome structure to both store genetic information and regulate replication timing, may represent a potential organizational strategy. Indeed, parallels to eukaryotic systems are striking: DnaA extrusion resembles ORC licensing through chromatin accessibility (Fragkos et al., 2015), while biomass-DNA coupling evokes yeast size-control checkpoints (Xie et al., 2022).

While H-NS perturbation supports extrusion mechanism, future work should identify the full extruder interactome and elucidate how metabolic signals modulate their activity. Our synthetic reporter system provides a valuable tool for dissecting the spatial regulation of DNA-binding proteins, with translational potential in antimicrobial development (targeting pathogen-specific extruders) and synthetic biology (engineering tunable replication circuits). More broadly, this work establishes physical chromosome remodeling as a central coordinator of cellular homeostasis, a concept bridging prokaryotic and eukaryotic cell cycle paradigms.

Materials and methods

Plasmid and strain construction

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Strains used in this study were derived from E. coli MG1655 (Liu et al., 2011). Information for all strains and plasmids is listed in Appendix 2—table 1 and Appendix 2—table 2, respectively.

Plasmids for reporting DnaA activity

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Plasmids expressing GFP, mCherry, or LacZ under synthetic promoters were constructed based on the pPT plasmid backbone (Zong et al., 2017). The lacZ amplified from the MG1655 genome and mCherry gene amplified from pMD19-hupA-mcherry plasmid were assembled into the BspQI/BsrGI-digested pPT backbone via the One Step Cloning Kit (Vazyme, C112), to generate the pPT-lacZ and pPT-RFP plasmids, respectively. Synthetic promoters, generated by annealing the primer pairs listed in Appendix 2—table 3, were ligated directly into the BsaI-digested pPT, pPT-RFP, or pPT-lacZ plasmid as required. The native dnaA promoter (Pnative) amplified from the MG1655 genome was digested with BsaI and ligated into the BsaI-digested pPT plasmid. The Pcon-gfp cassette amplified from the Pcon-GFP plasmid was assembled into the SpeI-digested Psyn66-RFP plasmid to generate the Psny66-Pcon-FPs plasmid. Primer information for amplifying DNA fragments is detailed in Appendix 2—table 4.

Plasmids for strain construction

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For constructing the plasmid pMD19-Rhns, pMD19-RseqA, and pMD19-RdnaA, the hns, seqA, and dnaA genes were amplified from MG1655 genome, digested, and ligated into the same enzyme-digested pMD19-tetR plasmid (Zheng et al., 2016). For constructing the plasmid p15A-RdnaA, the T3-Ptet-tetR-dnaA cassette, kanr gene, and p15A-T1 fragment were amplified from plasmid pMD19-RdnaA, pEcCas (Li et al., 2021), and PZA31-Ptet-M2-GFP (Liu et al., 2019), respectively, and then assembled via MultiS One Step Cloning Kit (Vazyme, C113). The plasmid P_CRidnaA1 was obtained by ligating the annealed primer pairs to the BsaI-digested CPP00458 plasmid for the dnaA targeting. The chloramphenicol resistance gene Cmr amplified from the PZA31-Ptet-M2-GFP plasmid replaced the kanr gene in the pEcCas plasmid via λRed recombination method, resulting in the CmPcas plasmid. Primer information is detailed in Appendix 2—table 4.

Strain construction

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The seqA- and hns-titratable strains were constructed as previously described (Zheng et al., 2016). Briefly, after transforming the pSIM5 plasmid into MG1655 cells, the Ptet-tetR-driven seqA or hns cassette was inserted at the attB site on the genome, and then the native gene was replaced with the kanr gene amplified from plkml plasmid.

For the construction of the dnaA-titratable strain, we first inserted the Ptet-tetR cassette into the intS site via CRISPR/Cas9-λRed genome editing system (Jiang et al., 2015) by targeting 20-nucleotide sequence (tcttcctgcagaccagatcc). Then, applying the λRed recombination systems in the CmPcas plasmid, the bla:Ptet-dnaA cassette amplified from the pMD19-RdnaA plasmid via overlap PCR was inserted between the yidA and yidX genes, and the native Pnative-dnaA was replaced with the T3-Kan-T1 cassette amplified from the p15A-RdnaA plasmid.

In order to delete the native lacZ gene in the genome of the RdnaA1, Rhns1, and MG1655 cells, the homologous recombination fragment amplified from the genome of the CL1 strain was used to knock out the native lac operon via CRISPR/Cas9-λRed editing system (Jiang et al., 2015) by targeting 20-nucleotide sequence (cttccggctcgtatgttgtg).

For the construction of DnaA shutdown strain (CRidnaA1), the PJ23119-sgRNAdnaA:PJ23100-tetR:Ptet-dUn1Cas12f1 cassette was amplified from the P_CRidnaA1 plasmid and flanked with homologous sequences to the asnA and viaA gene. This cassette was then inserted into the MG1655 genome using the CRISPR/Cas9-λRed editing system, targeting the 19-nucleotide sequence (aagccgcctgctcagacgc). The CPP00458 plasmid, kindly provided by the Xiongfei Fu lab, contains the Un1Cas12f1 variant, which carries mutations D143R, T147R, D326A, and K330R that abolish its DNA-cleaving activity. However, it retains the ability to bind DNA in an sgRNA-guided manner, thereby inhibiting the expression of the target gene.

All primers for the construction of DNA fragments are detailed in Appendix 2—table 4.

Growth conditions

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Cells for strain construction were grown in Luria-Bertani (LB) medium with appropriate antibiotics: ampicillin (100 µg·ml–1), kanamycin (50 µg·ml–1), spectinomycin (50 µg·ml–1), chloramphenicol (25 µg·ml–1), based on plasmid presence. For titratable strains, 10 ng·ml⁻¹ aTc was added during strain construction and seed culture preparation. Seed cultures were initiated by inoculating three to six individual colonies from an LB agar plate into 14 ml round-bottom test tubes containing 2 ml of the desired medium for overnight growth.

For physiological phenotypic characterization, all cells were cultivated at 37°C with shaking at 150 rpm in a water bath shaker. The dnaA-, seqA-, and hns-titratable cells were characterized under varying aTc concentrations in rich defined medium supplemented with glycerol (M6). hns-titratable cells were also characterized in rich defined medium supplemented with glucose (M1). When strains contained reporter plasmid, 25 µg·ml⁻¹ chloramphenicol was added. The autoregulated-dnaA strain (MGCL1) with a reporter plasmid was tested in seven different media: M1 (MOPS+EZ+AUCG+Glucose), M2 (MOPS+EZ+Glucose), M3 (MOPS+EZ+ AUCG+Gluconate), M6 (MOPS+EZ+ AUCG+Glycerol), M12 (MOPS+EZ+Glycerol), M13 (MOPS+CAA+Glycerol), and M18 (MOPS+Glucose). Detailed medium compositions were provided previously (Zheng et al., 2020).

Steady-state growth was established as previously described (Zheng et al., 2020) by culturing cells for ~10 generations in exponential growth phase with ~15-fold dilution at OD600 ~0.2, using the same medium supplemented with antibiotic or aTc as required. OD600 measurements were taken at consecutive time points using a spectrometer (Genesys 10s, Thermo Fisher Scientific), and the growth rate (or doubling time) λ was calculated by fitting the data to an exponential growth curve. Samples for determination of population-averaged cell mass (m-) and cellular origin number (o-), quantification of mRNA levels, and mRNA FISH were collected when OD600 reached ~0.2.

Measurements of population-averaged cellular mass (m¯), cellular oriC number (o¯), the initiation to division period (C+D), and initiation mass (mi)

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The population-averaged cell mass (m¯), cellular oriC number (o¯), and initiation mass (mi) were calculated as described previously (Zheng et al., 2020). Briefly, m¯ was determined by dividing the OD600 by the cell concentration (OD600 ml per 109 cells). For cell counting, samples were collected in precooled cell count buffer (0.9% NaCl with 0.12% formaldehyde, filtered with a 0.22 µm filter) and stained with 1 µg·ml⁻¹ DAPI in the same buffer. Cell number concentration was measured by counting the DAPI-stained particles using flow cytometry equipped with 405 nm laser (CytoFLEX, Beckman Coulter Life Sciences). The o¯ was calculated based on the classical run-out experiments (Zheng et al., 2020). In brief, cell suspensions were treated with 500 µg·ml⁻¹ rifampicin and 30 µg·ml⁻¹ cephalexin to inhibit new initiation and cell division, respectively, and maintained for two to three mass doublings to complete ongoing replication. Cells were fixed with 70% precooled absolute ethanol and treated with stain buffer (20 mM Tris-HCl, pH 8.0, 130 mM NaCl, 10 ng·ml⁻¹ DAPI). Using the same flow cytometry, we measured the cellular origin distribution based on DAPI signaling, from which o¯ was calculated. C+D was calculated based on the equation: C+D = ln(o¯)/λ. mi was calculated based on the equation: mi=m¯o¯×1ln2 (Bremer et al., 1979; Si et al., 2017; Zheng et al., 2016).

Screening of the synthetic promoter library

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After transforming the reporter plasmid into dnaA-titratable cells, the seed culture was cultivated in M6 medium with 10 ng·ml–1 aTc, then diluted 1:180 in fresh medium containing either 0.5 or 50 ng·ml–1 aTc and cultivated at 37°C and 800 r.p.m. in a digital thermo-shaker (AOSHENG) using flat-bottom 96-well plates (Corning). Following approximately 4 hr of pre-cultivation, the cultures were diluted 1:180 in fresh medium with the same aTc concentration. The OD600 and GFP fluorescence intensity were continuously monitored during cell growth using a Synergy H1 microplate reader (BioTek). The nonfluorescent strain (MG1655) cultured in 180 µl of M6 medium for each experiment was used to subtract the background fluorescence. For OD600 between 0.1 and 0.2, the repression fold change was calculated by dividing the mean fluorescence intensity per OD600 of the cells grown with 0.5 ng·ml–1 aTc induction by the corresponding intensity for cells grown with 50 ng·ml–1 aTc induction.

Quantitative real-time PCR

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Quantitative real-time PCR was performed using a protocol similar to previously described (Zheng et al., 2016). Briefly, RNA was stabilized using Bacteria Protect Reagent (QIAGEN) and extracted using RNeasy Mini Kit (QIAGEN). 500 ng RNA was reverse-transcribed (TAKARA), and the resulting cDNA samples were diluted 1:25 with PCR-grade water. Quantitative real-time PCR was carried out on a qTower3 (Analytikjena) or CFX Connect system (Bio-Rad), using the following program: 30 s at 95°C, followed by 40 cycles of denaturation (5 s at 95°C), annealing, and elongation (30 s at 58°C). Primer sequences were listed in Appendix 2—table 4. The rpoA gene was used as the reference gene.

lacZ and dnaA mRNA FISH

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FISH for lacZ or dnaA mRNA was performed using a method similar to that previously described (Skinner et al., 2013). Briefly, antisense DNA oligo probes were designed against lacZ or dnaA and synthesized with TAMRA labeling at the 3’ end. The sequences of probes targeting lacZ or dnaA are either as previously described (Skinner et al., 2013) or listed in Appendix 2—table 5. 27 ml steady-state growth culture (OD600~0.2) was fixed by directly adding 3 ml of 37% (vol/vol) formaldehyde, followed by incubation for 30 min at room temperature with gentle mixing using a nutator. The fixed cells were collected by centrifugation at 600 × g for 8 min and washed twice with 1 ml RNase-free PBS. The cell pellet was resuspended in 300 µl DEPC water, and 700 µl precooled absolute ethanol was added to permeabilize the cells for 1 hr at room temperature in the nutator. After permeabilization, the cells were divided into two equal parts for characterization of lacZ or dnaA mRNA as needed. Hybridization was performed as described previously (Skinner et al., 2013), with the stringency of the hybridization and wash solutions adjusted by changing the concentration of formamide, 30% for dnaA probes and 40% for lacZ probes. After hybridization, the cells were washed with the appropriate wash solution and prepared for imaging.

Cell imaging and cell size parameters measurement

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For imaging, 3 µl of cell suspension was pipetted onto the center of a 24×60 mm2 coverslip. A thin 1.5% agarose pad (prepared with 1x PBS) was then placed on top of the cell suspension droplet. Fluorescent and phase images were acquired using an inverted microscope (IX-83, Olympus) equipped with a 100x oil objective (Olympus), an automated xy-stage (ASI, MS2000), and a sCMOS camera (Prime BSI, photometrics). Fluorescence excitation was performed as previously described (Xia et al., 2022). Solid-state lasers (Coherent OBIS: 405-100 LX, 561-100 LS) and Semrock emission bandpass filters (445/40 nm for DAPI and 598/25 nm for RFP) were used for excitation and collecting fluorescent signals.

Images were aligned with the phase-contrast channel by selecting at least six pairs of common control points and applying a geometric transformation using the MATLAB imwarp function. This allowed for adjustment of the cell masks in the RFP or DAPI channel. A customized ImageJ-based image-processing package, MicrobeJ (v5.13l1) plug-in for Fiji (Ducret et al., 2016), was used to contour cells and calculate cell size parameters and fluorescence intensity, including the mean cell width, cell length, and cell area from phase-contrast images. The cell volume was calculated using a cylinder-plus-two-hemispheres geometry. The total RFP intensity per cell volume, corrected for background subtraction, was then used to estimate the mRNA concentration of lacZ or dnaA.

Characterization of cell cycle-dependent fluctuations in DnaA activity

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Data filtering was first performed based on the intensity of the DAPI fluorescence signal. To eliminate non-cell regions resulting from incorrect segmentation of the phase-contrast images, only DAPI-positive cells were included in the analysis. To further minimize the potential impact of variability in fixation, permeabilization, and wash efficiencies, which could affect the TAMRA fluorescence signal (representing the mRNA FISH signal), only cells with mean DAPI signals within the 95% confidence intervals were included in the subsequent analysis.

The concentration of the lacZ mRNA (mZ) can be expressed as mZ=J/δZ, where δZ is the degradation rate and J is the mRNA synthesis flux. J is in turn given by the promoter on-rate (k), the RNAP concentration ([RNAP]), and the concentration of the lacZ gene ([GZ]). Assuming that the factors δZ, [RNAP], and [GZ] are the same between the two strains with Psyn66 and Pcon driving lacZ, then the ratio of the mRNA concentration obtained at each point of the cell cycle gives the ratio of the promoter on-rates, kPsyn66 and kPcon. Since a constitutive promoter is nonresponsive to DnaA activity, kcon is independent of cell cycle and we can obtain DnaA activity-regulated promoter activity as ksyn66=[mZ](Psyn66)[mZ](Pcon), then the DnaA activity is denoted as ksyn66-1.

The concentration of the lacZ mRNA was binned based on cell volume, with a bin size of 0.1 µm3, and was used to determine mZ(Psyn66) and mZ(Pcon). The relation between [mZ](Pcon) and cell volume was smoothed using the MATLAB smooth function, and from this, ksyn66 was deduced.

Based on the DnaA activity oscillations, the cell volume where DnaA activity reaches maximum (V*) was calculated during a representative birth-to-division cell cycle. The cell volume distribution was used to calculate the number of cells in each volume-doubling range. The range containing the highest cells was considered the representative birth-to-division cell cycle and was shaded in gray (Figure 5—figure supplement 1D). From this, V* was obtained.

Calculation of cell volume at the time of replication initiation (Vi)

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Cell volume at the time of replication initiation (Vi) was calculated based on population-averaged cell volume (V¯) and the population-averaged cellular oriC number (o¯). The equation used for this calculation is as follows:

Vi=vi×NoriCini.=V¯o¯×1ln2×2⌊log2o¯⌋

where vi is the initiation volume by definition, i.e., cell volume per oriC at the time of replication initiation (Bremer et al., 1979; Si et al., 2017; Zheng et al., 2016). V¯ was obtained by analyzing the phase-contrast images, o¯ was obtained by run-out experiments (Zheng et al., 2020). NoriCini. is the cellular oriC number at the time of replication initiation. ⌊log2⁡o¯⌋ denotes the floor function of log2⁡o¯.

DnaA shutdown experiment

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To shut down DnaA expression, steady-state growth of the CRidnaA1 cells was first established after ~10 generations of exponential growth in M1 medium. At OD600 between 0.15 and 0.2, 24 ml of cell culture was transferred to 12 ml of pre-warmed M1 medium containing 150 ng·ml⁻¹ aTc to induce dUn1Cas12f1 expression at time 0. Under the guidance of sgRNAdnaA, the transcription of the dnaA gene was inhibited. Samples were collected at various time points to determine population-averaged cellular mass (o¯), cellular oriC number (o¯), and to quantify mRNA levels. When the OD600 reached around 0.2, the cultures were diluted about 2-fold with pre-warmed M1 medium containing 50 ng·ml⁻¹ aTc to maintain adequate nutrition. The steady-state growing MG1655 cells in M1 medium were also collected for quantification of wild-type dnaA mRNA levels. Three independent experiments were conducted.

H-NS shift-up experiment

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The DnaA activity reporter plasmid Psyn66-Pcon-FPs was transformed into the hns-titratable strain (Rhns2), and cells were cultivated overnight in M6 medium containing 10 ng·ml⁻¹ aTc and 25 µg·ml⁻¹ chloramphenicol for seed culture. Seed cultures were washed and grown in the same medium without aTc for ~10 generations of exponential growth. At an OD600 of ~0.2, 40 ml of cell culture was transferred to 20 ml of pre-warmed same medium containing 150 ng·ml⁻¹ aTc to induce H-NS expression at time 0. Samples were collected at time points –2, 1, 3, 5, 7, 10, 15, 20, 25, and 30 min for the determination of population-averaged cellular mass (m¯), cellular oriC number (o¯), and quantification of mRNA levels. Steady-state growing MG1655 in M6 medium was also collected for hns mRNA quantification. Three independent experiments were conducted.

Electrophoretic mobility shift assay

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The genes encoding dnaA and hns from the MG1655 genome were individually amplified and cloned into the expression vector pET-28a using primers listed in Appendix 2—table 4. E. coli BL21 (DE3) carrying pET-28a-DnaA or pET-28a-H-NS was used for overproduction of His6-DnaA or His6-H-NS. For each protein purification, a 1 l culture was induced by the addition of 1 mM IPTG at OD600=0.4. After 1.5 hr of incubation, cells were harvested by centrifugation (5000 rpm for 20 min at 4°C). The pellet was resuspended in 50 ml of buffer A (50 mM Tris-HCl, pH 7.0, 500 mM NaCl, 0.5 mM PMSF, 5% glycerol). Cell resuspension was lysed on ice for 30 min (3 s on, 7 s off) using a sonicator at 4°C, and cell debris was removed by centrifugation (14,000 rpm for 60 min at 4°C). Subsequently, the protein was purified from the soluble supernatant by Ni2+-affinity chromatography (HisTrap HP Columns; GE Healthcare) with a ÄKTA Pure Protein Purification System. Proteins were collected using gradient elution with buffer B (20 mM Tris-HCl, pH 7.5, 500 mM NaCl, 400 mM imidazole, 5% glycerol). Eluted proteins were further purified and concentrated by ultrafiltration using Amicon Ultra 10K (for H-NS) and 30K (for DnaA) filters (Millipore). Finally, after overnight dialysis (0.025 µm Membrane Filter; MF-Millipore) at 4°C, the protein was stored in buffer C (45 mM HEPES-KOH, pH 7.6, 600 mM potassium glutamate, 1 mM DTT, 10 mM magnesium acetate, 0.5 mM EDTA, 20% glycerol). Judged by SDS-PAGE with Coomassie Brilliant Blue staining, the purity of each protein was >90%.

For the experiments where H-NS acts on the datA bound by DnaA, the purified DnaA, datA, and ATP were first incubated in buffer D (20 mM Tris-HCl, pH 7.5, 150 mM potassium glutamate, 10 mM magnesium acetate, 8 mM DTT) at 20°C for 10 min. Then, the H-NS was added to buffer D (15 µl) which contained DnaA-datA complexes and incubated for an additional 10 min at 20°C. The final complexes were separated by electrophoresis in 2% (wt/vol) agarose gel at 80 V for 70 min in Tris-Borate buffer, followed by staining with SYBR Safe (Invitrogen).

Titration model

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In the titration model, we posit that the cell mass grows exponentially according to the differential equation dVdt=λV, where V represents the volume of the cell and λ=1.54h−1 (corresponding to doubling time of 27 min) is the growth rate. Concurrently, the number of total DnaA protein (A) increases at a rate proportional to the cell’s mass, described by dAdt=αAV, with αA=300h−1μm−3 denoting the synthesis rate per unit volume. The chromosome’s structure is abstracted as a vector representing the copy numbers of uniformly distributed sites along its length, and the number of DnaA-boxes (Ab) is quantified by summing the boxes number at their respective locations. In this model, 300 DnaA-boxes are evenly distributed on the chromosome while an additional 100 DnaA-boxes are located near the oriC. This arrangement approximates a head-weighted distribution of DnaA-boxes and helps prevent over-initiation (Hansen et al., 1991). Binding of DnaA proteins to these DnaA-boxes allows us to calculate the concentration of free DnaA as Af=A-Ab/V, under condition of A-Ab≥0. DNA replication commences once the concentration of free DnaA exceeds a critical threshold [Afc]=10μm−3: [Af]>[Afc]. 10 min sequestration time was set after replication initiation to mimic the sequestration time (Campbell and Kleckner, 1990).

To explore the dynamics of the oriC number following the cessation of dnaA expression, we can set the synthesis rate to be zero αA=0. To simulate the dependence of initiation mass on dnaA expression, αA varies from 60 h-1μm-3 to 3840 h-1μm-3.

Extrusion model

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The extrusion model was formulated following the titration model, with an additional extruder introduced as a protein with synthesis rate αH=180h−1μm−3 and dynamics dH/dt=αHV. The additional extruder competes the DnaA-boxes with DnaA proteins, with higher affinity to DnaA-boxes. So that it can displace DnaA from the DnaA-boxes, leading to a revised calculation for free DnaA concentration as Af=A-(Ab-H)/V, under condition of Ab-H≥0 and A-(Ab-H)≥0. To get stochastic modeling for the adder phenomenon, we introduced white noises on cell mass growth rate and the DnaA synthesis rate at each replication cycle: λ=λ0+ηλ0, αA=α0+ηα0, where λ0=0.66h−1,α0=300h−1μm−3, and η=0.1 representing white noise. This extrusion model exhibits consistent performance under various parameter ranges.

DnaA-ATP/DnaA-ADP switch model

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The switch model was simulated following the idea of the LDDR model proposed by Berger and Wolde, 2022. The formulations of the DnaA-ATP fraction dynamics are:

dfdt=(αl[D]TV+αd1[D]TNd1+αd1[D]TNd2)1−fKD[D]T+1−f−(βdatA[D]T+βrida[D]T)NofKD[D]T+f+αA[D]Tλ(1−f)

In this equation [D]T=400μm−3 is the concentration of total DnaA proteins in a cell, and αl=750h−1 is the activation rate of DnaA-ATP by lipid; αd1=100h−1, αd2=643h−1 are the activation rates of DnaA-ATP by the DARS1 and DARS2 sites; βdatA=600h−1,βrida=500h−1 are the deactivation rates of DnaA-ATP by datA and RIDA; KD=50μm−3 the dissociation constant of DnaA activation and deactivation; Nd1,Nd2,No are copy numbers of DARS1, DARS2, and oriC sites extracted dynamically from the DNA copy number vector; αA=300h−1μm−3 is the DnaA expression level. The DNA replication initiation was determined when f reaches a threshold fC=0.75 (Berger and Wolde, 2022). To simulate the dnaA shutdown, we simply deleted the term of dnaA synthesis (λ(1−f)).

Titration-switch model and titration-switch-extrusion model

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In the integrated model combining titration and switch mechanisms, replication initiation is triggered when the concentration of free DnaA-ATP [AATPf] exceeds a threshold [AATPfc]. The concentration of free DnaA-ATP is titrated by the DnaA-boxes on the chromosome as in the titration model, with [AATPf]=(AATP−Ab)/V, under condition of (AATP−Ab)≥0. The parameters related to the titration dynamics remain consistent with those described in earlier sections of the titration model.

The dynamics of total DnaA-ATP (AATP) is governed by both expression and conversion processes as described in Berger and Wolde, 2022. According to this framework, the lipid and DARS systems facilitate the conversion of DnaA-ADP to DnaA-ATP at constant rates, while the datA site and RIDA systems also play a role in converting DnaA-ATP to DnaA-ADP, again at fixed rates. Additionally, the synthesis of new DnaA proteins provides another source of DnaA-ATP.

To be specific, DNA replication initiation was set when the concentration of free DnaA-ATP, [AfATP]=(AATP−Ab)/V exceeds a threshold [Afc]. The dynamics of total DnaA number in a cell A followed dAdt=ϕP0λV1+([AfATP]/KDP)n, and the dynamics of total DnaA-ATP number AATP followed:

dAATPdt=(αlV+αd1Nd1+αd2Nd2)[AADP]KD+[AADP]−(βdatA+βrida)No[AfATP]KD+[AfATP]+ϕP0λV1+([AfATP]/KDP)n

where the three terms on the right-hand side represent activation and deactivation of DnaA-ATP and new synthesis of DnaA-ATP. In these equations, [AADP]=(A−AATP)V is the concentration of DnaA-ADP; [DT]=400μm−3 is the total DnaA concentration; KDP=400μm−3 is the dissociation constant of DnaA promoter; ϕp0 is the DnaA protein ratio relative to all proteins, which is proportional to αA and ranges from 0.002% to 0.1%; n=5 is the cooperativity of dnaA expression. The same activation/deactivation rates and dissociation constants of DnaA (αl,αd1,αd2,βdatA,βrida,KD) are used as in the switch model; Nd1,Nd2,No are copy numbers of DARS1, DARS2, and oriC sites extracted dynamically from the DNA copy number vector. To get a good fit of the initiation mass-dnaA expression relation, high threshold of free DnaA-ATP concentration was applied, [AATPfc]=200μm−3.

To simulate the titration-switch-extrusion model, we incorporated the extruder as described in the extrusion model, where its amount was given by H=αHV. The concentration of free DnaA-ATP was then calculated as [AATPf]=(AATP−(Ab−H))/V, under condition of (Ab−H)≥0 and (AATP−(Ab−H))≥0. To explore the best fit of continuous DNA replication rounds following dnaA shutdown, αH=550h−1μm−3 is used.

All parameters are summarized in Appendix 1—table 1.

Appendix 1

Supplementary information for model simulation, model parameters, and model interpretation

Appendix 1—figure 1
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Predictions of the titration-switch model and titration-switch-extrusion model.

(A) Comparison of the titration-switch model predictions and experimental data for the relation between relative initiation mass and dnaA mRNA levels. The relative initiation mass was calculated as mass/oriC averaged over more than 100 cycles after steady DNA replication initiation was established, while the relative dnaA mRNA was achieved by setting various dnaA expression rates αA. Dynamics of total oriC number during DnaA shutdown predicted by titration-switch model (B) and titration-switch-extrusion model (C), normalized to the value at DnaA shutdown (dash line).

Appendix 1—table 1
Parameters used in models.
ParameterDescriptionValuesSource
Titration model and extrusion model
λBiomass growth rate1.54h−1This study (if not specifically defined)
αADnaA synthesis rate300h−1μm−3Fitted in this study
[Afc]Threshold of DnaA concentration10μm−3Fitted in this study
αHExtruder synthesis rate180h−1μm−3Fitted in this study
ηRelative noise level of λ and αA in stochastic model0.1Fitted in this study
Switch model
[D]TConcentration of total DnaA protein400μm−3Berger and Wolde, 2022;
LDDR model
αlActivation rate of DnaA-ATP by lipid750h−1
αd1Activation rate of DnaA-ATP by the DARS1 site100h−1
αd2Activation rate of DnaA-ATP by the DARS2 site643h−1
βdatADeactivation rate of DnaA-ATP by data site600h−1
βridaDeactivation rate of DnaA-ATP by RIDA system500h−1
KDDissociation constant of DnaA activation and deactivation50μm−3
KDPDissociation constant of DnaA promoter300μm−3
fCThreshold of DnaA-ATP fraction for DNA replication initiation0.75
Titration switch model and Titration-switch-extrusion model
KDPDissociation constant of DnaA promoter400μm−3Berger and Wolde, 2022; SI table switch-titration model
nCooperativity of dnaA expression5
αHExtruder synthesis rate550h−1μm−3Fitted in this study
[AATPfc]Threshold of DnaA-ATP concentration for DNA replication initiation200μm−3Fitted in this study

Appendix 1—note 1

To understand the initiation mass dependency on dnaA expression level αA (Figure 2G), we can solve the dynamics of DnaA amounts A(t) and cell volume V(t), yielding the solutions: A(t)=αAV0λeλt+const. The boundary conditions corresponding to successive rounds of DNA replication initiation at times t=0 and t=tI are: A(t=0)=AI⋅O2+Ab2,V(t=0)=VI⋅O2, and A(t=tI)=AI⋅O+Ab,V(t=tI)=VI⋅O, where AI is the threshold of DnaA number per oriC and VI is the cell volume per oriC at the time of initiation (tI).

Applying these conditions, we derive

VI=AI−AbαAλ

showing that the initiation mass is inversely proportional to the DnaA expression level αA, assuming AI and Ab are constant. This inverse relationship predicted by the titration model implies a much sharper dependence of initiation mass on DnaA expression than the exponential dependence observed experimentally.

Following the same analysis in the titration model, the extrusion model has the same DnaA amounts and volume dynamics as in the titration model with similar H-NS dynamics H(t)=αHV0λeλt+const. The condition of DNA replication initiation sets the boundary condition:A(t=0)=AI⋅O2+Ab2+H(t=0),V(t=0)=VI⋅O2 and A(t=tI)=AI⋅O+Ab+H(t=tI),V(t=tI)=VI⋅O.

Solving these equations, we get:

VI=AI-AbαA+αHλ

The addition of extruder shifts the inverse dependency of initiation mass-dnaA expression curve to the left, which results in a flatter curve. To achieve a better fit to the experiment data, αH=1500h−1μm−3 were used in Figure 6E.

Appendix 2

Supplementary information for strains, plasmids, and oligonucleotides used in this study

Appendix 2—table 1
Strains used in this study.
StrainRelevant genetic marker(s) or featuresSource or reference
MG1655E. coli K12(AMB1655)Liu et al., 2011
CL1MG1655 ΔcheZ, ΔlacLiu et al., 2011
MGCL1MG1655 ΔlacThis study
RdnaA1MG1655 PdnaA-dnaA::PkanR-kanR yidA::(bla:Ptet-dnaA)::yidX intS::Ptet-tetRThis study
RdnaA2RdnaA1 ΔlacThis study
RseqA1MG1655 seqA::kan attB::(bla:Ptet-tetR-seqA)This study
Rhns1MG1655 hns::kan attB::(bla:Ptet-tetR-hns)This study
Rhns2Rhns1 ΔlacThis study
CRidnaA1MG1655 asnA::(PJ23119-sgRNAdnaA:PJ23100-tetR:Ptet -dUn1Cas12f1)::viaAThis study
Appendix 2—table 2
Plasmids used in this study.
PlasmidRelevant genotypeSource or reference
pSIM5Cmr, repA101(Ts) ori, λRedZheng et al., 2016
plkmlAmpr, pUC ori, loxp-kan-loxpZheng et al., 2016
pMD19-tetRAmpr, pUC ori, bla:Ptet-tetRZheng et al., 2016
pMD19-hupA-mcherryAmpr, pUC ori, bla:Ptet-tetR-hupA-mcherryThis study
pMD19-RhnsAmpr, pUC ori, bla:Ptet-tetR-hnsThis study
pMD19-RseqAAmpr, pUC ori, bla:Ptet-tetR-seqAThis study
pMD19-RdnaAAmpr, pUC ori, bla:Ptet-tetR-dnaAThis study
p15A-RdnaAKanr, p15A ori, Ptet-tetR-dnaAThis study
pTargetFaadAr, pMB1 ori, sgRNAJiang et al., 2015
pEcCasKanr, pSC101 ori, sacB ParaB-λRed Pcas-cas9Li et al., 2021
pZA31-Ptet-M2-GFPCmr, p15A ori, Ptet-gfpLiu et al., 2019
CmPcasCmr, pSC101 ori, sacB ParaB-λRed Pcas-cas9This study
CPP00458aadAr, pSC101 ori, PJ23119-sgRNA-T-PJ23100-tetR-T-Ptet-dUn1Cas12f1Gift from Xiongfei Fu lab
P_CRidnaA1aadAr, pSC101 ori, PJ23119-sgRNAdnaA-T-PJ23100-tetR-T-Ptet-dUn1Cas12f1This study
pPTCmr, pSC101 ori, BsaI-lacZa-BasI-riboJ-sfgfpZong et al., 2017
pPT-RFPCmr, pSC101 ori, BsaI-lacZa-BasI-riboJ-mcherryThis study
pPT-lacZCmr, pSC101 ori, BsaI-lacZa-BasI-riboJ-lacZThis study
Psyn66-GFPCmr, pSC101 ori, Psyn66-riboJ-sfgfpThis study
Pcon-GFPCmr, pSC101 ori, Pcon-riboJ-sfgfpThis study
Psyn66-RFPCmr, pSC101 ori, Psyn66-riboJ-mcherryThis study
Pnative-GFPCmr, pSC101 ori, Pnative-riboJ-sfgfpThis study
Psyn66-lacZCmr, pSC101 ori, Psyn66-riboJ-lacZThis study
Pcon-lacZCmr, pSC101 ori, Pcon-riboJ-lacZThis study
Psny66-Pcon-FPsCmr, pSC101 ori, Pcon-riboJ-sfGFP, Psyn66-riboJ-mcherryThis study
pET-28a-DnaAKanr, pUC ori f1 ori, PlacI-lacI, PT7/lacO-dnaA-6*hisThis study
pET-28a-H-NSKanr, pUC ori f1 ori, PlacI-lacI, PT7/lacO-hns-6*hisThis study
Appendix 2—table 3
Primer pairs for synthetic reporter.
PrimersSequenceUse
DP420cctggtagatagattgacaagagttatccacagtaggatactgagcacaPsyn1
DP421agcttgtgctcagtatcctactgtggataactcttgtcaatctatctac
DP422cctggtagatagattgacacttgttatacacagggcgatactgagcacaPsyn2
DP423agcttgtgctcagtatcgccctgtgtataacaagtgtcaatctatctac
DP424cctggtagatagattgacaatacttttccacaggtagatactgagcacaPsyn3
DP425agcttgtgctcagtatctacctgtggaaaagtattgtcaatctatctac
DP426cctggtagatagattgacaccgatcattcacagttagatactgagcacaPsyn4
DP427agcttgtgctcagtatctaactgtgaatgatcggtgtcaatctatctac
DP428cctggtagatagattgacacttgtgtggataagggcgatactgagcacaPsyn5
DP429agcttgtgctcagtatcgcccttatccacacaagtgtcaatctatctac
DP430cctggtagatagattgacattatccacatagttcccgatactgagcacaPsyn6
DP431agcttgtgctcagtatcgggaactatgtggataatgtcaatctatctac
DP432cctggtagatagattgacacccctgcgatttttcccgatactgagcacaPsyn7
DP433agcttgtgctcagtatcgggaaaaatcgcaggggtgtcaatctatctac
DP434cctgttatccacattgacacccctgcgatagttcccgatactgagcacaPsyn8
DP435agcttgtgctcagtatcgggaactatcgcaggggtgtcaatgtggataa
DP436cctggtagatagattgacacccctgcgatagttcccgatactttatccacPsyn9
DP437agctgtggataaagtatcgggaactatcgcaggggtgtcaatctatctac
DP438cctgacagagttatccacagtagatagattgacaccgatcattcacagttagatactgagcacaPsyn10
DP439agcttgtgctcagtatctaactgtgaatgatcggtgtcaatctatctactgtggataactctgt
DP440cctggaggggttatacacaactcaaagattgacaccgatcattcacagttagatactgagcacaPsyn11
DP441agcttgtgctcagtatctaactgtgaatgatcggtgtcaatctttgagttgtgtataacccctc
DP442cctgccatactgtggaaaaggtagaagattgacaccgatcattcacagttagatactgagcacaPsyn12
DP443agcttgtgctcagtatctaactgtgaatgatcggtgtcaatcttctaccttttccacagtatgg
DP444cctgttatccacattgacacccctgcgatagttcccgatactttatccacPsyn13
DP445agctgtggataaagtatcgggaactatcgcaggggtgtcaatgtggataa
DP446cctgttatccacattgacacccctgcgatttttcccgatactgagcacaPsyn14
DP447agcttgtgctcagtatcgggaaaaatcgcaggggtgtcaatgtggataa
DP448cctgttatccacattgacattatccacatagttcccgatactgagcacaPsyn15
DP449agcttgtgctcagtatcgggaactatgtggataatgtcaatgtggataa
DP450cctggtagatagattgacattatccacatagttcccgatactttatccacPsyn16
DP451agctgtggataaagtatcgggaactatgtggataatgtcaatctatctac
DP452cctggtagatagattgacacccctgcgatttttcccgatactttatccacPsyn17
DP453agctgtggataaagtatcgggaaaaatcgcaggggtgtcaatctatctac
DP454cctggatagattgacatgtggataagtgtggatgatactgagcacaPsyn18
DP455agcttgtgctcagtatcatccacacttatccacatgtcaatctatc
DP456cctggatagattgacattatccacagttttcccgatactgagcacaPsyn19
DP457agcttgtgctcagtatcgggaaaactgtggataatgtcaatctatc
DP458cctggatagattgacattatccacagctttccagatactgagcacaPsyn20
DP459agcttgtgctcagtatctggaaagctgtggataatgtcaatctatc
DP460cctgttatccacattgacacccctgcgatagttcccgatactgtggataaPsyn21
DP461agctttatccacagtatcgggaactatcgcaggggtgtcaatgtggataa
DP462cctgtgtggataattgacacccctgcgatagttcccgatactttatccacPsyn22
DP463agctgtggataaagtatcgggaactatcgcaggggtgtcaattatccaca
DP464cctgtgtggataattgacacccctgcgatagttcccgatactgtggataaPsyn23
DP465agctttatccacagtatcgggaactatcgcaggggtgtcaattatccaca
DP466cctgtgtggataattgacacccctgcgatttttcccgatactgagcacaPsyn24
DP467agcttgtgctcagtatcgggaaaaatcgcaggggtgtcaattatccaca
DP468cctgttatccacattgacatgtggataatagttcccgatactgagcacaPsyn25
DP469agcttgtgctcagtatcgggaactattatccacatgtcaatgtggataa
DP470cctgtgtggataattgacattatccacatagttcccgatactgagcacaPsyn26
DP471agcttgtgctcagtatcgggaactatgtggataatgtcaattatccaca
DP472cctgtgtggataattgacatgtggataatagttcccgatactgagcacaPsyn27
DP473agcttgtgctcagtatcgggaactattatccacatgtcaattatccaca
DP474cctggtagatagattgacattatccacatagttcccgatactgtggataaPsyn28
DP475agctttatccacagtatcgggaactatgtggataatgtcaatctatctac
DP476cctggtagatagattgacatgtggataatagttcccgatactttatccacPsyn29
DP477agctgtggataaagtatcgggaactattatccacatgtcaatctatctac
DP478cctggtagatagattgacatgtggataatagttcccgatactgtggataaPsyn30
DP479agctttatccacagtatcgggaactattatccacatgtcaatctatctac
DP480cctggtagatagattgacacccctgcgatttttcccgatactgtggataaPsyn31
DP481agctttatccacagtatcgggaaaaatcgcaggggtgtcaatctatctac
DP482cctggtagatagattgacatgtggataatttttcccgatactgagcacaPsyn32
DP483agcttgtgctcagtatcgggaaaaattatccacatgtcaatctatctac
DP484cctgtgtggataattgacattatccacatagttcccgatactttatccacPsyn33
DP485agctgtggataaagtatcgggaactatgtggataatgtcaattatccaca
DP486cctgttatccacattgacattatccacatagttcccgatactgtggataaPsyn34
DP487agctttatccacagtatcgggaactatgtggataatgtcaatgtggataa
DP488cctgtgtggataattgacattatccacatagttcccgatactgtggataaPsyn35
DP489agctttatccacagtatcgggaactatgtggataatgtcaattatccaca
DP490cctgttatccacattgacatgtggataatagttcccgatactgtggataaPsyn36
DP491agctttatccacagtatcgggaactattatccacatgtcaatgtggataa
DP492cctgtgtggataattgacatgtggataatagttcccgatactttatccacPsyn37
DP493agctgtggataaagtatcgggaactattatccacatgtcaattatccaca
DP494cctgtgtggataattgacatgtggataatagttcccgatactgtggataaPsyn38
DP495agctttatccacagtatcgggaactattatccacatgtcaattatccaca
DP496cctgttatccacattgacattatccacatagttcccgatactttatccacPsyn39
DP497agctgtggataaagtatcgggaactatgtggataatgtcaatgtggataa
DP498cctgttatccacattgacacccctgcgatttttcccgatactttatccacPsyn40
DP499agctgtggataaagtatcgggaaaaatcgcaggggtgtcaatgtggataa
DP500cctgttatccacattgacattatccacagttttcccgatactgagcacaPsyn41
DP501agcttgtgctcagtatcgggaaaactgtggataatgtcaatgtggataa
DP502cctgttatccacattgacacccctttatccacacccgatactttatccacPsyn42
DP503agctgtggataaagtatcgggtgtggataaaggggtgtcaatgtggataa
DP504cctgttatccacattgacacccctgcgttatccacagatactttatccacPsyn43
DP505agctgtggataaagtatctgtggataacgcaggggtgtcaatgtggataa
DP506cctgttatccacattgacatgtggataatagttcccgatactttatccacPsyn44
DP507agctgtggataaagtatcgggaactattatccacatgtcaatgtggataa
DP508cctgttatccacattgacaccccttgtggataacccgatactttatccacPsyn45
DP509agctgtggataaagtatcgggttatccacaaggggtgtcaatgtggataa
DP510cctgttatccacattgacacccctgcgtgtggataagatactttatccacPsyn46
DP511agctgtggataaagtatcttatccacacgcaggggtgtcaatgtggataa
DP512cctgttatccacattgacattttcccgatagttcccgatactttatccacPsyn47
DP513agctgtggataaagtatcgggaactatcgggaaaatgtcaatgtggataa
DP514cctgttatccacattgacatcgggaaaatagttcccgatactttatccacPsyn48
DP515agctgtggataaagtatcgggaactattttcccgatgtcaatgtggataa
DP516cctgttatccacattgacacccctttttcccgacccgatactttatccacPsyn49
DP517agctgtggataaagtatcgggtcgggaaaaaggggtgtcaatgtggataa
DP518cctgttatccacattgacaccccttcgggaaaacccgatactttatccacPsyn50
DP519agctgtggataaagtatcgggttttcccgaaggggtgtcaatgtggataa
DP520cctgttatccacattgacacccctgcgtcgggaaaagatactttatccacPsyn51
DP521agctgtggataaagtatcttttcccgacgcaggggtgtcaatgtggataa
DP522cctgtgtggataagatagattgacatgtggataagtgtggatgatactgagcacaPsyn52
DP523agcttgtgctcagtatcatccacacttatccacatgtcaatctatcttatccaca
DP524cctgttatccacagatagattgacattatccacagctttccagatactgagcacaPsyn53
DP525agcttgtgctcagtatctggaaagctgtggataatgtcaatctatctgtggataa
DP526cctgttatccacagatagattgacattatccacagttttcccgatactgagcacaPsyn54
DP527agcttgtgctcagtatcgggaaaactgtggataatgtcaatctatctgtggataa
DP528cctgttatccacagatagattgacatgtggataagtgtggatgatactgagcacaPsyn55
DP529agcttgtgctcagtatcatccacacttatccacatgtcaatctatctgtggataa
DP530cctgtgtggataattgacatgtggataagtgtggatgatactgagcacaPsyn56
DP531agcttgtgctcagtatcatccacacttatccacatgtcaattatccaca
DP532cctgttgacattatccacagttttcccgatactttatccacPsyn57
DP533agctgtggataaagtatcgggaaaactgtggataatgtcaa
DP534cctgttatccacattgacatgtggataatttttcccgatactttatccacPsyn58
DP535agctgtggataaagtatcgggaaaaattatccacatgtcaatgtggataa
DP536cctgttatccacattgacattatccacatttttcccgatactgtggataaPsyn59
DP537agctttatccacagtatcgggaaaaatgtggataatgtcaatgtggataa
DP538cctgtgtggataattgacattatccacatttttcccgatactttatccacPsyn60
DP539agctgtggataaagtatcgggaaaaatgtggataatgtcaattatccaca
DP540cctgtgtggataattgacattatccacatttttcccgatactgtggataaPsyn61
DP541agctttatccacagtatcgggaaaaatgtggataatgtcaattatccaca
DP542cctgtgtggataattgacatgtggataatttttcccgatactttatccacPsyn62
DP543agctgtggataaagtatcgggaaaaattatccacatgtcaattatccaca
DP544cctgttatccacattgacatgtggataatttttcccgatactgtggataaPsyn63
DP545agctttatccacagtatcgggaaaaattatccacatgtcaatgtggataa
DP546cctgtgtggataattgacatgtggataatttttcccgatactgtggataaPsyn64
DP547agctttatccacagtatcgggaaaaattatccacatgtcaattatccaca
DP548cctgtgtggataattgacatgtggataagtgtggatgatacttgtggataPsyn65
DP549agcttatccacaagtatcatccacacttatccacatgtcaattatccaca
DP550cctgttatccacattgacattatccacagttttcccgatactttatccacPsyn66
DP551agctgtggataaagtatcgggaaaactgtggataatgtcaatgtggataa
DP552cctgtgtggataattgacatgtggataagtgtggatgatactttatccacPsyn67
DP553agctgtggataaagtatcatccacacttatccacatgtcaattatccaca
DP554cctgttgacacccctgcgatagttcccgatactgagcacaPcon
DP555agcttgtgctcagtatcgggaactatcgcaggggtgtcaa
DP556cctgttatccacacccgggttatccacagttttcccgagcccttatccacPneg
DP557agctgtggataagggctcgggaaaactgtggataacccgggtgtggataa
Appendix 2—table 4
Oligonucleotides for the construction of strains and plasmids and qPCR.
PrimersSequenceUse
DJP001gaaagaggagaaatactagatgaccatgattacggattcacAmplifying lacZ gene from MG1655 genome
DJP002ttgatgcctggcttatcattatttttgacaccagaccaact
DJP003gaaagaggagaaatactagatggtttccaagggcgaggAmplifying mCherry gene from pMD19-hupA-mcherry
DJP004ttgatgcctggcttatcattatttgtagagctcatccatgc
DJP005ccacaaggtctccagctgatcaagatcctgcaaaacgatAmplifying native dnaA promoter (Pnative) from MG1655 genome
DJP006ccacatggtctcccctgccaatttttgtctatggtcat
DJP007ctgttttcttgcaagattactagtccatccagtgctcatttgtacagttcatccataccAmplifying Pcon-gfp cassette from Pcon-GFP plasmid
DJP008ccttagtgactcctgcagtcctgggtgttgacacccctgcgat
DJP009cgccatatgtcactttcgctttggcaAmplify dnaA gene from MG1655 genome for the construction of pET-28a-DnaA plasmid
DJP010cgcaagcttttacgatgacaatgttctga
DJP011cgccatatgagcgaagcacttaaaatAmplify hns gene from MG1655 genome for the construction of pET-28a-H-NS plasmid
DJP012cgcaagcttttattgcttgatcaggaaatc
DJP013cgatctgcagaaagaggagaaatactaggtgtcactttcgctttggcAmplifying dnaA gene from MG1655 genome
DJP014gtgagccggatccttacgatgacaatgttctgatt
DJP015atgcctgcagtcacacaggaaacctactagatgaaaacgattgaagttgatgatgAmplifying seqA gene from MG1655 genome
DJP016gtcaggatccttagatagttccgcaaaccttct
DJP017cagtaagcttaaagaggagaaatactagatgagcgaagcacttaaaattcAmplifying hns gene from MG1655 genome
DJP018gtacggatccttattgcttgatcaggaaatcgtcg
DJP019tggatcgcgaagaaaggcAmplifying T3-Ptet-tetR-dnaA cassette from pMD19-RdnaA plasmid
DJP020tcgatatcaaccatggctgcggcaaaatcgctcgagt
DJP021cgttttatttgatgggtcgacctgcagggaaagccacgtAmplifying kanr gene from the plasmid pEcCas
DJP022ttcttcgcgatccatgctagcagcaaccaattaaccaattc
DJP023gcagccatggttgatatcgagctcgcttggaAmplifying p15A-T1 fragment from the PZA31-Ptet-M2-GFP plasmid
DJP024cacatgaagtcgacccatcaaataaaacgaaaggctc
DJP025tcaacccactgcagcaaccaattaaccaattctgattacgccccgccctgccaAmplifying Cmr gene from pZA31-Ptet-M2-GFP plasmid for the construction of CmPcas
DJP026tgtctgcttacataaacagtaatacaaggggtgttatggagaaaaaaatcactgg
DJP027ttaaaggtattaaaaacaactttttgtctttttaccttcccgtttcgctccaggaaacagctatgaccatgAmplifying the T0-Amp-T1-Ptet-tetR-hns or T0-Amp-T1-Ptet -tetR-seqA cassette, and then assembling to attB locus via λRed recombination system
DJP028cacaggttgctccgggctatgaaatagaaaaatgaatccgttgaagcctgtgtaaaacgacggccagt
DJP029tctattattacctcaacaaaccaccccaatataagtttgagattactacaatgattgaacaagatggattgcacAmplifying kanr gene from plkml plasmid for the deletion of native hns gene
DJP030aaaaaatcccgccgctggcgggattttaagcaagtgcaatctacaaaagatcagaagaactcgtcaagaagg
DJP031ggcctgcacgattgtggattgccattgctttgtcctttgtctgcaacgttctagtgaacctcttcgagggAmplifying kanr gene from plkml plasmid for the deletion of native seqA gene
DJP032catatactcctggcgacttgtattcagctaagacactgcactggattaaggccgatcatattcaataaccc
DJP033gagtcatgcacagattcgtaT0-Ptet-tetR-T3 cassette was amplified from plasmid pMD19-tetR with primers DJP035/DJP036, the intS upstream and downstream sequences were amplified from MG1655 genome with primers DJP033/DJP034, and DJP037/DJP038, respectively. Three fragments were ligated via overlap PCR with primers DJP033/DJP038.
DJP034gatctgaagcgaaccatga
DJP035tcatggttcgcttcagatcaggttgtgtgttcctcttcattc
DJP036cctcatagccgatttgtttgaaggaaacagctatgaccatga
DJP037caaacaaatcggctatgagg
DJP038agtgtataagggtgttcagc
DJP039gtacgttagatcgtagacgcttggcgataaagaacgccacttcgcccggccgtgagcatttaggatccggctcaccttcaAmplifying T3-kanr-T1 cassette from p15A-RdnaA plasmid to replace the native Pnative-dnaA on the genome
DJP040gatcgattaagccaatttttgtctatggtcattaaattttccaatatgcggcgtaaatctagggcggcggatttg
DJP041ttaggcaccccaggctttacThe dnaA-T3 and T0-Amp-T1-Ptet fragments were amplified from the pMD19-RdnaA plasmid using primers DJP041/DJP042 and DJP043/DJP044, respectively. These two fragments were then combined via overlap PCR using primers DJP045/DJP046 to generate a homologous recombination fragment for inserting T0-Amp -T1-Ptet-dnaA-T3 between the yidA and yidX genes.
DJP042cagtgatagagatactgagcacataagcttaaagaggagaaagactaggtgtcactttcgc
DJP043gtgctcagtatctctatcactgatagggatgtcaatctctatcactgatagggagggactcgag
DJP044aaaacgacggccagtgaa
DJP045gatggcgtggcgtttgctattgagaagtatgtgctgaattaatctgtgggcggtcatcttcggctactgtct
DJP046accgctgcaatttctggttgtatatgcagtaaaccaataatcagtaagcgcaggaaacagctatgaccatg
DJP047saacttcgagtggagtccgccgtgInserting to the CPP00458 backbone to generate P_CRidnaA1 plasmid to shut down dnaA expression
DJP048cgagcacggcggactccactcgaa
DJP049aaacgatgaagaccgtctttctccAmplifying homologous sequences to asnA gene
DJP050ttcttagacgtcaggtggcattattacagcagagaagggacg
DJP051tgccacctgacgtctaagaaAmplifying PJ23119-sgRNAdnaA:PJ23100-tetR:Ptet-dUn1Cas12f1 cassette from P_CRidnaA1 plasmid
DJP052tctagattactgcgcagatggcgacgataatgacagcagccaactcagcttc
DJP053gccatctgcgcagtaatctagatcgcatcccggtatcaaagcAmplifying homologous sequences to viaA gene
DJP054atgaacagtgtgcgaaagcg
DJP055ccacaaggcatcgaacaagcAssembling the upper three fragments through overlap PCR
DJP056gtggaacccggtactggaag
DJP057cttctttggtgctgtactcaRT-qPCR primer for rpoA
DJP058tggttgatatcgagcaagtg
DJP059cccgattgcaggatgagttRT-qPCR primer for dnaA
DJP060tacccaatcgaggacaaaac
DJP061cgttatccggaccatatgaaRT-qPCR primer for gfp
DJP062cttcaaatttcacttccgca
DJP063aagttaaactgcgtggtactRT-qPCR primer for mcherry
DJP064acaggtttcttggctttgta
DJP065tatgttgaaattttccgccgRT-qPCR primer for seqA
DJP066attcatccgaaagcagaagt
DJP067gtattgacccgaacgaactgRT-qPCR primer for hns
DJP068agtccaggttttagtttcgc
Appendix 2—table 5
43 3'-TAMRA probes spanning the whole coding sequence of the dnaA gene.
gccaaagcgaaagtgacacgagaacgataggtcggttctgcttcctgagatcgttctttacacgaagtcgatggtgatcg
tgcaatcgggcaagacactgacgtgtgtttgacgtttacgggcgttgaaggtgtggaaaatgaccagtttttcctgcaat
aattctgtggctggtaactccgccagttggttagatttacatctgttgattaccttccagccgtcttctgaatattgtcg
caatgggcgtatccacatacggaacaacgggttataggcaatagcgatccgaggtgagaacgcgactttgatcttgtagt
agcgtgttatcgctcagttcatgcagcaggtgagttttaccaacgccgttgatctctttcatcgacgcttggaaaggaga
cccaatcgaggacaaaacggtaaaccactttggcattcggcaaccgaagcgggatttcaatgtggttagtcagctctttc
ttattaaggtacttgtcccgtgaacaaagcgctcggagtgtttttcatcaggatcgccaccaccaaacgcatcgccaatc
gcagaaactggttagcagtctttgcagggctttaaccatgacgaatgtcgttttcgtcggttacggcaggcatgaagcac
cgacttcaaaacgcagctgtttaaactcttcgatcgcgttggcgataaagaacgccacttctcttcacgcaactgctcga
gtagaaggcgcagcacgttgatctacggaacggtagtagcgtacgttagatcgtagacgcaatcttctttgatatcgtgg
ggacgttatcccaacctgaggaatatcgtcgatcagcagtggtaaagttggcattggcaa

Data availability

The cellular parameters obtained after mRNA FISH treatment have been deposited on Dryad (DOI: https://doi.org/10.5061/dryad.bvq83bkp4). Source data files have been provided for Figures 2-7. Simulation data can be generated using the custom-made code and the parameter sets provided. The code is publicly available on GitHub (copy archived at Bai, 2025).

The following data sets were generated
    1. Li D
    2. Zheng H
    3. Bai Y
    4. Zhang Z
    5. Cheng H
    6. Huang X
    7. Wei T
    8. Chang M
    9. Zaritsky A
    10. Hwa T
    11. Liu C
    (2025) Dryad Digital Repository
    Extrusion-modulated DnaA activity oscillations coordinate DNA replication with biomass growth.
    https://doi.org/10.5061/dryad.bvq83bkp4

References

Article and author information

Author details

  1. Dengjin Li

    1. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    2. University of Chinese Academy of Sciences, Beijing, China
    Contribution
    Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Hai Zheng and Yang Bai
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0001-4148-5476

  2. Hai Zheng

    1. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    2. University of Chinese Academy of Sciences, Beijing, China
    Contribution
    Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Dengjin Li and Yang Bai
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7561-2269

  3. Yang Bai

    1. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    2. University of Chinese Academy of Sciences, Beijing, China
    Contribution
    Data curation, Software, Formal analysis, Funding acquisition, Validation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Dengjin Li and Hai Zheng
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9976-2686

  4. Zheng Zhang

    Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Software, Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7824-211X

  5. Hao Cheng

    Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Formal analysis, Validation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared

  6. Xiongliang Huang

    1. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    2. University of Chinese Academy of Sciences, Beijing, China
    Contribution
    Formal analysis, Validation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  7. Ting Wei

    Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    Contribution
    Formal analysis, Validation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2926-5849

  8. Matthew Chang

    NUS Synthetic Biology for Clinical and Technological Innovation and Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore; National Centre for Engineering Biology, Singapore, Singapore
    Contribution
    Funding acquisition, Validation, Writing – review and editing
    Competing interests
    No competing interests declared

  9. Arieh Zaritsky

    Faculty of Natural Sciences, Ben-Gurion University of the Negev, Beersheba, Israel
    Contribution
    Funding acquisition, Validation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared

  10. Terence Hwa

    Department of Physics & Department of Molecular Biology, University of California at San Diego, San Diego, United States
    Contribution
    Conceptualization, Formal analysis, Validation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1837-6842

  11. Chenli Liu

    1. Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China
    2. University of Chinese Academy of Sciences, Beijing, China
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    cl.liu@siat.ac.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3029-7207

Funding

National Natural Science Foundation of China (32025022)

  • Chenli Liu

National Natural Science Foundation of China (32230062)

  • Chenli Liu

Chinese Academy of Sciences (Strategic Priority Research Program of the Chinese Academy of Sciences XDB0480000)

  • Chenli Liu

National Natural Science Foundation of China (32170042)

  • Hai Zheng

National Key Research and Development Program of China (2024YFA0916403)

  • Hai Zheng

Chinese Academy of Sciences (Youth Innovation Promotion Association of the Chinese Academy of Sciences 2022369)

  • Hai Zheng

National Key Research and Development Program of China (2021YFA0910703)

  • Yang Bai

National University of Singapore (NUHSRO/2024/064/NUSMed/05/ SynCTI2.0)

  • Matthew Chang

United States-Israel Binational Science Foundation (2017004)

  • Arieh Zaritsky

National Centre for Engineering Biology (NRF-MSG-2023-0003)

  • Matthew Chang

Israel Science Foundation (Joint NSFC-ISF Research Grant 32061143021)

  • Chenli Liu

National Natural Science Foundation of China (Joint NSFC-ISF Research Grant 32061143021)

  • Chenli Liu

Israel Science Foundation (Israel Science Foundation-NSFC joint research program 3320/20)

  • Arieh Zaritsky

National Natural Science Foundation of China (Israel Science Foundation-NSFC joint research program 3320/20)

  • Arieh Zaritsky

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank numerous colleagues for discussions. We sincerely thank Dr. Aiguo Xia for his help with microscopy, and Dr. Sheng Yang for providing valuable suggestions on CRISPR-Cas9 system. Special thanks to Drs. Pan Chu and Yeqing Zong for kindly gifting us the CPP00458 and pPT plasmids. This research was financially supported by the National Natural Science Foundation of China (32025022, 32230062), Strategic Priority Research Program of the Chinese Academy of Sciences (XDB0480000), Joint NSFC-ISF Research Grant (32061143021) to CL, US-Israel Binational Science Foundation (2017004) and Israel Science Foundation-NSFC Joint Research Program (3320/20) to AZ National Natural Science Foundation of China (32170042), National Key R&D Program of China (2024YFA0916403), Youth Innovation Promotion Association CAS (number 2022369) to HZ, National Key R&D Program of China (2021YFA0910703) to YB, National Centre for Engineering Biology (NRF-MSG-2023–0003), and National University of Singapore (NUHSRO/2024/064/NUSMed/05/SynCTI2.0) to MWC.

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© 2025, Li, Zheng, Bai et al.

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

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  1. Dengjin Li
  2. Hai Zheng
  3. Yang Bai
  4. Zheng Zhang
  5. Hao Cheng
  6. Xiongliang Huang
  7. Ting Wei
  8. Matthew Chang
  9. Arieh Zaritsky
  10. Terence Hwa
  11. Chenli Liu
(2025)
Extrusion-modulated DnaA activity oscillations coordinate DNA replication with biomass growth
eLife 14:RP107214.
https://doi.org/10.7554/eLife.107214.3

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