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  • Molecular Systems Biology: 14 (11)

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Article

A conserved mechanism drives partition complex assembly on bacterial chromosomes and plasmids

Roxanne E Debaugny, Aurore Sanchez, Jérôme Rech, Delphine Labourdette, Jérôme Dorignac, Frédéric Geniet, John Palmeri, Andrea Parmeggiani, François Boudsocq, Véronique Anton Leberre, Jean‐Charles Walter, View ORCID ProfileJean‐Yves Bouet
DOI 10.15252/msb.20188516 | Published online 16.11.2018
Molecular Systems Biology (2018) 14, e8516
Roxanne E Debaugny
Laboratoire de Microbiologie et Génétique Moléculaires, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université de Toulouse, UPS, Toulouse, France
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Aurore Sanchez
Laboratoire de Microbiologie et Génétique Moléculaires, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université de Toulouse, UPS, Toulouse, France
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Jérôme Rech
Laboratoire de Microbiologie et Génétique Moléculaires, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université de Toulouse, UPS, Toulouse, France
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Delphine Labourdette
LISBP, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France
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Jérôme Dorignac
Laboratoire Charles Coulomb, CNRS‐Université Montpellier, Montpellier, France
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Frédéric Geniet
Laboratoire Charles Coulomb, CNRS‐Université Montpellier, Montpellier, France
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John Palmeri
Laboratoire Charles Coulomb, CNRS‐Université Montpellier, Montpellier, France
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Andrea Parmeggiani
Laboratoire Charles Coulomb, CNRS‐Université Montpellier, Montpellier, FranceDynamique des Interactions Membranaires Normales et Pathologiques, CNRS‐Université Montpellier, Montpellier, France
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François Boudsocq
Laboratoire de Microbiologie et Génétique Moléculaires, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université de Toulouse, UPS, Toulouse, France
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Véronique Anton Leberre
LISBP, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France
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Jean‐Charles Walter
Laboratoire Charles Coulomb, CNRS‐Université Montpellier, Montpellier, France
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Jean‐Yves Bouet
Laboratoire de Microbiologie et Génétique Moléculaires, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université de Toulouse, UPS, Toulouse, France
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Author Affiliations

  1. Roxanne E Debaugny1,
  2. Aurore Sanchez1,5,
  3. Jérôme Rech1,
  4. Delphine Labourdette2,
  5. Jérôme Dorignac3,
  6. Frédéric Geniet3,
  7. John Palmeri3,
  8. Andrea Parmeggiani3,4,
  9. François Boudsocq1,
  10. Véronique Anton Leberre2,
  11. Jean‐Charles Walter (jean-charles.walter{at}umontpellier.fr)*,3 and
  12. Jean‐Yves Bouet (jean-yves.bouet{at}ibcg.biotoul.fr)*,1
  1. 1Laboratoire de Microbiologie et Génétique Moléculaires, Centre de Biologie Intégrative (CBI), Centre National de la Recherche Scientifique (CNRS), Université de Toulouse, UPS, Toulouse, France
  2. 2LISBP, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France
  3. 3Laboratoire Charles Coulomb, CNRS‐Université Montpellier, Montpellier, France
  4. 4Dynamique des Interactions Membranaires Normales et Pathologiques, CNRS‐Université Montpellier, Montpellier, France
  5. 5Present Address: Institut Curie, UMR 3664 CNRS‐IC, Paris, France
  1. ↵* Corresponding author. Tel: +33 467 143 146; E‐mail: jean-charles.walter{at}umontpellier.fr
    Corresponding author. Tel: +33 561 335 906; E‐mail: jean-yves.bouet{at}ibcg.biotoul.fr
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Abstract

Chromosome and plasmid segregation in bacteria are mostly driven by ParABS systems. These DNA partitioning machineries rely on large nucleoprotein complexes assembled on centromere sites (parS). However, the mechanism of how a few parS‐bound ParB proteins nucleate the formation of highly concentrated ParB clusters remains unclear despite several proposed physico‐mathematical models. We discriminated between these different models by varying some key parameters in vivo using the F plasmid partition system. We found that “Nucleation & caging” is the only coherent model recapitulating in vivo data. We also showed that the stochastic self‐assembly of partition complexes (i) is a robust mechanism, (ii) does not directly involve ParA ATPase, (iii) results in a dynamic structure of discrete size independent of ParB concentration, and (iv) is not perturbed by active transcription but is by protein complexes. We refined the “Nucleation & caging” model and successfully applied it to the chromosomally encoded Par system of Vibrio cholerae, indicating that this stochastic self‐assembly mechanism is widely conserved from plasmids to chromosomes.

Synopsis

Embedded Image

High‐resolution ChIP‐seq and physico‐mathematical modeling are used to analyze the in vivo ParB DNA‐binding profiles. The “Nucleation and caging” self‐assembly mechanism is widespread to ensure faithful bacterial DNA segregation by ParABS systems.

  • ParBS partition complexes are highly dynamic nucleoprotein complexes.

  • The robust ParB DNA binding profiles derived by ChIP‐seq data are well‐described by the “Nucleation and caging” model.

  • The size of the partition complex is invariant to intracellular variation in ParB levels.

  • This self‐assembly mechanism is observed on Escherichia coli and V. cholerae chromosomes and on the F plasmid.

  • DNA segregation
  • Escherichia coli
  • F plasmid
  • ParABS
  • plasmid partition

Mol Syst Biol. (2018) 14: e8516OpenUrlAbstract/FREE Full Text

Introduction

The segregation of DNA is an essential process for the faithful inheritance of genetic material. Minimalistic active partition systems, termed Par, ensure this key cell cycle step in bacteria (Baxter & Funnell, 2014) and archaea (Schumacher et al, 2015). Three main types of bacterial partition systems have been identified and classified by their NTPase signatures. Of these, the type I, also called ParABS, is the only one present on chromosomes and the most widespread on low‐copy‐number plasmids (Gerdes et al, 2000). Each replicon encodes its own ParABS system and their proper intracellular positioning depends on the interactions of the three ParABS components: ParA, a Walker A ATPase; ParB, a dimer DNA binding protein; and parS, a centromere‐like DNA sequence that ParB binds specifically. The ParA‐driven mechanism that ensures the proper location and the directed segregation of replicons relies on the positioning of ParBS partition complexes within the nucleoid volume (Le Gall et al, 2016) and on a reaction diffusion‐based mechanism (Hwang et al, 2013; Lim et al, 2014; Hu et al, 2017; Walter et al, 2017).

The centromere‐like parS sites are located close to the replication origin on chromosomes and plasmids, and are typically composed of 16‐bp palindromic motifs (Mori et al, 1986; Lin & Grossman, 1998). ParB binds with high affinity to its cognate parS as dimers (Hanai et al, 1996; Bouet et al, 2000). This serves as a nucleation point for assembling high molecular weight ParB‐parS partition complexes, as initially seen by the silencing of genes present in the vicinity of parS (Lynch & Wang, 1995; Lobocka & Yarmolinsky, 1996). ParB binds over 10 Kbp away from parS sites for all ParABS systems studied to date (Rodionov et al, 1999; Murray et al, 2006; Sanchez et al, 2015; Donczew et al, 2016; Lagage et al, 2016). This phenomenon, termed spreading, refers to the binding of ParB to centromere‐flanking DNA regions in a non‐specific manner. The propagation of ParB on DNA adjacent to parS is blocked by nucleoprotein complexes such as replication initiator complexes in the case of the P1 and F plasmids (Rodionov et al, 1999; Sanchez et al, 2015), or repressor–operator complexes on the bacterial chromosome (Murray et al, 2006). These “roadblock” effects led to the initial proposal that ParB propagates uni‐dimensionally on both sides of the parS sites, in a so‐called “1D‐spreading” model (see Fig EV1A). However, this model was put into question as (i) the quantity of ParB dimers present in the cell was insufficient to continuously cover the observed spreading zone, and (ii) ParB binding to parS adjacent DNA resisted biochemical demonstration (reviewed in Funnell, 2016).

Figure EV1.
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Figure EV1. Physical predictions of the current ParB/parS assembly mechanisms

  1. Current models for ParB spreading and partition complex assembly. Schematic representation of the main currently proposed mechanisms for the assembly of partition complexes. ParB dimer (dark blue ovoids) binds specifically to the parS centromere sequence (black rectangle). (a) “1D spreading”. ParB dimers propagate by nearest‐neighbor interactions in 1D following the DNA track (black line) and form filaments away from parS in both directions. (b) “Spreading and bridging”. ParB dimers form (short) 1D filaments on parS and on nsDNA by nearest‐neighbor interactions. By bridging together these patches of ParB induce the formation of DNA loops. (c) “Nucleation and caging”. The transient interactions of ParB with itself and with ParB‐nsDNA provide a network of weaker interactions that nucleates the formation of a highly confined ParB zone. By preventing fast ParB diffusion away from the ParB/parS complex, these independent but synergistic interactions actively cluster most ParB around parS. Importantly, the DNA in the vicinity of parS would preferentially enter this high‐density region of ParB. This results in the stochastic binding of ParB over the centromere‐proximal DNA sequences which depend on the natural loops of the DNA, and leads to the observed power law decrease in ParB density occurring over large genomic distance.

  2. Modeling of the evolution of the DNA binding profiles in the vicinity of parS as a function of the ParB level. Schematics of ParB DNA binding profile as a function of ParB concentration from the predictions of the three main physico‐mathematical models. Note that the intracellular concentration is a good estimate of the amount of ParB in clusters as over 90% of ParB are highly confined around parS. (Left) The “1D‐spreading” model predicts a rapid decrease of the ParB density after the parS site (Broedersz et al, 2014). Most of the particles are homogeneously distributed at an average constant value along the DNA. This behavior is explained from general statistical physical ground: a 1D system of particles with nearest‐neighbor interactions cannot display a phase transition leading to a global clustering. (Middle) The “Spreading & bridging” model, in the strong coupling limit, predicts a clustering of all ParBs along the DNA with the constraint of overlapping with parS site. This leads to a triangular profile with a 1/m slope depending on the number of particles m (Broedersz et al, 2014). (Right) With the “Nucleation and caging” model, the decay only depends on the geometry of the foci (discussed in the manuscript). Upon variation of ParB level, the profiles would remain unchanged at a fixed cluster size despite fluctuation in ParB density and would thus overlap after a rescaling of the amplitude. Only “Nucleation and Caging” describes the profiles observed experimentally using high‐resolution ChIP‐sequencing (Fig 2A).

As an alternative to “1D‐spreading”, two other models for partition complex assembly have been proposed, namely “Spreading & bridging” (Broedersz et al, 2014) and “Nucleation & caging” (Sanchez et al, 2015). Both models (see Fig EV1A) rely on strong ParB clustering with over 90% of ParB confined around parS (Sanchez et al, 2015). The “Spreading & bridging” model proposes that nearest‐neighbor interactions (1D‐spreading) initiated at parS and non‐parS DNA sites in combination with their subsequent interactions in space (3D‐bridging), lead in one of the conditions tested (strong spreading and bridging) to the condensation of the ParB‐bound DNA into a large 3D complex over a contiguous 1D DNA domain (Broedersz et al, 2014; Graham et al, 2014). The “Nucleation & caging” model rather proposes that the combination of dynamic but synergistic interactions, ParB‐ParB and ParB‐nsDNA (Sanchez et al, 2015; Fisher et al, 2017), clusters most of the ParB around parS nucleation sites where a few ParB dimers are stably bound (Fig 1A). The in vivo ParB binding pattern from high‐resolution ChIP‐sequencing data was described with an asymptotic decay as a characteristic power law with an exponent b = −3/2, corresponding to the decreasing probability of the DNA to interact with the ParB cluster as a function of the genomic distance from parS (Sanchez et al, 2015). This model therefore proposes that the DNA surrounding the parS site interacts stochastically with the sphere of high ParB concentration. Interestingly, these three different assembly mechanisms have been explicitly modeled (Broedersz et al, 2014; Sanchez et al, 2015), thus allowing their predictions to be experimentally tested.

Figure 1.
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Figure 1. ParBF binding outside of parS centromere on plasmid and chromosome

  • A. Schematic representation of the “Nucleation & caging” model. Most ParB dimers (green dots) are highly confined in a cluster (dotted circle) centered on the parS sites (black rectangles) onto which some ParBs are stably bound (red dots). The DNA entering the cluster is bound stochastically by ParB. Red and blue lines represent DNA present at small and large (or on a different molecule) genomic distance from parS, respectively.

  • B. ParB clusters on F plasmid in vivo. Typical Escherichia coli cells (DLT3594) display foci of ParBF‐mVenus protein (top) expressed from the endogenous genetic locus of the F plasmid (F1‐10B‐mVenus). The nucleoid is labeled with Hu‐mCherry (central). The overlay (bottom) combines the two fluorescent channels. Over 99% of cells harbor ParBF foci. Scale bars: 1 μm.

  • C. ParBF binding outside parSF on the F plasmid is compatible with a power law decay. High‐resolution ChIP‐seq performed on DLT3586 carrying the F plasmid (F1‐10B). The ParB density, normalized to 1 at the first bp downstream the last parSF binding repeat after background subtraction, is displayed over 14 Kbp on the right side of parSF. Monte Carlo simulations and analytic formula are represented in red and dotted black lines, respectively. MC simulations were performed with a Freely Jointed Chain of linear length L = 15 Kbp and a cluster radius σ = 75 nm. The two other parameters, the Kuhn length a = 10 bp and the total number of proteins on the F plasmid Nt = 360 (related to the normalization constant of the protein concentration κ = 0.41), were fitted from the ChIP‐seq data (see text and Box 1). As a benchmark for simulations, the analytics are obtained from equation (1) with the same parameters. Inset: The ParBF binding profile (black line) is represented as the number of nucleotide reads over 80 Kbp centered at parS. The number of reads in the input sample (gray line) is normalized to the total number of reads in the IP sample.

  • D, E Same as (B and C) with parSF inserted at the xylE locus on E. coli chromosome from DLT3584 and DLT2075, respectively. Cells were grown in the presence of 100 μM IPTG. The Kuhn length was adjusted to a = 22 bp in the simulations and analytics. The characteristics of the A–F genetic loci are presented in Appendix Fig S1A. Note that a highly similar ParBF DNA binding pattern is obtained when ParBF was expressed in trans from a plasmid (strain DLT3567; Appendix Fig S1D).

To study the assembly mechanism of partition complexes, we used the archetypical type I partition system of the F plasmid from Escherichia coli. By varying several key parameters, we evaluated ParB binding patterns in vivo in relation to predictions of each model. We also investigated the chromosomal ParABS system of the main chromosome of Vibrio cholerae. In all tested conditions, our data indicate that ParB binding profiles robustly correlate only with the predictions of the “Nucleation & caging” model.

Results

ParBF distribution pattern around parSF is similar on chromosome and plasmid DNA

The F plasmid partition complex assembles on a centromere sequence, parSF, composed of twelve 43‐bp tandem repeats (Helsberg & Eichenlaub, 1986), which contain ten 16‐bp inverted repeat motifs to which ParBF binds specifically in vitro (Pillet et al, 2011) and in vivo (Sanchez et al, 2015). Partition complex assembly has been investigated using small versions of the F plasmid, either ~10 or ~60 Kbp. To discriminate between the different partition complex assembly models, we used two larger DNA molecules: the native 100‐Kbp F plasmid (F1‐10B; Appendix Table S1) and the 4.6‐Mbp E. coli chromosome with parSF inserted at the xylE locus, in strains either expressing (DLT1472) or not (DLT1215) ParBF from an IPTG‐inducible promoter.

We first verified the formation of ParBF clusters on these two different DNA molecules using the ParBF‐mVenus fluorescent fusion protein. ParBF‐mVenus, fully functional in plasmid partitioning (Appendix Table S2), was expressed from the endogenous locus on the F plasmid (F1‐10B‐BmV) or from a low‐copy‐number plasmid under the control of an IPTG‐inducible promoter (pJYB294). In both cases, we observed bright and compact foci in nearly all cells (Fig 1B and D), indicating that the assembly of highly concentrated ParBF clusters on parSF from large DNA molecules, plasmid or chromosome, occurs similar to the smaller F plasmid counterparts (Sanchez et al, 2015). The number of foci from parSF inserted on the chromosome is half of what is observed with the F plasmid, as expected from the twofold difference in copy number (Collins & Pritchard, 1973).

We then performed ChIP‐sequencing using anti‐ParB antibodies and compared the ParBF patterns from the 100‐Kbp F1‐10B plasmid and the xylE::parSF chromosome insertion (ChIP‐seq data are summarized in Table EV1). For F1‐10B, we observed a ParB binding pattern extending over 18 Kbp of parSF‐flanking DNA nearly identical to the one previously observed on the 60‐Kbp F plasmid (Sanchez et al, 2015), with the asymmetrical distribution arising from RepE nucleoprotein complexes formed on the left side of parSF on incC and ori2 iterons (Fig 1C). Besides the strong ParB binding enrichment in the vicinity of parSF, no other difference in the pattern between the input and IP samples was observed on the F plasmid and on the E. coli chromosome. When parSF is present on the chromosome, the ParBF binding pattern displays a comparable enrichment of xylE::parSF‐flanking DNA over 15 Kbp (Fig 1E). The ParBF distribution extends ~9 and 6 Kbp on the right and left sides of parSF, respectively. The asymmetry does not depend on parSF orientation as an identical ParBF binding pattern was observed with parSF inserted in the reversed orientation (xylE::parSF‐rev, Appendix Fig S1B and C). Similar patterns were also observed when ParBF or ParBF‐mVenus were expressed in trans from a plasmid (Appendix Figs S1D and S3D). To the left side of parSF, ParBF binding ends near the yjbE locus that harbors two promoters (locus A; Fig 1E, inset and Appendix Fig S1A), and to the right, ParBF binding ends at the yjbI gene locus (locus E; Fig 1E and Appendix Fig S1A). A dip in the ParB binding intensity is also observed ~1 Kbp downstream from parSF spanning ~300 bp, corresponding to a promoter region (locus C; Fig 1E and Appendix Fig S1A). Dips and peaks in this ParBF binding pattern differ in terms of position and intensity when compared to the one present on the F plasmid. Overall, these data clearly indicate that the global ParBF binding distribution around parSF depends neither on the size nor the DNA molecule, plasmid or chromosome, and that the ParBF binding probability is dependent on the local constraints of each given locus.

The “Nucleation & caging” binding model describes the partition complex assembly from the nucleation point to large genomic distance

Based on a smaller version of the F plasmid, we previously proposed the “Nucleation & caging” model describing ParB stochastic binding at large distance (> 100 bp) from parS due to DNA looping back into the confined ParB cluster. The characteristic asymptotic decay is compatible with a power law with the exponent b = −3/2, a property that is also observed with 100‐Kbp F plasmid (Fig 1C) and with parSF inserted on the E. coli chromosome (Fig 1E and Appendix Fig S1C). This property is thus an intrinsic parameter of the ParBF binding profile at distance > 100 bp from parSF. The abrupt initial drop in ParBF binding at a shorter genomic distance (< 100 bp) from parSF is explained by the difference of ParBF binding affinities between specific parSF sites (Kd ~2 nM) and non‐specific DNA (Kd ~300 nM; Ah‐Seng et al, 2009). We modeled the DNA molecule by a Freely Jointed Chain (FJC) constituted of N monomers of size a [Kuhn length about twice the persistence length of the corresponding Worm‐like chain (Schiessel, 2013)]. One particle is always attached on parS whereas non‐specific sites are in contact with a reservoir of particles displaying a Gaussian distribution centered on parS. The ParB density was normalized to 1 by the value on the right side of parS and captured for non‐specific sites in the following phenomenological formula as the product of two probabilities integrated over the volume:Embedded Image (1)where Embedded Image is the probability for two DNA loci spaced by a genomic distance as to be at a distance r in space for a Gaussian polymer (de Gennes, 1979); Embedded Image is the equilibrium size of the section of DNA of linear length as; Embedded Image is the probability to find a protein ParB at a radial distance r from the centromere, with κ a normalization constant setting the total number Nt of ParB on the DNA molecule and σ the typical size of the cluster. Note that C(r) is the linearized form of the Langmuir model (Phillips et al, 2012) offering a more compact and intuitive expression for PNC(s). From (1), we easily calculate (see Box 1 for the details of the calculation):Embedded Image (2)

Box 1: Analytic calculation of the linear probability of bound particles along DNA

We model the DNA molecule by a Freely Jointed Chain (FJC) characterized by N freely rotating monomers of size a (total linear length L = aN). The probability distribution P(r, s) to have two monomers of a Gaussian polymer at a distance r and spaced by s monomers (linear distance as) along the polymer is given by de Gennes (1979):Embedded Image (3)where Embedded Image is the averaged radius occupied by a portion of polymer of size as. In the same way, we define the probability to find a particle ParB at the distance r from parS with a Gaussian repartition centered at parS and with a width σ corresponding to the averaged radius of the foci occupied by proteins:Embedded Image (4)where κ is an adimensional normalization constant setting the total number of ParB on the DNA. Thus, the occupation rate of a protein on DNA is given by:Embedded Image (5)

The integration of equation (5) gives:Embedded Image (6)

Note that PNC(0) = κ, thus κ is setting the height of the drop between specific and non‐specific sites and can be estimated directly from the ChIP‐seq data. When R2(s) ⪢ 3σ2, we recover a pure algebraic law PNC ~ s−3/2. The total number of particle Nt on the plasmid is:Embedded Image (7)

The latter integral gives the expression of the parameter κ as a function of Nt:Embedded Image (8)

The second term in equation (8) containing the total number N of monomers induces only corrections to the dominant behavior, we will thus restrict ourselves to the length enriched in ChIP‐seq, i.e. 15 Kbp. Note that the limit N→∞ in equation (8) gives us a condition on the ratio a/σ in order to have proteins on DNA. As κ is the amplitude of a probability, it has to satisfy the condition 0 ≤ κ ≤ 1. Indeed, at a fixed σ, if the Kuhn length becomes too large the polymer does not return in the focus frequently enough in order to ensure Nt bound proteins onto the DNA.

We note that the ParB proteins that bind to the DNA molecule targeted by ChIP‐seq come from a bound state on competing non‐spe DNA (see Fig 1A). Thus, the gain in energy is zero and the binding is solely governed by entropy. However, regarding the binding on specific DNA, there is a gain of energy corresponding to the difference between specific and non‐specific binding energies Δε = εs − εns, respectively. This energy difference Δε is sufficiently large in E. coli to consider that parS sites are always occupied.

Note that the decay versus the genomic distance as is asymptotically determined by a power law of exponent −3/2 modulated by an amplitude depending on the concentration of ParB. The model has only three parameters: σ = 75 nm is determined from superresolution microscopy (Lim et al, 2014; Sanchez et al, 2015). The two remaining parameters κ (a function of the total number of proteins Nt) and the Kuhn length a are readily obtained from a fit of ChIP‐seq data (see Box 1 for the calculation and Materials and Methods for the fitting procedure). Note that the relation between κ and Nt depends on the bioinformatics analysis (Appendix Fig S1E). We obtained κ = 0.41 for both F plasmid or parSF‐chromosomal insertions, leading to 360 and 120 ParB per DNA molecule, respectively, in good agreement with former estimate (Bouet et al, 2005). The last remaining free parameter is the Kuhn length a, estimated to 10 or 22 bp for the F plasmid or parSF‐chromosomal insertions, respectively, to fully describe the ParBF DNA binding profiles (Fig 1C and E, and Appendix Fig S1D). These fitted values are lower than expected, likely due to the modeling that does not account for supercoiling and confinement. Nevertheless, using these defined parameters, the refined “Nucleation & caging” model provides a qualitative prediction of the experimental data over the whole range of genomic positions, from a few bp to more than 10 Kbp.

ParBF DNA binding pattern over a wide range of ParB concentrations favors the “Nucleation & caging” model

The physical modeling for each proposed model (Broedersz et al, 2014; Sanchez et al, 2015) predicts distinct and characteristic responses upon variation of the intracellular ParB concentration (see explanations in Fig EV1B). Briefly, (i) the “1‐D filament” model predicts a rapid decrease of ParB binding followed by a constant binding profile dependent on ParB amount, (ii) the “Spreading & bridging” model predicts linear decays with slopes depending on the ParB amount, and (iii) the “Nucleation & caging” model predicts a binding profile which depends only on the size of the foci. The exponent b = −3/2 of the power law distribution would not change upon ParB amount variation resulting in an overall similar decay at a fixed focus size. In order to discriminate between these three model predictions, we performed ChIP‐seq experiments over a large range of intracellular ParB concentrations. To prevent interference with plasmid stability, we used the chromosomally encoded xylE::parSF construct expressing parBF under the control of an IPTG‐inducible promoter (DLT2075).

Without IPTG induction, ParBF was expressed at ~0.2 of the physiological concentration from F plasmid, as judged by Western blot analyses (Appendix Fig S2B). We also tested an 8‐ and 14‐fold overproduction of ParBF. Assuming the twofold difference in copy number (Fig 1B and D), these three conditions provided ParBF/parSF ratios of 0.4, 16, and 28, relative to the F plasmid one. At these three ratios, ChIP‐seq data revealed that ParBF binding extended similarly over ~15 Kbp around parSF. We analyzed the right side of parSF displaying the longest propagation distance by normalizing each dataset (Fig 2A). It revealed that regardless of ParBF concentration, (i) the ParBF distribution in the vicinity of parSF always displays a good correlation with a power law fitting with an exponent of −3/2, (ii) the ParBF binding profile ends at the same genomic location, i.e. 9 Kbp from parSF, and (iii) the location of the dips and peaks in the pattern is highly conserved, as confirmed by correlation analyses (Table EV2). These findings indicate a highly robust ParBF binding pattern that is invariant over a ~70‐fold variation of the ParBF amount.

Figure 2.
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Figure 2. ParBF DNA binding pattern is robust over a large range of intracellular ParBF concentrations

  1. Normalized and rescaled ParBF binding profiles at different ParBF/parSF ratio. ChIP‐seq density on the right side of parSF inserted at xylE was measured in DLT2075 induced (16, 28) or not (0.4) with IPTG (100 and 500 μM), or carrying HCN plasmids pZC302 (0.04) or pJYB57 (0.016), normalized as in Fig 1C and E with the amplitudes of the curves rescaled by the indicated factors (1.2, 10, or 50) to overlap with the curves of highest amplitude. The ParB/parS ratio is calculated relative to the one of F plasmid as determined from Western blot analyses (Appendix Fig S2B). Monte Carlo simulations and analytical formula are plotted with the same parameters as in Fig 1E. Note (i) that the dips at ˜9 Kbp are not visible for the low levels of available ParB since the signal is close to the basal level, and (ii) that the ChIP‐seq data at 100 μM IPTG induction (16) are the same as in Fig 1E. Inset: Same as in the main to display the density without rescaling.

  2. ParBF is dispersed in the cell upon titration by HCN plasmids. ParBF‐mVenus expressed from pJYB294 was imaged as in Fig 1D in DLT3577 (left) and DLT3576 (right) carrying pZC302 and pJYB57, respectively. The number of extra parSF per cell, indicated on top of each raw, is estimated from the copy number per cell of HCN plasmids carrying 10 specific binding sites. Scale bars: 1 μm.

  3. The size of ParBF clusters is independent of the intracellular ParBF concentration. We considered two possible evolutions of the cluster size upon variations of ParB amount in the framework of “Nucleation & caging” with corresponding schematics drawn on the right. For direct comparison with (A), all curves are displayed with a rescaling of the amplitude corresponding to the WT expression level. Top: constant ParB concentration; supposing that clusters are compact, the cluster radius σ would depend on the number m of ParB like σ = m1/3. Predictions profiles, plotted at different ratio of ParB/parS, vary within the range of the experimental levels tested. Bottom: constant cluster size; ParB concentrations vary but the range of exploration remains the same resulting in overlapping profiles.

To further vary the amount of ParBF available for partition complex assembly, high‐copy‐number (HCN) plasmids containing the parSF sequence were introduced into the xylE::parSF strain to efficiently titrate ParBF by its binding to the excess of specific binding sites (~200‐ and ~500‐fold on pBR322 and pBSKS derivatives, respectively; Diaz et al, 2015). Epifluorescence microscopy of these strains reveals that all cells display a diffuse ParB‐mVenus fluorescence (Fig 2B) in contrast to concise foci without titration (Fig 1A), suggesting a large reduction of ParB availability to non‐specific sites in the vicinity of parSF on the chromosome. ChIP‐seq analyses in the two titration conditions revealed that ParB binding in the vicinity of parSF was dramatically reduced as expected. However, rescaling the signals by a factor of 10 and 50 for the pBR322 and pBSKS parSF‐carrying derivatives, corresponding to a ParBF/parSF ratio of 0.04 and 0.016, respectively, revealed a ParBF binding pattern above the background level (Fig 2B, inset). In both datasets, ParBF binding decreases progressively over about the same genomic distance and with a similar power law decay as without titration. Moreover, even with these very low amounts of available ParBF, the dips and peaks in the profiles are present at similar positions (Table EV2).

The invariance of the overall ParB profile over three orders of magnitude of ParB concentration (Fig 2B, inset) excludes the predictions of both the “1‐D filament” and the “Spreading & bridging” models (Fig EV1). In addition, the conservation in the positions of the dips and peaks indicates that the probability of ParBF binding at a given location is also not dependent on the amount of ParBF in the clusters. These results are strongly in favor of the refined “Nucleation & caging” model presented above.

The size of the dynamic ParB/parS cluster is independent of ParB intracellular concentration

In all of the ParB induction levels tested, the genomic distance over which ParBF binds around parSF is constant and displays a very similar decay (Fig 2A). This conserved binding behavior could provide information on the cluster size as a function of ParB amount. Indeed, the “Nucleation & caging” model predicts a density per site Embedded Image (see equation (2)). Thus, the PNC(s) decay is entirely determined by the geometry of the foci and the intrinsic flexibility of the DNA, and the overall amplitude depends on the number of ParB. Varying the ParB amount could lead to two limiting situations: (i) the density of ParB, but not σ, is constant, (ii) σ is fixed and ParB density is variable. We plotted these two situations in the range of ParB/parS ratio considered experimentally (Fig 2C): with (i) the different PNC(s) strongly varied, and (ii) PNC(s) was invariant relative to the ParB amount resulting in overlapping profiles. Experimental data (Fig 2A) are in excellent agreement with the latter. From this modeling, we thus concluded that the size of partition complexes is invariant to change in ParB intracellular concentration.

The arginine rich motif (box II) of ParBF is critical for partition complex assembly

The ability of ParB to multimerize through dimer–dimer interactions is required for the formation of ParB clusters. A highly conserved patch of arginine residues present in the N‐terminal domain of ParB (box II motif; Yamaichi & Niki, 2000) has been proposed to be involved in ParB multimerization (Breier & Grossman, 2007; Song et al, 2017). To examine to what extent the box II motif is involved in vivo in the assembly of ParBF clusters, we changed three arginine residues to alanine (Appendix Fig S3A). The resulting ParBF‐3R* variant was purified and assayed for DNA binding activity by electro‐mobility shift assay (EMSA) in the presence of competitor DNA using a DNA probe containing a single parSF site (Fig 3A). ParBF‐3R* binds parSF with high affinity (B1 complex) indicating no defect in parS binding nor dimerization, a property required for parS binding (Hanai et al, 1996). However, in contrast to WT ParB, the formation of secondary complexes (B'2 and B'3), resulting from non‐specific DNA binding and dimer–dimer interaction (Sanchez et al, 2015), was impaired further suggesting the implication of box II in dimer–dimer interaction. A mini‐F carrying the parBF‐3R* allele (pAS30) was lost at a rate corresponding to random distribution at cell division (Appendix Table S2), indicating that this variant is unable to properly segregate the mini‐F.

Figure 3.
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Figure 3. The box II motif of ParBF is crucial for ParBF binding in the vicinity of parSF and cluster formation

  1. The formation of secondary ParBF‐DNA complexes requires the box II motif. EMSA was performed with a 144‐bp 32P‐labeled DNA fragments (C144) carrying a single 16‐bp parS binding motif. Reaction mixtures containing 100 μg ml−1 sonicated salmon sperm DNA were incubated in the absence (−) or the presence of increasing concentrations (gray triangle; 10, 30, 100, 300, and 1,000 nM) of ParBF or ParBF‐3R*. Positions of free and bound probes are indicated on the left. B1 represents complexes involving the specific interaction on the 16‐bp binding site, while B'2 and B'3 complexes represent secondary complexes involving the parSF site with one or two additional nsDNA‐binding interactions, respectively (Sanchez et al, 2015).

  2. ParBF cluster formation requires the box II motif. Epifluorescence microscopy of ParBF‐3R*‐mVenus from DLT3566 is displayed as in Fig 1D. Scale bars: 1 μm.

  3. ParBF in vivo DNA binding in the vicinity of parSF sites requires the box II motif. ChIP‐seq was performed on DLT3726 carrying parSF in the xylE chromosomal locus and expressing ParBF‐3R* variant. ParBF‐3R* DNA binding profile displayed the number of nucleotide reads as a function of the Escherichia coli genomic coordinates. The peak at parSF covered approximately 950 bp, which corresponds to the 402 bp between the 1st and 10th specific binding sites and ˜280 bp on each sides (representing the average size of the DNA library; see Appendix Fig S3E). No ParBF‐3R* enrichment was found on parSF‐flanking DNA and elsewhere on the chromosome. Inset: Zoom in on the right side of parSF over 5 Kbp with the ParB density, normalized to 1 at the first bp after the last parS binding repeat, plotted as a function of the distance from parSF. Note that a highly similar DNA binding pattern is obtained with ParBF‐3R*‐mVenus (strain DLT3566; Appendix Fig S3C).

The ParBF‐3R* variant was then expressed in native or fluorescently tagged (ParB‐R3*‐mVenus) forms, from pJYB303 or pJYB296, respectively, in the xylE::parSF strain. By imaging ParBF‐3R*‐mVenus, we observed only faint foci in a high background of diffuse fluorescence (Fig 3B). These barely detectable foci may correspond to ParBF‐3R*‐mVenus binding to the 10 specific sites present on parSF and, if any, to residual ParBF cluster formation. We then performed ChIP‐seq assays with ParBF‐3R* present in ~25‐fold excess (relative ParBF/parSF ratio compared to the F plasmid one; Appendix Fig S3B). The resulting DNA binding profile displayed enrichment only at parSF with a total absence of ParBF binding on parSF‐flanking DNA (Fig 3C). Indeed, no residual ParBF binding to non‐specific DNA was detected when the size of the DNA fragments in the IP library is taken into account (Appendix Fig S3E). This pattern differs from those observed in conditions of ParBF titration (Fig 2A; inset), indicating that the ParBF‐3R* box II variant is fully deficient in clustering in vivo. The same patterns were also observed with ParBF‐3R*‐mVenus (Appendix Fig S3C and F) indicating that the mVenus fluorescent‐tag fused to ParBF does not promote cluster assembly.

Together, these results indicate that the box II variant is specifically deficient in ParBF cluster assembly but not in parSF binding, and thus reveal that the box II motif is critical for the auto‐assembly of the partition complex.

ParB also propagates stochastically from native chromosomal parS sites

ParABS systems are present on most bacterial chromosomes (Gerdes et al, 2000). To determine whether chromosomal ParB‐parS partition complexes also assembled in vivo in a similar manner to the F plasmid, we investigated the bacterium V. cholerae, whose genome is composed of two chromosomes. We focused on the largest chromosome to which ParBVc1 binds to three separated 16‐bp parS sites comprised within 7 Kbp (Saint‐Dic et al, 2006; Baek et al, 2014; Fig 4A).

Figure 4.
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Figure 4. ParB of Vibrio cholerae assembled in cluster similarly to ParBF

  1. Schematic representation of the genomic locus of the chromosome 1 of V. cholerae with the three parS sites, named parS1‐3. The rRNA operon (blue rectangle) spans the genomic coordinates 53,823–59,123.

  2. ChIP‐seq performed on strain N16961 is displayed as the number of nucleotide reads in function of the genomic coordinates. Correspondence to the parS1‐3 location represented in (A) is indicated by gray dotted lines. The number of reads in the input sample (gray line) is normalized to the total number of reads in the IP sample.

  3. We modeled the ChIP‐Seq data as in Fig 1C–E by means of MC simulations with a Freely jointed chain of size N = 2,000 monomers of size a = 16 bp. Data are normalized after background subtraction to the read value at parS1 (genomic coordinate 62,438). The best fit was achieved with σ = 25 nm and an amplitude κ = 0.15 leading to Nt˜50 ParB on the chromosome. In the MC simulation, we accounted for the finite width of the distribution around parS sites by including the average fragment size of the DNA library (304 bp; for comparison, a simulation without is provided in Appendix Fig S4B).

We purified ParBVc1 antibodies against his‐tagged ParBVc1 and performed ChIP‐seq assays on exponentially growing cultures. The ParBVc1 DNA binding pattern covered ~18 Kbp and displayed three peaks at the exact location of the three parSVc1 sites (Fig 4B). No other ParB binding was observed over the Vibrio genome. Each peak exhibits a distinct but reproducible difference in intensity that might correspond to the slight differences in parSVc1 sequences (Appendix Fig S4A). An asymmetry in the binding pattern was observed on the left side of parS1 with the limit of ParBVc1 binding corresponding to the end of the rRNA operon located ~4 Kbp upstream from parS1 (Fig 4B). This suggests that highly transcribed genes might significantly interfere with the extent of ParB binding.

We modeled ParBVc1 DNA binding profile with the framework of the refined “Nucleation and caging” model (see above). The simulations consider three non‐interacting spheres centered on each of the parS sites and take into account (Fig 4C) the average fragment size of the DNA library to account for the width of the peaks around each parS (same modeling as displayed in Appendix Fig S1E for the F plasmid). Simulations are found in good agreement with the ChIP‐seq data with the following parameters: σ = 25 nm, a = 16 bp, and κ = 0.15 leading to Nt~50 ParB proteins on the chromosome (see Materials and Methods for the fitting procedure). Overall, these parameters are of the same order of magnitude as those used for E. coli. The maxima in the ParB binding profile depends on the parS sites (Fig 4C) and are interpreted as a difference in binding affinity. In the simulations, the ParB density is normalized to 1 by the value on the right of parS1. The relative density of the two other parS sites is fixed according to the values read on the ChIP‐seq plot (3 and 29% lower affinity for parS2 and parS3 compared to parS1, respectively). We also noticed a clear difference at the minima of ParB binding on either side of parS2 (64.2 and 68 Kbp; Appendix Fig S4B). In the case of a single cluster constraining the three parS, the profile would only depend on the genomic distance from parS2 resulting in a symmetrical pattern, while in the case of three independent clusters, an absence of symmetry due to the occupation of the specific sites is expected. This indicates that the system displays three independent clusters nucleated at each parS sites. However, the possibility that these clusters mix together at a frequency dependent on the genomic distance between parS sites is not excluded. At larger distances from parS sites, differences between the experimental data and the simulation probably arise from strong impediments to ParB binding, such as the presence of the rRNA operon.

These data strongly support that the partition complex assembly mechanism is conserved on plasmid and chromosome ParABS systems.

Nucleoprotein complexes, but not active transcription, are the major determinants for the impediment of ParB stochastic binding

The major dips in the ParBF DNA binding signal are often found at promoter loci (Appendix Fig S1A). To investigate the link between gene expression and the impediment to ParB propagation, we reproduced the ChIP‐seq assays using the xylE::parSF strain grown in the presence of rifampicin, an inhibitor of RNA synthesis that traps RNA polymerases at promoters loci in an abortive complex unable to extend RNAs beyond a few nucleotides (Herring et al, 2005). We did not observe significant changes to the ParB signal on either side of parSF (Fig 5A; compare red and blue curves). Notably, the ParB signal still strongly drops in promoter regions (e.g., loci A, C, and E) and the dips and peaks are present at the same locations (Fig 5B and Table EV2). This indicates that active transcription by RNA polymerase is not a major impediment to ParB binding, but rather that RNA polymerases bound or stalled at the promoter could.

Figure 5.
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Figure 5. Robust dips and peaks signatures in ParB DNA binding profiles

ChIP‐sequencing assays were performed on DLT2075 (xylE::parSF) expressing ParBF grown in exponential (expo) or stationary (stat) phases with addition of rifampicin when indicated (+Rif). The assays have been performed in duplicate for the +Rif and once for the stationary phase experiments.

  1. ParBF DNA binding around parSF is independent of active transcription. The color‐coded ParBF profiles are represented over 50 Kbp as the relative ParB density normalized to 1 at the first bp after the last parSF binding site. Loci A, C, E, and F are defined in Appendix Fig S1A.

  2. The dips and peaks are highly similar in the three indicated conditions. Same as in (A) with zoom in on the right side of parSF up to 9 Kbp and normalization to 1 at genomic coordinate 230. The dotted line corresponds to the analytics description of “Nucleation and caging” (see details in Fig 1C–E).

  3. ParBF binding profile upstream of the locus A. Same as in (A) with zoom in from −6.5 to −16.5 Kbp by normalization to 1 at genomic coordinate −6.5 Kbp (upstream of the dip at the locus A). The ParBF DNA binding profile remains compatible to a power law, represented by the analytics description (dotted line), upstream of the locus A in stationary phase (black) and in exponential phase (blue). Also, the dips and peaks are highly similar in both conditions. These data are not in favor of the “1D‐spreading” or the “Spreading and bridging” models that predicts a basal uniform distribution or a linear decrease after a barrier, respectively (Broedersz et al, 2014).

  4. The promoter region at locus A prevents ParBF DNA binding. ChipIP‐seq assays were performed in isogenic xylE::parSF strains (DLT2075; black curve) in which the locus A is replaced by a kanamycin gene (DLT3651; red curve). The assay in the Δ(locus A) genomic context has been performed once. The relative ParB density as a function of the distance from parSF is drawn and normalized as in (A). The promoter region is depicted as in Appendix Fig S1A.

We also measured the ParB binding profile in stationary phase, a growth condition in which gene expression is strongly reduced. On the right side of parSF, ParB distribution was similar to all other tested conditions (Fig 5A), thus confirming the robustness of the binding pattern. On both sides, the strong reduction of ParB binding at loci A, C, and E was still observed. However, in contrast to the other conditions, ParB binding recovers after these loci and extends up to ~18 Kbp on both sides, resulting in the location of parSF in the middle of a ~36 Kbp propagation zone. Interestingly, the ParB binding profiles after these recoveries are still compatible with a power law exhibiting the same characteristics as at lower genomic distances (Fig 5C). In stationary phase, the reduced intracellular dynamics (Parry et al, 2014) and the higher compaction of the DNA (Meyer & Grainger, 2013) may stabilize the partition complex revealing the ParBF bound at larger distances from parSF. Also, in higher (stationary phase) or lower (rifampicin‐treated cells) DNA compaction states (Appendix Fig S5A), the ParBF DNA binding pattern is not altered, exhibiting a similar profile of dips and peaks (Fig 5B). This indicates that the assembly of the partition complex is not perturbed by variation in DNA compaction level within the nucleoid.

To further demonstrate the impediment of ParBF binding in promoter regions, we constructed a strain in which the locus A, carrying two promoters, an IHF and two RcsB binding sites, is replaced by a kanamycin resistance gene (Fig 5D). The measured ParBF binding pattern remained highly comparable except at the locus A where the dip is absent. This result clearly indicates that site‐specific DNA binding proteins are the main factors for restricting locally ParBF binding.

ParB molecules exchange rapidly between partition complexes

Single molecule in vivo localization experiments have shown that over 90% of ParBF molecules are present at any time in the confined clusters (Sanchez et al, 2015). However, stochastic binding of most ParBF on non‐specific DNA suggests that partition complexes are highly dynamic. To unravel ParBF dynamics, we performed fluorescence recovery after photobleaching (FRAP) on two‐foci cells for measuring ParBF dynamics between partition complexes. By laser‐bleaching only one focus, we could determine whether ParBF dimers could exchange between clusters and measure the exchange kinetics. As ParBF foci are mobile, we choose to partially bleach (~50%) the focus enabling immediate measurement of fluorescence recovery (Fig 6A and B). A few seconds after bleaching, the fluorescence intensity recovers while it decreases in the unbleached focus. This exchange is progressive and the intensity between the two foci equilibrated in ~80 s on average (between 50 and 120 s for most individual experiments). We estimate that, when exiting a cluster, each ParBF dimer has the same probability to reach any of the two clusters. Therefore, the time of equilibration between the two foci corresponds to the exchange of all ParBF. These results thus indicate that the partition complexes are dynamic structures with a rapid exchange of ParBF molecules between clusters.

Figure 6.
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Figure 6. ParB dynamics between partition complexes

ParBF exchange between foci was measured by FRAP and FLIM (fluorescence lifetime imaging microscopy) from two‐foci cell of DLT1215 carrying pJYB234.

  1. Representative images of a photobleached cell during a FRAP experiment. The 488 nm laser was pulsed (Bleach) on one of the two foci at ˜2.4 s (black arrow). Red and blue arrows correspond to the bleached and unbleached focus, respectively. Time is indicated in seconds (upper right). The cell outline is drawn in red. Scale bar: 1 μm.

  2. Quantification of ParBF‐mVenus fluorescence intensity over time. The dynamics of fluorescence intensity is shown from averaging 18 independent measurements of the bleached (FRAP, red line) and unbleached (FLIM, blue line) foci. Foci fluorescence intensity in each experiment was normalized to the average intensity of each focus before photobleaching. The Appendix Fig S6 displays the three pre‐bleaching and the first post‐bleaching data points on an expanded scale. Natural bleaching during the course of the experiments (green curve) was estimated for each measurement by averaging the fluorescence intensity of 15 foci present in each field of view. Error bars correspond to standard deviation (mean ± SD).

Discussion

Despite over three decades of biochemical and molecular studies on several ParABS systems, the mechanism of how a few ParB bound to parS sites can attract hundreds of ParB in the vicinity of parS to assemble a high molecular weight complex remained puzzling. The three main mechanisms proposed for ParB‐parS cluster assembly have been studied from physico‐mathematical perspectives (Broedersz et al, 2014; Sanchez et al, 2015), predicting very different outcomes for the ParB binding profile in the vicinity of parS sites upon change in ParB concentration. Here, the ParB binding patterns were found invariant over a large variation of ParB amount displaying a robust decay function compatible with a power law with the characteristic exponent b = −3/2 and a conserved length of the propagation zone (Fig 2A). Strikingly, even in the titration conditions tested, which resulted in a very low amount of ParB available to bind to non‐specific DNA sites, the overall ParB DNA binding pattern remained invariant (Fig 2A, inset). Neither “1D spreading” nor “Spreading & bridging” physical models could describe these data in the conditions tested (Broedersz et al, 2014). A variant of the latter model has explored the ParB binding pattern in the low spreading strength limit (Walter et al, 2018). This “Looping & clustering” model also predicts variations in the ParB binding pattern over a simulated 4‐fold range of ParB amount, which is in contrast to the invariant pattern observed experimentally over more than three orders of magnitude (Fig 2). In conclusion, only the “Nucleation & caging” model based on stochastic ParB binding well describes the experimental data and provides accurate predictions for the mechanism of the partition complexes assembly.

We refined the modeling of the dynamic and stochastic ParB binding model by including DNA binding affinities for specific and non‐specific sites to describe the initial drop observed immediately after parS. In this framework, we found that ParB clusters have a constant size accommodating important variations in ParB concentration (Fig 2C). We propose that the cluster size is dependent on the intrinsic ParB‐ParB and ParB‐nsDNA interactions, and would thus be an inherent characteristic of each ParABS system (Funnell & Gagnier, 1993; Sanchez et al, 2015; Taylor et al, 2015). In the case of F and P1 plasmids, overexpression of ParB was reported to silence genes in the vicinity of their cognate parS (Lynch & Wang, 1995; Lobocka & Yarmolinsky, 1996), by a mechanism based on 1D‐spreading (Rodionov et al, 1999). Our finding that the size of ParB clusters is invariant but their density increases with ParB overexpression provides a new explanation for the silencing phenomenon. We propose that RNA polymerases accessibility to promoters present near parS is dependent on the ParB density within the cluster. At physiological level, RNA polymerase would have efficient access to promoter sites while upon the rise of ParB level their diffusion within the high‐density cluster would be reduced proportionally to the overexpression level, as observed experimentally (Rodionov et al, 1999). This is reminiscent to the observation that a change in the level of supercoiling is specifically induced on ParB‐parS carrying mini‐F plasmids (Biek & Shi, 1994). It has been shown that this deficit in negative supercoiling could be due to the reduce accessibility of DNA gyrases to the small sized mini‐F plasmid (< 10 Kbp) that is “masked” by the ParB‐parS nucleoprotein complex (Bouet & Lane, 2009). The invariance in the size of the ParB cluster but the density may also well explain the supercoiling deficit observed in vivo. The refined modeling also well describes the chromosomal partition system of V. cholera, predicting three independent clusters nucleated at each of the three parS sites (Fig 4C). In all cases reported here, the partition complex assembly is well described by the “Nucleation & caging” model, and we propose that this mechanism of assembly is conserved on chromosome and plasmid partitioning systems.

In addition to its robustness within a large range of ParB concentration (Fig 2A) and different nucleoid compaction states (Fig 5A), the in vivo ParB DNA binding pattern also exhibits conserved dips and peaks at particular locations (Table EV2). The major dips are located at promoter regions (Fig 1E and Appendix Fig S1A) but do not depend on active transcription (Fig 5B). This suggests that these specific signatures mostly depend on the intrinsic local genomic environment. This hypothesis was confirmed by deleting the locus A, carrying several regulator binding sites, which led to the suppression of the dip at this position (Fig 5D). Therefore, proteins such as transcriptional regulators and NAPs (nucleoid‐associated proteins) that bind specifically to DNA prevent ParB binding to these sites, thus reducing locally the ParB signal. We propose that this impediment to ParB binding is proportional to the time of occupancy of these regulators at their site‐specific DNA binding sites. Larger nucleoprotein complexes, as exemplified on the F plasmid at the iteron sites (ori2 and incC; Fig 1C) that interact in cis and in trans (Das & Chattoraj, 2004), were previously proposed to be spatially excluded from the vicinity of the ParB cluster with a low probability that DNA beyond these sites comes back into the cluster preventing ParB binding (Sanchez et al, 2015). Such an exclusion does not occur from smaller protein–DNA complexes, with the recovery of the ParB binding signal that further follows the characteristic power law decay (e.g., locus A; Fig 5C). These results show that low molecular weight protein–DNA complexes do not impair the overall, only the local, ParB binding pattern.

The formation of highly concentrated clusters of ParB relies on a strong ParB‐parS interaction and two other interactions, ParB‐ParB and ParB‐nsDNA (Sanchez et al, 2015; Fisher et al, 2017). ParB mutants that do not propagate outside parS are impaired in partition activity and in cluster formation in vivo (Rodionov et al, 1999; Breier & Grossman, 2007). The conserved box II motif (Yamaichi & Niki, 2000) was suggested to be part of the dimer–dimer interface (Breier & Grossman, 2007; Graham et al, 2014) but some misfolding caveat has been reported with some mutants, such as ParBBsub‐G77S (Song et al, 2017). In vivo the box II variant (ParBF‐3R*) is totally deficient in partition activity and cluster formation (Fig 3B) while proficient for parSF binding (Fig 3C). The total absence of ParBF‐3R* binding outside parSF (Fig 3C and Appendix Fig S3E) indicates that the box II motif is the major interface for the interaction between ParB dimers and is critical for the partition complexes assembly in vivo and the DNA partition activity.

ParA interacts with partition complexes in a ParB‐dependent manner both in vitro and in vivo (Bouet & Funnell, 1999; Lemonnier et al, 2000) to ensure the ATP‐dependent segregation of centromere sites upon DNA replication (Fung et al, 2001; Scholefield et al, 2011; Ah‐Seng et al, 2013). Previous studies from V. cholerae and S. Venezuela have reported contradictory results on the involvement of ParA in the assembly of the partition complex (Baek et al, 2014; Donczew et al, 2016), which may arise from the pleiotropic effects of ParA on cellular processes, such as gene transcription or DNA replication (Murray & Errington, 2008). The ParBF DNA binding profiles on the F plasmid (Fig 1C) and on the E. coli chromosome (Fig 1E), in the presence and absence of ParAF, respectively, are highly similar, therefore indicating that they assemble independently of ParA. Partition complexes, composed of hundreds of ParB dimers, were thought to be confined at the interface between the nucleoid and the inner membrane (Vecchiarelli et al, 2012). The observation that they rather are located within the nucleoid in a ParA‐dependent manner (Le Gall et al, 2016) raises the question as to how they are not excluded from it. The “Nucleation & caging” model could solve this apparent paradox. Indeed, relying on a strong ParB‐parS interaction (nM range) and two other synergistic, but labile interactions, ParB‐ParB and ParB‐nsDNA (hundreds of nM range; Fisher et al, 2017; Sanchez et al, 2015), it would allow the dynamic confinement of most ParB without forming a rigid static structure. This dynamic organization is further supported by the finding that ParB dimers quickly exchange between clusters (~80 s; Fig 6). By comparison, the equilibration times between H‐NS and TetR‐tetO clusters were 5 or 10 times much longer, respectively (Kumar et al, 2010). Since > 90% of ParB are present in clusters (Sanchez et al, 2015), it implies that their time of residency is much longer inside than outside, in agreement with fast diffusion coefficients (~1 μm2 s−1) for non‐specific DNA binding proteins (Kumar et al, 2010). We propose that, collectively, all the individual but labile interactions for partition complex assembly allow the whole complex attracted by ParA to progress within the mesh of the nucleoid.

Materials and Methods

Bacterial strains and plasmids

Escherichia coli and V. cholerae strains and plasmids are listed in Appendix Table S1. Cultures were essentially grown at 37°C with aeration in LB (Miller, 1972) containing thymine (10 μg ml−1) and chloramphenicol (10 μg ml−1) as appropriate. For microscopy and stability assays, cultures were grown at 30°C with aeration in MGC (M9 minimal medium supplemented with 0.4% glucose, 0.2% casamino acids, 1 mM MgSO4, 0.1 mM CaCl2, 1 μg ml−1 thiamine, 20 μg ml−1 leucine, and 40 μg ml−1 thymine).

Strain DLT1471 was constructed in several steps. First, the Eco47III–ApaI sopOPAB DNA fragment from plasmid F was inserted into a repts plasmid‐borne 933‐codon 3′ fragment of the lacZ gene (pJYB50). The resulting plasmid, pJYB52, was introduced into DLT1215 and proceeded for allele exchange by selecting double recombination events as described (integration–excission assay; Cornet et al, 1994), yielding to the following chromosome fusion lacZ’::PparABF::’lacZ in strain DLT1471. Strain DLT1472 expressing ParBF from the chromosome fusion lacZ’::PparBF::’lacZ was described previously (Bouet et al, 2005).

Strains DLT2073 and DLT2075 are DLT1215 and DLT1472 derivatives, respectively, in which a 538‐bp DNA fragment carrying the entire parSF site has been introduced at the EcoRV restriction site of the xylE gene (91 min on the E. coli chromosome) in the forward orientation. Briefly, the PCR‐amplified xylE gene was introduced onto a pFC13 derivative (Cornet et al, 1994) using the HindIII and XhoI restrictions sites, leading to pJYB102. A parSF DNA fragment, PCR‐amplified from pDAG114 using the following oligonucleotides SopC‐5′RV (5′‐TCCTTTGATATCGGCCAGAAAGCATAACTG‐3′) and SopC‐3′RV (5′‐GCCGATATCAGGAATTCATGGAATCGTAGTCTC‐3′), was introduced into the EcoRV restriction of the xylE locus on pJYB102, leading to pJYB103.1 and pJYB103.2 depending on the orientation of parSF insertion. These plasmids were introduced into DLT1472 and subjected to the integration–excision procedure as above. Strains DLT2074 and DLT2076 are identical to DLT2073 and DLT2075, respectively, with parSF inserted in xylE in the reverse orientation (parSF‐rev). Insertions of parSF on the E. coli chromosome at locations other than xylE were performed using lambda red recombineering and selecting for the FRT‐KanR‐FRT cassette amplified from pDK4 (Datsenko & Wanner, 2000). The hupA‐mcherry allele used for live imaging of the nucleoid was inserted in various E. coli strains by P1 transduction from strain DLT3053 (Le Gall et al, 2016).

The original plasmid F, F1‐10 (gift from C. Lesterlin), was converted to ccdB− to allow for performing stability assays. The removal of the CcdB toxin from the addiction system was performed along with the introduction of the chloramphenicol resistant gene, using lambda red recombineering (Datsenko & Wanner, 2000), by inserting the corresponding loci from the mini‐F pDAG114 (Lemonnier et al, 2000) into F1‐10, leading to F1‐10B. When necessary, the excision of the FRT‐kan‐FRT selection cassette was performed using the pCP20 plasmid (Datsenko & Wanner, 2000). F1‐10B ΔAB and F1‐10B‐BmV derivatives were constructed by lambda red recombineering using plasmids pDAG209 and pJYB234, respectively, as substrates to generate the linear DNA fragment that include the cat gene for selecting the recombinants.

Mutations in parBF were first introduced into pYAS6 by mutagenic primer‐directed replication using the Stratagene QuikChange kit and subsequently integrated into mini‐F derivatives or the pAM238 expression vector by PCR amplification followed by allelic replacement using appropriate restriction enzymes. The mVenus gene was constructed by introducing the monomeric A207K mutation in the venus‐Yfp gene (Sanchez et al, 2015). All plasmid constructs were verified by DNA sequencing (MWG). Mini‐F and F plasmids derivatives were introduced in strains by CaCl2‐ or electro‐transformation and conjugation, respectively.

pJYB322 was constructed by introducing a 171‐bp DNA fragment carrying three consensus parS sites (5′‐TGTTTCACGTGAAACA‐3′), called 3x‐parSchr, into the NheI and ClaI restriction sites of pJYB263, the ΔparA derivative of pJYB234 (Le Gall et al, 2016).

Plasmid stability assays

Stability of mini‐F and plasmid F derivatives was assayed in strain DLT1215 grown over 25 generations in MGC at 30°C or over 20 generations in LB at 37°C, and subsequently plated on LB agar medium, replica plating to medium with chloramphenicol, and calculating loss rates from the fractions of each sample resistant to chloramphenicol, as previously described (Sanchez et al, 2013).

Epifluorescence microscopy

Exponentially growing cultures were deposited on slides coated with a 1% agarose buffered solution and imaged as previously described (Diaz et al, 2015), using an Eclipse TI‐E/B wide field epifluorescence microscope. Snapshots were taken using a phase contrast objective (CFI Plan Fluor DLL 100X oil NA1.3) and Semrock filters sets for YFP (Ex: 500BP24; DM: 520; Em: 542BP27) and Cy3 (Ex: 531BP40; DM: 562; Em: 593BP40) with an exposure time range of 0.1–0.5 s. Nis‐Elements AR software (Nikon) was used for image capture and editing.

ChIP‐sequencing assay and analysis

ChIP‐seq was performed as previously described (Diaz et al, 2017) with minor modifications, using polyclonal antibodies raised against WT ParBF or his‐tagged ParBVc1. ParBF and ParBVc‐1 antibodies were affinity‐purified from anti‐ParBF (our own) and anti‐ParBVc‐1 (gift donated from the lab of D. Chattoraj) polyclonal serums, using purified ParBF and ParBVc‐1‐his6, respectively. The optimization step for determining the amount of antibodies needed to pull down all ParBVc1 in the IP samples was fully described (Diaz et al, 2017).

A maximum of 2 ng of ChIP DNA was used to synthetize the library as described in manufacturer's instructions (Ion ChIP‐Seq Library Preparation on the Ion Proton™ System—revision B—Step10). The last size selection was modified by a double size selection (0.55×/0.25×) of binding to AMPure® XP beads followed by a step of wash and elution. After qualification and quantification (Bioanalyzer—Agilent, Santa Clara, CA), libraries were diluted to 100 pM and pooled in a ratio of 20% input and 80% IP. For all the subsequent analyses, the measured size distribution of the DNA fragments was subtracted of the linker size added for sequencing (93 bp). Template preparation (clonal library amplification on sequencing bead) was made using the Ion PI™ Template OT2 200 Kit version 3 following the manufacturer's instructions. Emulsion PCR was performed in the Ion OneTouch™ 2 Instrument. DNA‐positive ISPs were then recovered and enriched in the Ion OneTouch™ ES according to standard protocols. Sequencing of samples was conducted on Ion Proton and PI chips according to the Ion PI™ 200 Sequencing Kit Protocol (version 3).

The sequence reads were counted and aligned as described (Diaz et al, 2017). The quality of reads was assessed with FastQC program (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and they were mapped with TMAP from Torrent Suite software 5.0.4 (https://www.thermofisher.com/fr/fr/home/life-science/sequencing/next-generation-sequencing/ion-torrent-next-generation-sequencing-workflow/ion-torrent-next-generation-sequencing-data-analysis-workflow/ion-torrent-suite-software.html). The counting of reads over the genomic sequences was performed using bedtools 2.26.0 (http://bedtools.readthedocs.io/en/latest/) with the tool genomecov and the option –d and –fs or –bga for the bedgraph files production for peak visualization. This allows to display the totality of ChIP‐seq data with the possibility to view several datasets simultaneously using IGV (Integrated Genome Viewer, version 2.3; http://software.broadinstitute.org/software/igv/). Graphing the DNA portion of interest from ChIP‐seq data was done using Excel or R softwares. Cognate input and IP samples were normalized by the number of total reads for direct comparison. For the ParB density plots, the data were normalized after background subtraction and set to the value of 1 at the last bp of the 10th repeat of parSF, allowing to display the results of Monte Carlo simulations on the same graph.

Fit of the parameters

The “Nucleation and caging” model contains only three parameters: σ, a, and Nt. One parameter is known experimentally: σ ~ 75 nm (Lim et al, 2014; Sanchez et al, 2015). We know the order of magnitude of Nt ~ 300 (Bouet et al, 2005) as a benchmark of the fitted value obtained from κ from ChIP‐seq data. The value of σ2/a is fitted from ChIP‐seq, which gives access to the value of a (σ being known). We use the following trial function to perform a non‐linear fit of the ChIP‐seq data between 0 and 10 Kbp:Embedded Image where A0 and A1 are the two fit parameters allowing to obtained the two free parameters of the model: κ (related to Nt) and a. Note that the length unit to analyze the ChIP‐seq data is the base pair (bp). We identify A0 = κ and A1 = a/3σ2. For the F plasmid, we find A0 = 0.41 and A1 = 1.96 × 10−4 nm−1. From A1, using σ = 75 nm, we get a ~ 3.4 nm (~10 bp). From A0, we get κ ~ 0.41 leading to Nt ~ 360. For the chromosome, using the same analysis, we get A0 = κ ~ 0.41 leading to a smaller number of proteins on the DNA, Nt ~ 120, yet of the same order of magnitude as the F plasmid. The second variable of the fit A1 = 4.33 × 10−4 nm−1 leads to a ~ 7.5 nm (~22 bp). For the estimation of the parameters for V. cholerae, the experimental values for σ and a are not available, and κ (or Nt) could not be read directly from the ChIP‐seq data because of the fragment size library and the presence of barrier (rRNA operon) that impede the ParB binding signal at large genomic distance. Thus, given the order of magnitude observed in E. coli (between 10 and 20 bp for the F plasmid and the chromosome, respectively), it is reasonable to assume a same range for V. cholerae, with a = 16 bp corresponding to the footprint of a ParB. In order to conserve the height and peaks of the ChIP‐seq data, we take σ = 25 ± 5 nm and κ = 0.15 ± 0.05. The probability to form a foci is chosen to be PparS1 = 1, PparS2 = 0.9, and PparS3 = 0.6 in order to match the height of each peak observed in ChIP‐seq. These simulations are only semi‐quantitative, in order to show on very general physical ground that the “Nucleation and caging” is able to explain the long range decay observed in ChIP‐seq.

Monte Carlo simulations

The Monte Carlo procedures used an explicit polymer modeled by a Freely Jointed Chain (FJC). The polymer is in contact with a ParB reservoir. To reproduce the confinement of ParB around parS, the particles are not simulated explicitly. Instead, we modeled ParB binding to the polymer with a probability decreasing as a Gaussian function of the locus distance from parS. The Monte Carlo procedures were performed using the following scheme:

  1. Build a Freely Jointed Chain (FJC) of N monomers of size a.

  2. Define a particular site on the FJC as parS (or potentially many parS for V. cholerae) and define a Gaussian distribution of particles Embedded Image.

  3. For each monomer label by an index i, choose a random number ran. If ran < C(r), then a particle is attached to the site i.

  4. Start again at step 1 until the statistics is good enough.

This fast procedure allows to get very good statistics. In Figs 1C–E and 2A, and Appendix Fig S4B, we have analytical expressions for PNC(s), which serve as a benchmark for simulations at the bp resolution. In Fig 4C (xylE::parSF), Appendix Fig S1E (plasmid F), and Appendix Fig S4B (V. cholerae) where we included the average DNA fragments size in the ChIP libraries—as well as three parS sequences for V. cholerae—the analytical expressions were not obvious, we therefore used simulations.

Modeling the ChIP‐seq data with the integration of the average fragments size of the DNA library

The ChIP‐seq assay is based on the sequencing of sonicated fragments (of size ~250 bp) with an intrinsic unknown on the precise location of ParB (footprint of 16 bp). Here, the bioinformatic convention to build the ParB profile is to count +1 read at each bp of the fragments with at least one detected bound ParB. From the simulation perspective, this leads to two averages: (i) over the bound ParB positions, and (ii) over all fragment positions. When a bound ParB is detected, the average over the fragments positions consists in adding to the simulated profile a triangular function centered at the actual ParB position (where it takes the value of 1) and decreasing linearly down to 0 at a genomic distance ± the fragment size.

Correlation analysis

The correlation analyses were performed using the following formula:Embedded Image

Western immunoblotting

The determination of ParBF relative intracellular concentrations and antibody purifications was performed as described (Diaz et al, 2015). When indicated, samples were diluted in DLT1215 extract to keep constant the total amount of proteins.

EMSA and proteins purification

Electro‐mobility shift assay was performed as described (Bouet et al, 2007) in the presence of sonicated salmon sperm DNA as competitor (100 mg ml−1), using 1 nM radiolabeled 144‐bp DNA probe containing a single parSF site generated by PCR. ParBF and ParBF‐3R* proteins were purified using an intein strategy as previously described from plasmids pYAS6 and pYAS25, respectively (Ah‐Seng et al, 2009). ParBVc‐1‐his6 was affinity‐purified in a single step using a 10–1,000 nM imidazole gradient from strain DLT3431 (Castaing et al, 2008).

ParBF and ParBF‐3R* proteins were purified as previously described (Ah‐Seng et al, 2009).

FRAP and FLIM assays

Escherichia coli cells (Stellar) carrying pJYB213 (ParBF‐eGfp), grown in mid‐exponential phase in MGC medium and spotted on microscope slides coated with 1% MGC‐buffered agarose, were subjected to laser‐bleaching. Each field was imaged three times (pre‐bleached step) before photobleaching (at ~2.4 s) a single ParBF focus with a 488 nm laser into two‐foci cells. ROI (region of interest) were 0.2 × 0.2 or 0.3 × 0.3 corresponding to 5 or 9 pixels, respectively. The laser power was set between 67 and 74 Hz to ensure partial bleaching, thus enabling to follow fluorescence recovery on a time scale of second right after photobleaching. Images were taken using an EMCCD camera with a 0.13 μm per pixel resolution (Hamamatsu). To follow recovery dynamics, images were taken every 5, 10, and 20 s up to 38, 108, and 169 s, respectively. Overall, photobleaching in the field of view during the time course of each FRAP experiment (green curves in Fig 6B and Appendix Fig S6A) was averaged from 15 unbleached foci from each field. Normalization to 1 was performed by averaging the focus fluorescence intensity from the three pre‐bleached images.

Data availability

The datasets produced in this study are available in the following databases:

  • Chip‐Seq data for V. cholera: Gene Expression Omnibus GSE114980 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114980)

  • Chip‐Seq data for E. coli: Gene Expression Omnibus GSE115274 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115274)

Acknowledgements

We thank Y. Ah‐Seng for the constructing the ParBF‐3R* allele, the platform GeT‐Biopuces (Genopole, Toulouse) for sequencing experiments and S. Cantaloube and T. Mangeat (LITC‐CBI platform) and C. Reyes for advices and preliminary experiments in microscopy. We are grateful to F. Cornet, P. Rousseau, P. Polard, M. Nollmann, I. Junier, and members of the team for fruitful discussions and critical reading of the manuscript. We thank C. Lesterlin for sharing the F1‐10 plasmid, D. Chattoraj for the anti‐ParBVc1 serum, and Y. Yamaichi for pSM836 and V. cholerae strains. This work was supported by Agence National pour la Recherche (ANR‐14‐CE09‐0025‐01) and the CNRS INPHYNITI program, RD by a PhD grant from Université de Toulouse (APR14), AP, FG, JP, and JCW by the Labex NUMEV (AAP 2013‐2‐005, 2015‐2‐055, 2016‐1‐024).

Author contributions

Conceptualization, J‐YB and AP; Methodology, J‐YB, J‐CW, VAL, and AP; Investigation, RED, J‐CW, AS, JR, and J‐YB; Formal Analysis, J‐CW, JD, FG, JP, and AP; Writing – Original Draft, RED, J‐CW, and J‐YB; Writing – Review & Editing, J‐YB, J‐CW, RED, and AP; Funding Acquisition, J‐YB, AP, JP, and VAL; Resources, DL and FB; Supervision, J‐YB, VAL, and AP.

Conflict of interest

The authors declare that they have no conflict of interest.

Expanded View

Appendix [msb188516-sup-0001-Appendix.pdf]

Expanded View Figures PDF [msb188516-sup-0002-EVFigs.pdf]

Table EV1 [msb188516-sup-0003-TableEV1.docx]

Table EV2 [msb188516-sup-0004-TableEV2.docx]

Funding

Agence Nationale de la Recherche (ANR)http://dx.doi.org/10.13039/501100001665 ANR‐14‐CE09‐0025‐01
Centre National de la Recherche Scientifique (CNRS)http://dx.doi.org/10.13039/501100004794
Université de Toulouse APR14
Labex NUMEV AAP 2013‐2‐0052015‐2‐0552016‐1‐024

References

  1. ↵
    Ah‐Seng Y, Lopez F, Pasta F, Lane D, Bouet JY (2009) Dual role of DNA in regulating ATP hydrolysis by the SopA partition protein. J Biol Chem 284: 30067–30075
    OpenUrlAbstract/FREE Full Text
  2. ↵
    Ah‐Seng Y, Rech J, Lane D, Bouet JY (2013) Defining the role of ATP hydrolysis in mitotic segregation of bacterial plasmids. PLoS Genet 9: e1003956
    OpenUrlCrossRefPubMed
  3. ↵
    Baek JH, Rajagopala SV, Chattoraj DK (2014) Chromosome segregation proteins of Vibrio cholerae as transcription regulators. MBio 5: e01061–01014
    OpenUrl
  4. ↵
    Baxter JC, Funnell BE (2014) Plasmid partition mechanisms. Microbiol Spectr 2: PLAS‐0023‐2014
  5. ↵
    Biek DP, Shi JP (1994) A single 43‐bp sopC repeat of plasmid mini‐F is sufficient to allow assembly of a functional nucleoprotein partition complex. Proc Natl Acad Sci USA 91: 8027–8031
    OpenUrlAbstract/FREE Full Text
  6. ↵
    Bouet JY, Funnell BE (1999) P1 ParA interacts with the P1 partition complex at parS and an ATP‐ADP switch controls ParA activities. EMBO J 18: 1415–1424
    OpenUrlAbstract/FREE Full Text
  7. ↵
    Bouet JY, Surtees JA, Funnell BE (2000) Stoichiometry of P1 plasmid partition complexes. J Biol Chem 275: 8213–8219
    OpenUrlAbstract/FREE Full Text
  8. ↵
    Bouet JY, Rech J, Egloff S, Biek DP, Lane D (2005) Probing plasmid partition with centromere‐based incompatibility. Mol Microbiol 55: 511–525
    OpenUrlCrossRefPubMedWeb of Science
  9. ↵
    Bouet JY, Ah‐Seng Y, Benmeradi N, Lane D (2007) Polymerization of SopA partition ATPase: regulation by DNA binding and SopB. Mol Microbiol 63: 468–481
    OpenUrlCrossRefPubMedWeb of Science
  10. ↵
    Bouet JY, Lane D (2009) Molecular basis of the supercoil deficit induced by the mini‐F plasmid partition complex. J Biol Chem 284: 165–173
    OpenUrlAbstract/FREE Full Text
  11. ↵
    Breier AM, Grossman AD (2007) Whole‐genome analysis of the chromosome partitioning and sporulation protein Spo0J (ParB) reveals spreading and origin‐distal sites on the Bacillus subtilis chromosome. Mol Microbiol 64: 703–718
    OpenUrlCrossRefPubMedWeb of Science
  12. ↵
    Broedersz CP, Wang X, Meir Y, Loparo JJ, Rudner DZ, Wingreen NS (2014) Condensation and localization of the partitioning protein ParB on the bacterial chromosome. Proc Natl Acad Sci USA 111: 8809–8814
    OpenUrlAbstract/FREE Full Text
  13. ↵
    Castaing JP, Bouet JY, Lane D (2008) F plasmid partition depends on interaction of SopA with non‐specific DNA. Mol Microbiol 70: 1000–1011
    OpenUrlCrossRefPubMedWeb of Science
  14. ↵
    Collins J, Pritchard RH (1973) Relationship between chromosome replication and F'lac episome replication in Escherichia coli. J Mol Biol 78: 143–155
    OpenUrlCrossRefPubMed
  15. ↵
    Cornet F, Mortier I, Patte J, Louarn JM (1994) Plasmid pSC101 harbors a recombination site, psi, which is able to resolve plasmid multimers and to substitute for the analogous chromosomal Escherichia coli site dif. J Bacteriol 176: 3188–3195
    OpenUrlAbstract/FREE Full Text
  16. ↵
    Das N, Chattoraj DK (2004) Origin pairing (‘handcuffing’) and unpairing in the control of P1 plasmid replication. Mol Microbiol 54: 836–849
    OpenUrlCrossRefPubMedWeb of Science
  17. ↵
    Datsenko KA, Wanner BL (2000) One‐step inactivation of chromosomal genes in Escherichia coli K‐12 using PCR products. Proc Natl Acad Sci USA 97: 6640–6645
    OpenUrlAbstract/FREE Full Text
  18. ↵
    Diaz R, Rech J, Bouet JY (2015) Imaging centromere‐based incompatibilities: insights into the mechanism of incompatibility mediated by low‐copy number plasmids. Plasmid 80: 54–62
    OpenUrl
  19. ↵
    Diaz RE, Sanchez A, Anton Le Berre V, Bouet JY (2017) High‐resolution chromatin immunoprecipitation: ChIP‐sequencing. In The bacterial nucleoid methods in molecular biology, Espéli O (ed.), Vol. 1624, pp 61–73. New York, NY: Humana Press
  20. ↵
    Donczew M, Mackiewicz P, Wrobel A, Flardh K, Zakrzewska‐Czerwinska J, Jakimowicz D (2016) ParA and ParB coordinate chromosome segregation with cell elongation and division during Streptomyces sporulation. Open Biol 6: 150263
    OpenUrlAbstract/FREE Full Text
  21. ↵
    Fisher GL, Pastrana CL, Higman VA, Koh A, Taylor JA, Butterer A, Craggs T, Sobott F, Murray H, Crump MP, Moreno‐Herrero F, Dillingham MS (2017) The structural basis for dynamic DNA binding and bridging interactions which condense the bacterial centromere. eLife 6: e28086
    OpenUrl
  22. ↵
    Fung E, Bouet JY, Funnell BE (2001) Probing the ATP‐binding site of P1 ParA: partition and repression have different requirements for ATP binding and hydrolysis. EMBO J 20: 4901–4911
    OpenUrlAbstract/FREE Full Text
  23. ↵
    Funnell BE, Gagnier L (1993) The P1 plasmid partition complex at parS: II. Analysis of ParB protein binding activity and specificity. J Biol Chem 268: 3616–3624
    OpenUrlAbstract/FREE Full Text
  24. ↵
    Funnell BE (2016) ParB partition proteins: complex formation and spreading at bacterial and plasmid centromeres. Front Mol Biosci 3: 44
    OpenUrl
  25. ↵
    de Gennes PG (1979) Scaling concept in polymer physics. Ithaca, NY: Cornell University Press
  26. ↵
    Gerdes K, Moller‐Jensen J, Bugge Jensen R (2000) Plasmid and chromosome partitioning: surprises from phylogeny. Mol Microbiol 37: 455–466
    OpenUrlCrossRefPubMedWeb of Science
  27. ↵
    Graham TG, Wang X, Song D, Etson CM, van Oijen AM, Rudner DZ, Loparo JJ (2014) ParB spreading requires DNA bridging. Genes Dev 28: 1228–1238
    OpenUrlAbstract/FREE Full Text
  28. ↵
    Hanai R, Liu RP, Benedetti P, Caron PR, Lynch AS, Wang JC (1996) Molecular dissection of a protein SopB essential for Escherichia coli F plasmid partition. J Biol Chem 271: 17469–17475
    OpenUrlAbstract/FREE Full Text
  29. ↵
    Helsberg M, Eichenlaub R (1986) Twelve 43‐base‐pair repeats map in a cis‐acting region essential for partition of plasmid mini‐F. J Bacteriol 165: 1043–1045
    OpenUrlAbstract/FREE Full Text
  30. ↵
    Herring CD, Raffaelle M, Allen TE, Kanin EI, Landick R, Ansari AZ, Palsson BO (2005) Immobilization of Escherichia coli RNA polymerase and location of binding sites by use of chromatin immunoprecipitation and microarrays. J Bacteriol 187: 6166–6174
    OpenUrlAbstract/FREE Full Text
  31. ↵
    Hu L, Vecchiarelli AG, Mizuuchi K, Neuman KC, Liu J (2017) Brownian Ratchet mechanism for faithful segregation of low‐copy‐number plasmids. Biophys J 112: 1489–1502
    OpenUrl
  32. ↵
    Hwang LC, Vecchiarelli AG, Han YW, Mizuuchi M, Harada Y, Funnell BE, Mizuuchi K (2013) ParA‐mediated plasmid partition driven by protein pattern self‐organization. EMBO J 32: 1238–1249
    OpenUrlAbstract/FREE Full Text
  33. ↵
    Kumar M, Mommer MS, Sourjik V (2010) Mobility of cytoplasmic, membrane, and DNA‐binding proteins in Escherichia coli. Biophys J 98: 552–559
    OpenUrlCrossRefPubMedWeb of Science
  34. ↵
    Lagage V, Boccard F, Vallet‐Gely I (2016) Regional control of chromosome segregation in Pseudomonas aeruginosa. PLoS Genet 12: e1006428
    OpenUrlCrossRef
  35. ↵
    Le Gall A, Cattoni DI, Guilhas B, Mathieu‐Demaziere C, Oudjedi L, Fiche JB, Rech J, Abrahamsson S, Murray H, Bouet JY, Nollmann M (2016) Bacterial partition complexes segregate within the volume of the nucleoid. Nat Commun 7: 12107
    OpenUrlCrossRefPubMed
  36. ↵
    Lemonnier M, Bouet JY, Libante V, Lane D (2000) Disruption of the F plasmid partition complex in vivo by partition protein SopA. Mol Microbiol 38: 493–505
    OpenUrlCrossRefPubMedWeb of Science
  37. ↵
    Lim HC, Surovtsev IV, Beltran BG, Huang F, Bewersdorf J, Jacobs‐Wagner C (2014) Evidence for a DNA‐relay mechanism in ParABS‐mediated chromosome segregation. eLife 3: e02758
    OpenUrlCrossRefPubMed
  38. ↵
    Lin DCH, Grossman AD (1998) Identification and characterization of a bacterial chromosome partitioning site. Cell 92: 675–685
    OpenUrlCrossRefPubMedWeb of Science
  39. ↵
    Lobocka M, Yarmolinsky M (1996) P1 plasmid partition: a mutational analysis of ParB. J Mol Biol 259: 366–382
    OpenUrlCrossRefPubMedWeb of Science
  40. ↵
    Lynch AS, Wang JC (1995) SopB protein‐meditated silencing of genes linked to the sopC locus of Escherichia coli F plasmid. Proc Natl Acad Sci USA 92: 1896–1900
    OpenUrlAbstract/FREE Full Text
  41. ↵
    Meyer AS, Grainger DC (2013) The Escherichia coli nucleoid in stationary phase. Adv Appl Microbiol 83: 69–86
    OpenUrlCrossRefPubMed
  42. ↵
    Miller JH (1972) Experiments in molecular genetics. Cold Spring Harbour, NY: Cold Spring Harbour Laboratory Press
  43. ↵
    Mori H, Kondo A, Ohshima A, Ogura T, Hiraga S (1986) Structure and function of the F plasmid genes essential for partitioning. J Mol Biol 192: 1–15
    OpenUrlCrossRefPubMedWeb of Science
  44. ↵
    Murray H, Ferreira H, Errington J (2006) The bacterial chromosome segregation protein Spo0J spreads along DNA from parS nucleation sites. Mol Microbiol 61: 1352–1361
    OpenUrlCrossRefPubMedWeb of Science
  45. ↵
    Murray H, Errington J (2008) Dynamic control of the DNA replication initiation protein DnaA by Soj/ParA. Cell 135: 74–84
    OpenUrlCrossRefPubMedWeb of Science
  46. ↵
    Parry BR, Surovtsev IV, Cabeen MT, O'Hern CS, Dufresne ER, Jacobs‐Wagner C (2014) The bacterial cytoplasm has glass‐like properties and is fluidized by metabolic activity. Cell 156: 183–194
    OpenUrlCrossRefPubMedWeb of Science
  47. ↵
    Phillips RB, Kondev J, Theriot J, Garcia H (2012) Physical biology of the cell. New York, NY: Garland Science
  48. ↵
    Pillet F, Sanchez A, Lane D, Anton Leberre V, Bouet JY (2011) Centromere binding specificity in assembly of the F plasmid partition complex. Nucleic Acids Res 39: 7477–7486
    OpenUrlCrossRefPubMedWeb of Science
  49. ↵
    Rodionov O, Lobocka M, Yarmolinsky M (1999) Silencing of genes flanking the P1 plasmid centromere. Science 283: 546–549
    OpenUrlAbstract/FREE Full Text
  50. ↵
    Saint‐Dic D, Frushour BP, Kehrl JH, Kahng LS (2006) A parA homolog selectively influences positioning of the large chromosome origin in Vibrio cholerae. J Bacteriol 188: 5626–5631
    OpenUrlAbstract/FREE Full Text
  51. ↵
    Sanchez A, Rech J, Gasc C, Bouet JY (2013) Insight into centromere‐binding properties of ParB proteins: a secondary binding motif is essential for bacterial genome maintenance. Nucleic Acids Res 41: 3094–3103
    OpenUrlCrossRefPubMedWeb of Science
  52. ↵
    Sanchez A, Cattoni DI, Walter JC, Rech J, Parmeggiani A, Nollmann M, Bouet JY (2015) Stochastic self‐assembly of ParB proteins builds the bacterial DNA segregation apparatus. Cell Syst 1: 163–173
    OpenUrl
  53. ↵
    Schiessel H (2013) Biophysics for beginners: a journey through the cell nucleus. Singapore: Pan Stanford Publishing
  54. ↵
    Scholefield G, Whiting R, Errington J, Murray H (2011) Spo0J regulates the oligomeric state of Soj to trigger its switch from an activator to an inhibitor of DNA replication initiation. Mol Microbiol 79: 1089–1100
    OpenUrlCrossRefPubMedWeb of Science
  55. ↵
    Schumacher MA, Tonthat NK, Lee J, Rodriguez‐Castaneda FA, Chinnam NB, Kalliomaa‐Sanford AK, Ng IW, Barge MT, Shaw PL, Barilla D (2015) Structures of archaeal DNA segregation machinery reveal bacterial and eukaryotic linkages. Science 349: 1120–1124
    OpenUrlAbstract/FREE Full Text
  56. ↵
    Song D, Rodrigues K, Graham TG, Loparo JJ (2017) A network of cis and trans interactions is required for ParB spreading. Nucleic Acids Res 45: 7106–7117
    OpenUrl
  57. ↵
    Taylor JA, Pastrana CL, Butterer A, Pernstich C, Gwynn EJ, Sobott F, Moreno‐Herrero F, Dillingham MS (2015) Specific and non‐specific interactions of ParB with DNA: implications for chromosome segregation. Nucleic Acids Res 43: 719–731
    OpenUrlCrossRefPubMed
  58. ↵
    Vecchiarelli AG, Mizuuchi K, Funnell BE (2012) Surfing biological surfaces: exploiting the nucleoid for partition and transport in bacteria. Mol Microbiol 86: 513–523
    OpenUrlCrossRefPubMed
  59. ↵
    Walter JC, Dorignac J, Lorman V, Rech J, Bouet JY, Nollmann M, Palmeri J, Parmeggiani A, Geniet F (2017) Surfing on protein waves: proteophoresis as a mechanism for bacterial genome partitioning. Phys Rev Lett 119: 028101
    OpenUrl
  60. ↵
    Walter JC, Walliser NO, David G, Dorignac J, Geniet F, Palmeri J, Parmeggiani A, Wingreen NS, Broedersz CP (2018) Looping and clustering model for the organization of protein‐DNA complexes on the bacterial genome. New J Phys 20: 035002
    OpenUrl
  61. ↵
    Yamaichi Y, Niki H (2000) Active segregation by the Bacillus subtilis partitioning system in Escherichia coli. Proc Natl Acad Sci USA 97: 14656–14661
    OpenUrlAbstract/FREE Full Text

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Volume 14, Issue 11
01 November 2018
Molecular Systems Biology: 14 (11)
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