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Cell Cycle Constraints and Environmental Control of Local DNA Hypomethylation in α-Proteobacteria

Cell Cycle Constraints and Environmental Control of Local DNA Hypomethylation in α-Proteobacteria

  • Silvia Ardissone, 
  • Peter Redder, 
  • Giancarlo Russo, 
  • Antonio Frandi, 
  • Coralie Fumeaux, 
  • Andrea Patrignani, 
  • Ralph Schlapbach, 
  • Laurent Falquet, 
  • Patrick H. Viollier
PLOS
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Abstract

Heritable DNA methylation imprints are ubiquitous and underlie genetic variability from bacteria to humans. In microbial genomes, DNA methylation has been implicated in gene transcription, DNA replication and repair, nucleoid segregation, transposition and virulence of pathogenic strains. Despite the importance of local (hypo)methylation at specific loci, how and when these patterns are established during the cell cycle remains poorly characterized. Taking advantage of the small genomes and the synchronizability of α-proteobacteria, we discovered that conserved determinants of the cell cycle transcriptional circuitry establish specific hypomethylation patterns in the cell cycle model system Caulobacter crescentus. We used genome-wide methyl-N6-adenine (m6A-) analyses by restriction-enzyme-cleavage sequencing (REC-Seq) and single-molecule real-time (SMRT) sequencing to show that MucR, a transcriptional regulator that represses virulence and cell cycle genes in S-phase but no longer in G1-phase, occludes 5’-GANTC-3’ sequence motifs that are methylated by the DNA adenine methyltransferase CcrM. Constitutive expression of CcrM or heterologous methylases in at least two different α-proteobacteria homogenizes m6A patterns even when MucR is present and affects promoter activity. Environmental stress (phosphate limitation) can override and reconfigure local hypomethylation patterns imposed by the cell cycle circuitry that dictate when and where local hypomethylation is instated.

Author Summary

DNA methylation is the post-replicative addition of a methyl group to a base by a methyltransferase that recognise a specific sequence, and represents an epigenetic regulatory mechanism in both eukaryotes and prokaryotes. In microbial genomes, DNA methylation has been implicated in gene transcription, DNA replication and repair, nucleoid segregation, transposition and virulence of pathogenic strains. CcrM is a conserved, cell cycle regulated adenine methyltransferase that methylates GANTC sites in α-proteobacteria. N6-methyl-adenine (m6A) patterns generated by CcrM can change the affinity of a given DNA-binding protein for its target sequence, and therefore affect gene expression. Here, we combine restriction enzyme cleavage-deep sequencing (REC-Seq) with SMRT sequencing to identify hypomethylated 5’-GANTC-3’ (GANTCs) in α-proteobacterial genomes instated by conserved cell cycle factors. By comparing SMRT and REC-Seq data with chromatin immunoprecipitation-deep sequencing data (ChIP-Seq) we show that a conserved transcriptional regulator, MucR, induces local hypomethylation patterns by occluding GANTCs from the CcrM methylase and we provide evidence that this competition occurs during S-phase, but not in G1-phase cells. Furthermore, we find that environmental signals (such as phosphate depletion) are superimposed to the cell cycle control mechanism and can override the specific hypomethylation pattern imposed by the cell cycle transcriptional circuitry.

Introduction

DNA methylation is a conserved epigenetic modification that occurs from bacteria to humans and is implicated in control of transcription, DNA replication/repair, innate immunity and pathogenesis [1, 2]. Originally described as a mechanism that protects bacteria from invading foreign (viral) DNA [3], methyl-N6-adenine (m6A) modifications are thought to direct infrequent and stochastic phenotypic heterogeneity in bacterial cells [4, 5] and were recently implicated in transcriptional control of lower eukaryotic genomes and silencing in mouse embryonic stem cells [68].

How local changes in methylation are instated during the cell cycle remains poorly explored, even in γ-proteobacteria such as Escherichia coli and Salmonella enterica, as cell cycle studies on cell populations are cumbersome and require genetic manipulation [9]. Moreover, the replication regulator SeqA that controls the methylation state by preferentially binding hemi-methylated sequences is only encoded in γ-proteobacteria, suggesting that other mechanisms are likely operational in other systems [9, 10]. Model systems in which cell populations can be synchronized without genetic intervention are best suited to illuminate the interplay between methylation and cell cycle [11, 12]. The fresh-water bacterium Caulobacter crescentus and more recently the plant symbiont Sinorhizobium meliloti that reside in distinct environmental niches are such cell cycle model systems [13]. Akin to other α-proteobacteria, C. crescentus and S. meliloti divide asymmetrically into a smaller G1-phase cell and a larger S-phase cell and use conserved transcriptional regulators arranged in modules to coordinate transcription with cell cycle progression [1316] (Fig 1A). In C. crescentus, MucR1 and MucR2 were recently shown to negatively regulate numerous promoters that are activated by the cell cycle transcriptional regulator A (CtrA) in G1-phase. MucR orthologs control virulence functions in α-proteobacterial pathogens and symbionts, but can also control cell cycle-regulated promoters in C. crescentus [1720]. MucR1/2 target promoters by way of an ancestral zinc finger-like fold and both proteins are present throughout the C. crescentus cell cycle [17, 21, 22] (Fig 1A). By contrast, the OmpR-like DNA-binding response regulator CtrA is activated by phosphorylation and is only present in G1 and late S-phase cells [23, 24], but not in early S-phase cells (Fig 1A). The promoter controlling expression of the conserved DNA methyltransferase CcrM is among the targets activated by phosphorylated CtrA (CtrA~P) in late S-phase [15, 17, 2527]. CcrM introduces m6A marks at sites harbouring the recognition sequence 5’-GANTC-3’ (henceforth GANTCs) once passage of the DNA replication fork leaves GANTCs hemi-methylated (Fig 1B). CcrM is an unstable protein degraded by the ATP-dependent protease Lon throughout the cell cycle [28, 29]. Since the ccrM gene is expressed only in late S-phase cells, the time of expression dictates when the unstable CcrM protein is present during the cell cycle. CcrM no longer cycles when it is expressed from a constitutive promoter in otherwise WT cells or when Lon is inactivated [28, 30].

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Fig 1. Regulation of C. crescentus cell cycle and methylation of the chromosome.

(A) Schematic of the C. crescentus cell cycle and the regulatory interactions that control G1-phase promoters. Transcription from G1-phase promoters is activated by phosphorylated CtrA (CtrA~P, in blue) and repressed by MucR1/2. CtrA~P accumulates in pre-divisional and swarmer (SW) cells, and is eliminated by regulated proteolysis in the stalked (ST) cell upon compartmentalization and at the swarmer to stalked cell differentiation. The red bar indicates that MucR1/2 proteins are present at all stages of the cell cycle. (B) Schematic of the chromosome replication and adenine methylation (m6A) during the C. crescentus cell cycle. Chromosome replication and methylation are shown for the same cell cycle stages depicted in panel A. In non-replicative SW cells (T10) the chromosome is methylated on both strands. Upon differentiation into ST cells (T40), chromosome replication start from the origin (blue star) located at the old cell pole; progression of the replisomes (orange dots) generates hemi-methylated chromosomes (black and green lines represent respectively the methylated and unmethylated strand; T70 and T100). The cell cycle-regulated adenine methyltransferase CcrM is synthesized only in late pre-divisional cells, where it methylates the newly synthesized chromosome strands. CcrM is then specifically proteolyzed by the Lon protease. The purple numbers indicate the position along the chromosome of the P169 (1), P1149 (2) and P2901 (3) promoters. T10, T40, T70 and T100 indicate the time after synchronization at which the samples for anti-MucR1 ChIP-Exo were taken (see text).

https://doi.org/10.1371/journal.pgen.1006499.g001

With the advent of SMRT (single-molecule real-time) sequencing it is now possible to obtain m6A-methylome information of bacterial genomes at single base pair resolution [31, 32]. A recent cell cycle methylome analysis of C. crescentus by SMRT-sequencing revealed the large majority of GANTCs switch from hemi-methylated to a full methylated state (m6A-marked GANTCs on both strands) at the onset of CcrM expression [12]. Interestingly, a few sites were consistently hypomethylated, indicating that site-specific mechanisms control local hypomethylation patterns. Local hypomethylation patterns may arise if specific DNA-binding proteins and/or restricted local chromosome topology block access of CcrM to such GANTCs. Here, we combine restriction enzyme cleavage-deep sequencing (REC-Seq) with SMRT sequencing to unearth hypomethylated GANTCs in the genomes of wild type (WT) and mutant C. crescentus and S. meliloti. We show that the conserved transcriptional regulator MucR induces local m6A-hypomethylation by preventing CcrM from accessing GANTCs during S-phase, but only when CcrM cycles. Since repression of MucR target promoters is normally overcome in G1-phase, our data suggest that MucR is unable to shield GANTCs when CcrM is artificially present in G1 cells. Lastly, we discovered that phosphate starvation promotes methylation of specific MucR-shielded GANTCs, revealing an environmental override of the control system that normally instates local hypomethylation patterns during the cell cycle.

Results

Identification and analysis of hypomethylated GANTCs by restriction enzyme cleavage (REC-Seq)

Detection of hypomethylated sites by SMRT-sequencing requires sufficient sequencing depth and sophisticated bioinformatic analysis to differentiate unmethylated GANTCs from methylated ones. Since unmethylated GANTCs can be conveniently enriched for in C. crescentus by restriction enzyme cleavage using the HinfI restriction enzyme (which only cleaves unmethylated GANTCs) [33], we sought to apply HinfI-based cleavage followed by Illumina-based deep-sequencing (REC-Seq) to identify hypomethylated GANTCs, similar to a previous procedure used for analysis of hypomethylated m6A sites in the unrelated γ-proteobacterium Vibrio cholerae [34]. We tested REC-Seq on HinfI-treated genomic DNA (gDNA) from C. crescentus and, following bioinformatic filtering, obtained a list of unprotected GANTCs scaling with HinfI cleavage efficiency (“score” in S1 Table). Since nearly all GANTCs suggested to be consistently unmethylated by SMRT sequencing [12] are represented as high scoring GANTCs in the REC-Seq (note the growth conditions or limited SMRT sequencing depth may explain the differences), we concluded that REC-Seq captures hypomethylated GANTCs in scaling manner (see below where selected sites cleaved in WT are no longer cleaved in the ΔmucR1/2 mutant). Since CcrM also methylates GANTCs in other α-proteobacteria [35, 36], we also determined the hypomethylated GANTCs on the multipartite genome of S. meliloti [37] by HinfI REC-Seq and found such hypomethylated sites on the chromosome and both megaplasmids (S1 Table).

To validate the HinfI REC-Seq approach, we conducted REC-Seq (using the methylation-sensitive MboI restriction enzyme) on gDNA from Escherichia coli K12 and V. cholerae, as previously determined either by SMRT sequencing or REC-Seq [10, 34]. The Dam methylase introduces m6A marks at GATCs in many γ-proteobacterial genomes [4] that protect from cleavage by MboI. As known unmethylated sites in these control experiments indeed emerged with high score (S2 Table), we conclude that HinfI REC-Seq is an efficient method to detect and quantitate GANTCs that escape methylation by CcrM.

Several hypomethylated GANTCs in C. crescentus are MucR target sites

Having identified hypomethylated GANTCs in the C. crescentus genome by HinfI REC-Seq, we noted that many high scoring GANTCs lie in regions that are occupied by MucR1/2 as determined by previous chromatin-immunoprecipitation deep-sequencing (ChIP-Seq) analysis [17]. Of the hits with a score higher than 100, one third lie in MucR1/2 target sequences, and the proportion is even higher (50%) in the case of the 50 top hits (Table 1 and S1 Table). To test if MucR1/2 occludes these GANTCs from methylation by CcrM, we conducted HinfI-cleavage analysis of gDNA from WT (NA1000) and ΔmucR1/2 double mutant by qPCR (henceforth HinfI-qPCR assay) at six MucR1/2 target sites. The CCNA_00169 promoter (henceforth P169) contains four GANTCs; the CCNA_02901 promoter (P2901), the CCNA_01149 promoter (P1149) and the CCNA_01083 internal sequence contain two GANTCs each; the CCNA_02830 and CCNA_03248 promoters (P2830 and P3248) carry one GANTC each (Fig 2A). A high percentage (100%) of methylation in the HinfI-qPCR assay indicates that HinfI cannot cleave this site because of prior methylation by CcrM, whereas a low percentage reflects efficient cleavage of the non-methylated DNA by HinfI. In WT gDNA these six MucR1/2-target sequences are almost completely cleaved by HinfI, indicating that the GANTCs are hypomethylated in the presence of MucR1/2. However, these sites are methylated and therefore not cleaved by HinfI in ΔmucR1/2 cells (Fig 2B). As control for the specificity of the HinfI-qPCR assay we conducted the same analysis on sequences that are not MucR1/2 targets harbouring either i) a hypomethylated GANTC (PnagA), ii) several methylated GANTCs (PpodJ) or iii) a control sequence that does not contain GANTCs (PxylX). These controls revealed a level of amplification in the HinfI-qPCR assay as predicted (S1A Fig) and showed no difference between WT and ΔmucR1/2 cells. Thus, only hypomethylated sequences that are bound by MucR1/2 in vivo are converted to methylated GANTCs in the absence of MucR1/2.

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Table 1. REC-Seq in WT and ΔmucR1/2.

GANTC sites with the highest (top 50) REC-Seq score in WT C. crescentus are listed. The complete list of REC-Seq data for both WT and ΔmucR1/2 strains is reported in S1 Table.

https://doi.org/10.1371/journal.pgen.1006499.t001

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Fig 2. MucR occludes specific GANTC sites from methylation.

(A) Schematic of the loci carrying hypomethylated GANTCs occluded by MucR. The position of the hypomethylated GANTCs identified by Kozdon et al. [12] is indicated by purple asterisks. Red lines represent the occupancy of MucR1 and the values (x104 per-base coverage) calculated by the super-resolution bioinformatic approach represent the average of the four time points (T10, T40, T70 and T100, as described in the Methods). The MucR-dependent transcription start sites, determined by TSS-EMOTE, are indicated by black (sense) and green (antisense) arrows. Dashed arrows indicate transcription start sites found in both WT and ΔmucR1/2 strains (CCNA_01083-CCNA_01084) or down-regulated in the ΔmucR1/2 strain compared to the WT (promoter of CCNA_02831). (B) Methylation percentage of the loci shown in panel A in the WT and ΔmucR1/2 strains, as determined by HinfI-qPCR.

https://doi.org/10.1371/journal.pgen.1006499.g002

SMRT sequencing of WT and ΔmucR1/2 gDNA supported the result that these GANTCs carry m6A marks as inferred by a high characteristic interpulse-duration (IPD) ratio observed in ΔmucR1/2 versus WT cells (S3 Table). Interestingly, this analysis also revealed eleven GANTCs with the inverse behaviour, i.e. a low IPD ratio in ΔmucR1/2 versus WT cells, suggesting that they no longer carry m6A marks in the absence of MucR1/2. To confirm this result we conducted HinfI-qPCR assays at two of these GANTCs: the CCNA_01248 promoter (P1248) and the CCNA_03426 promoter (P3426). As predicted by the methylome analysis, we observed that the methylation percentage of these GANTCs was reduced in ΔmucR1/2 versus WT (S1B Fig). On the basis of these experiments, we conclude that MucR1/2 prevents m6A-methylation by CcrM at several MucR1/2-target sequences, but can also facilitate methylation at other sites. This would likely occur by an indirect mechanism involving other MucR-dependent DNA-binding proteins that compete with CcrM at certain GANTCs.

To obtain a global picture of hypomethylated GANTCs in the absence of MucR1/2, we conducted REC-Seq analysis on gDNA extracted from the ΔmucR1/2 strain (Table 1 and S1 Table). Comparison of the REC-Seq data for WT and ΔmucR1/2 cells (S2 Fig) supported the conclusion that binding of MucR1/2 prevents methylation by CcrM, as the GANTCs tested by HinfI-qPCR (shown in Fig 2) have a high REC-Seq score in WT and a low REC-Seq score (or they are not detected) in the ΔmucR1/2 strain. Moreover, most of the GANTCs that show a strong decrease in score between WT and ΔmucR1/2 cells are also lying in regions directly bound by MucR1/2 (Table 1 and S1 Table), based on ChIP-Exo (S4 Table) and published ChIP-Seq data [17].

Conditions that impair local GANTC hypomethylation by MucR1/2

Since CcrM is restricted to late S-phase and MucR1/2-repression is overcome in G1-phase [17, 28], we tested if MucR1/2-bound GANTCs are still hypomethylated when CcrM no longer cycles. To this end we used two strains: the Δlon::Ω (henceforth lon) mutant, as the Lon protease is responsible for degradation of CcrM throughout the cell cycle and upon inactivation of Lon the CcrM protein accumulates also in G1-cells, although it is only synthesized in S-phase [28, 29], and a strain with a second copy of the ccrM gene under control of the constitutive Plac promoter (integrated at the ccrM locus, ccrM::Plac-ccrM) [30, 33]. Indeed, HinfI-qPCR analysis revealed that the fraction of methylated P169, P1149 and P2901 GANTCs increases in lon and ccrM::Plac-ccrM strain relative to WT cells (Fig 3A).

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Fig 3. Hypomethylation by MucR is impaired in G1-phase cells.

(A) Methylation percentage of the P169, P1149 and P2901 sequences in strains in which the methyltransferase CcrM is stabilised. The HinfI-qPCR analysis indicates that methylation is increased in cells carrying Plac-ccrM or the lon mutation. In the case of the lon mutant, the methylation of P169, P1149 and P2901 is not affected by increased levels of MucR1 [R1: Pvan-mucR1, R1 long: N-terminally extended dominant-negative MucR1 variant expressed from Pvan on pMT335]. (B) Methylation percentage of the P169, P1149 and P2901 sequences in WT cells that constitutively express ccrM or heterologous GANTC-methylases from Pvan on pMT335 (TA, Thermoplasma acidophilum; HP, Helicobacter pylori; hinf, Haemophilus influenzae). (C) Immunoblot showing the steady-state levels of CcrM, MucR1 and MucR2 in WT cells as well as the Plac-ccrM, lon and ΔmucR1/2 strains. Molecular size standards are indicated on the right as blue lines with the corresponding values in kDa. (D) Genome-wide occupancy of MucR1 in synchronised WT cells at four different time points as determined by ChIP-Exo and super-resolution bioinformatic analysis: swarmer (T10), stalked (T40), early pre-divisional (T70) and late pre-divisional cells (T100). The x axis represents the nucleotide position on the genome, whereas the y axis shows the enrichment ratio (E.R.) for each promoter region as reported in the S4 Table (see Methods for a detailed description). (E) Heat map of the enrichment ratios of selected loci (those that contain hypomethylated GANTCs occluded by MucR, see Fig 2) at the four time points after synchronization. The pilA locus is shown as comparison as it is a well-characterized target of MucR [17]. The heat map shows that MucR1 is constitutively associated with these loci.

https://doi.org/10.1371/journal.pgen.1006499.g003

To exclude that constitutive presence of CcrM simply prevents MucR binding to DNA because CcrM outnumbers and therefore outcompetes MucR, we conducted several control experiments to demonstrate the specificity of the methylation control at these GANTCs. First, immunoblotting experiments revealed that MucR1/2 levels were maintained in the lon and ccrM::Plac-ccrM strains compared to WT (Fig 3C). Second, overexpression of either WT MucR1 or of an N-terminally extended (dominant-negative) MucR1 variant from Pvan on a high copy plasmid (pMT335) [17, 38] did not prevent methylation of P169, P1149 and P2901 GANTCs in lon mutant cells (Fig 3A) or alter CcrM steady-state levels (S1C Fig). Conversely, constitutive expression of CcrM from the same vector (pMT335) in WT cells recapitulated the effect on methylation of the P169, P1149 and P2901 GANTCs (Fig 3B). Similarly, methylases of Thermoplasma acidophilum (TA), Helicobacter pylori (HP) or Haemophilus influenzae (Hinf), which also specifically methylate GANTCs but are not related to α-proteobacterial CcrM, also lead to methylation of these hypomethylated GANTCs when expressed from pMT335 (Fig 3B). By contrast, the methylation state of GANTCs at the parS locus was not significantly altered by the expression of the methylases or by the lon mutation (S1D Fig). On the basis that CcrM and unrelated methylases are able to compete against MucR1/2 for methylation of P169, P1149 and P2901 GANTCs when expressed constitutively, we hypothesize that MucR1/2 no longer efficiently compete with CcrM in G1-phase when both proteins are present at this time (Fig 3A and 3B).

To test if MucR1 binds to its targets in G1-phase, we conducted chromatin-immunoprecipitation-followed by deep-sequencing of exonuclease treated fragments (ChIP-Exo), a technique with enhanced resolution compared to conventional ChIP-Seq [39]. We treated with the anti-MucR1 antibody chromatin prepared from synchronized cells at four different time points after synchronization [10 min (T10, G1 phase), 40 min (T40, G1-to-S transition), 70 min (T70, early S-phase), 100 min (T100, late S-phase) (Fig 1B)] and used a bioinformatic algorithm to define the binding sites at super-resolution (see Methods and [40]). Surprisingly, the binding profiles at the four time points appeared to be nearly congruent (Fig 3D) and quantification of the enrichment ratio failed to reveal major changes of MucR1 binding to its targets during the cell cycle (Fig 3E and S4 Table). On the other hand, conformational changes or altered dynamics of binding (i.e. dissociation constants, on- and off-rates) that are undetectable by our methods might allow transcription from the MucR-bound promoters in G1-phase. Transient release of DNA by MucR1/2 or changes in chromatin conformation could provide access to competing DNA binding proteins such as CcrM, RNA polymerase (RNAP) and other transcription factors (like CtrA) in G1-phase to induce methylation or firing of the MucR1/2 target promoters.

MucR-dependent hypomethylation regulates sense and anti-sense transcription

As MucR1/2 regulates the methylation state of the aforementioned GANTCs, we wondered if the MucR1/2-targets P169, P1149 and P2901 display promoter activity in a MucR1/2-dependent and/or methylation-dependent manner. To this end, we conducted LacZ (β-galactosidase)-based promoter probe assays of P169-, P1149- and P2901-lacZ transcriptional reporters (driving expression of a promoterless lacZ gene) in WT and ΔmucR1/2 cells and observed that LacZ activity of all reporters was elevated in ΔmucR1/2 cells versus WT (Fig 4A–4C). The increase was less dramatic for P169-lacZ (156 ± 5.8% relative to WT) than for P1149- and P2901-lacZ (439 ± 7.4 and 385 ± 40%, respectively). We then asked if promoter activity is augmented when cycling of CcrM is prevented. Indeed, the P169-, P1149- and P2901-lacZ reporters indicated an increase in promoter activity in the lon mutant and Plac-ccrM strains compared to WT (Fig 4D). Importantly, no increase in LacZ activity was observed in lon and Plac-ccrM strains with other promoters (PhvyA and PpilA, Fig 4D) that are bound by MucR1/2 and whose activity is increased in ΔmucR1/2 cells [17, 41] but contain no hypomethylated GANTCs. We further corroborated these results by showing that constitutive expression of C. crescentus CcrM or the T. acidophilum GANTC-methylase from Pvan on pMT335 led to an increase in P169-, P1149- and P2901-lacZ promoter activity (Fig 4E). Consistent with the fact that in ΔmucR1/2 cells these promoters are no longer hypomethylated, constitutive expression of CcrM from Plac-ccrM in ΔmucR1/2 cells had no significant effect on P169-, P1149- and P2901-lacZ promoter activity (Fig 4E).

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Fig 4. MucR and methylation by CcrM regulate transcription from target promoters.

Beta-galactosidase activity of P169 (WT promoter) and P169* (with all GANTCs mutated to GTNTCs) (A), P2901 (WT promoter) and P2901* (with the two GANTCs mutated to GTNTCs) (B) and P1149 (C) in WT and ΔmucR1/2 cells. Mutation of MucR1/2 increases expression from P169-, P2901- and P1149-lacZ independently from the presence of GANTCs. Values are expressed as percentages (activity of WT promoter in WT cells set at 100%). (D) Beta-galactosidase activity of P169-, P1149-, P2901-lacZ promoter probe constructs and two MucR-dependent control promoter reporters (PhvyA-lacZ and PpilA-lacZ) in WT and cells that constitutively express ccrM (ccrM::Plac-ccrM or Δlon::Ω). Methylation of the target promoters by CcrM increases the LacZ activity. Values are expressed as percentages (activity in WT cells set at 100%). (E) Beta-galactosidase activity of P169-, P1149- and P2901-lacZ in WT cells that constitutively express ccrM or a heterologous GANTC-methylase from T. acidophilum (TA) on plasmid under control of Pvan. Values are expressed as percentages (activity in WT carrying the empty vector set at 100%). (F) Beta-galactosidase activity of P169-, P1149- and P2901-lacZ in WT and ΔmucR1/2 cells that constitutively express ccrM (ccrM::Plac-ccrM). Values are expressed as percentages (activity in WT cells set at 100%).

https://doi.org/10.1371/journal.pgen.1006499.g004

To determine if changing the GANTC methylation state (by mutation to GTNTC) in P169- and P2901-lacZ (5 sites mutated for P169, P169*; 2 sites for P2901, P2901*) also affects promoter activity, we measured LacZ activity of the mutant promoters in WT and ΔmucR1/2 cells and found that they still exhibited MucR1/2-dependency, as the P169*- and P2901*-lacZ were still strongly de-repressed in the absence of MucR1/2 (Fig 4A and 4B). We also observed an increase (136% ± 6%) in activity of P169*-lacZ relative to P169-lacZ in WT, while the activity of P2901*-lacZ was decreased compared to P2901-lacZ. The mutations may alter the target sequence for other regulator(s) in addition to the methylation properties, thereby affecting transcription directly or indirectly in a positive or negative fashion [42, 43]. For example, P2901 is bound by the master cell cycle regulator CtrA in vivo and the ΔmucR1/2 mutation is known to affect CtrA expression [17], whereas the P169 promoter is affected by the phosphate starvation response (see below) [44, 45].

LacZ-based assays are a general and indirect measurement of promoter activity, but they do not pinpoint the transcription start sites (TSSs), thus cannot reveal the physical proximity of the TSS relative to the hypomethylated GANTCs. To correlate transcriptional regulation of MucR1/2 and hypomethylated GANTCs, we took advantage of the recently developed RNA-Seq-based strategy for exact mapping of transcriptome ends (EMOTE) [46] that can also be used to map the (unprocessed) 5’ends of nascent transcripts that harbour triphosphate 5’end (5’-ppp). To this end, total RNA is first treated with XRN1 (to remove transcripts with monophosphorylated 5’ends) and then 5’-ppp transcripts are treated with E. coli RppH, which converts the 5’ends to a monophosphorylated form that can be ligated to a bar-tagged RNA oligo using T4 RNA ligase [46] (S3 Fig). Comparative TSS-EMOTE analysis in total RNA extracted from WT and ΔmucR1/2 cells unearthed several TSSs that are activated when MucR1/2 is absent (arrows in Fig 2A, S5 Table). Importantly, several of these TSSs were detected in close proximity to the GANTCs within MucR1/2 target sequences, including P2901, P2830, P3248 and CCNA_01083. These results, therefore, validate the physical proximity and functional interplay between MucR1/2 and hypomethylated GANTCs. While for weak MucR1/2 target promoters the sequencing depth may have limited their detection by TSS-EMOTE, this analysis unexpectedly revealed several MucR-dependent antisense transcripts with potential regulatory roles (green arrows in Fig 2A). We validated the MucR1/2-dependency of two such antisense promoters (P2902_AS and P3247_AS) by LacZ-promoter probe assays and detected a substantial increase in activity of P2902_AS-lacZ and P3247_AS-lacZ in ΔmucR1/2 versus WT cells (S1E Fig), indicating that these promoters (and the GANTCs within) are clearly MucR1/2 regulated in C. crescentus.

Control of hypomethylation of MucR-target promoters in α-proteobacteria

Knowing that MucR is functionally interchangeable in α-proteobacteria [17, 18] and that hypomethylated GANTCs are also detected in the S. meliloti multipartite genome by HinfI REC-Seq (see above and S1 Table), we tested whether S. meliloti MucR also occludes GANTCs from methylation by CcrM in target promoters. We compared the methylation of WT and mucR::Tn S. meliloti gDNA by HinfI REC-Seq and SMRT-sequencing (S1 and S3 Tables). Guided by these data sets, we validated hypomethylation of GANTCs at or near the SMa1635 (SM2011_RS04470) and SMa2245 (SM2011_RS06125) genes by HinfI-restriction/qPCR analysis. We chose these GANTCs, located on the symbiotic megaplasmid pSymA, to take advantage of the S. meliloti multipartite genome and to explore if MucR-control of hypomethylation also applies to episomal elements such as a symbiotic megaplasmid. HinfI-restriction/qPCR analysis revealed that these GANTCs are largely hypomethylated in WT compared to mucR::Tn cells (Fig 5A and 5B). To confirm that these GANTCs are indeed direct targets of S. meliloti MucR, we conducted quantitative ChIP (qChIP) experiments (Fig 5D) with chromatin from S. meliloti WT and mucR::Tn cells precipitated using antibodies to C. crescentus MucR2 that recognize S. meliloti MucR on immunoblots (S4A Fig). The qChIP experiments revealed that S. meliloti MucR indeed binds at or near the hypomethylated SMa1635 and SMa2245 GANTCs of WT cells (Fig 5D), but not at a control site (SMc01552). Moreover, since CcrM is restricted to late S-phase also in S. meliloti [14], we tested whether constitutive expression of ccrMCc in S. meliloti WT cells affected the methylation of GANTCs at SMa1635 and SMa2245. Ectopic expression of ccrMCc from Plac on pSRK vector [47] significantly increased the methylation of SMa1635 and SMa2245, showing that S. meliloti MucR no longer occludes GANTCs in target promoters when cycling of CcrM is impaired (Fig 5C). Consistent with SMa1635 and SMa2245 being MucR targets, LacZ-based promoter probe experiments (using Pa1635-lacZ and Pa2245-lacZ) revealed that they are de-repressed in S. meliloti mucR::Tn cells compared to WT (Fig 5E) and that S. meliloti MucR represses Pa1635-lacZ and Pa2245-lacZ in C. crescentus WT or ΔmucR1/2 cells (Fig 5F). Importantly, when cycling of CcrM in C. crescentus was prevented by Plac-ccrM or the lon mutation Pa1635-lacZ and Pa2245-lacZ activity was increased compared to the WT strain (Fig 5G). Thus, MucR controls hypomethylation during α-proteobacterial cell cycle.

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Fig 5. Hypomethylation control by MucR is conserved in α-proteobacteria.

(A) Schematic of the two MucR-dependent hypomethylated loci (SMa1635 and SMa2245) identified by SMRT-sequencing in the in S. meliloti WT genome. Position of the hypomethylated GANTCs is indicated by purple asterisks. The blue arrows indicate the DNA fragments cloned for LacZ promoter probe assays. (B) HinfI-qPCR analysis showing that SMa1635 and SMa2245 are hypomethylated in S. meliloti WT cells compared to mucR::Tn cells. (C) Constitutive expression of ccrMCc from Plac on pSRK [47] in S. meliloti WT cells increases the methylation percentage of SMa1635 and SMa2245, indicating that hypomethylation of GANTCs by MucR is also impaired in S. meliloti G1-phase cells. (D) MucR occupancy at SMa1635, SMa2245 and SMc1552 (control) in WT and mucR::Tn S. meliloti cells, as determined by qChIP using antibodies to C. crescentus MucR2. SMa1635 and SMa2245 are bound by S. meliloti MucR, which suggests that hypomethylation of GANTCs at these loci is directly due to occlusion by MucR. (E) Beta-galactosidase activity of Pa1635–lacZ and Pa2245–lacZ in S. meliloti (fragments indicated by blue arrows in panel A). Both DNA fragments show a promoter activity that is strongly de-repressed in mucR::Tn cells compared to the WT strain. Values are expressed as percentages (activity in WT cells set at 100%). (F) Beta-galactosidase activity of Pa1635 and Pa2245 in C. crescentus WT and ΔmucR1/2 cells expressing mucRSm. Expression of mucRSm from Pvan on pMT335 decreases beta-galactosidase activity of Pa1635 and Pa2245. Values are expressed as percentages (activity in WT cells carrying the empty vector set at 100%). (G) Beta-galactosidase activity of Pa1635 and Pa2245 in C. crescentus WT, Plac-ccrM or lon cells. Values are expressed as percentages (activity in WT cells set at 100%).

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Environmental and systemic signals controlling hypomethylation patterns

To determine if other systemic (cell cycle) signals can alter methylation patterns in α-proteobacteria, we tested if CtrA can also occlude GANTC sites from methylation by CcrM. First, we constructed a synthetic promoter in which three GANTCs overlapping two CtrA-boxes (one GANTC in each CtrA box and one in between) were placed downstream of an attenuated E. coli phage T5 promoter on the lacZ promoter probe plasmid (Fig 6A). Next, we determined the methylation percentage of the GANTCs in WT C. crescentus cells harbouring the resulting reporter plasmid by HinfI-qPCR analysis and found that the GANTCs are only partially methylated in WT cells, but efficiently methylated when CcrM is expressed ectopically (Fig 6A). Thus, methylation patterns can also emerge from competition between CcrM and other cell cycle regulators such as CtrA at appropriately positioned GANTCs.

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Fig 6. Cell cycle and environmental signals affect methylation patterns.

(A) Competition between CtrA and CcrM. Schematic of the synthetic promoter carrying an attenuated E. coli phage T5 promoter followed by three GANTCs (purple asterisks) overlapping two CtrA-boxes (in yellow). HinfI-qPCR analysis shows that this sequence is hypomethylated in WT cells, whereas constitutive expression of ccrM (Plac-ccrM) increases the methylation percentage. This indicates that DNA-binding proteins other than MucR can also occlude GANTCs from methylation. (B) Methylation percentage of P169 and P1149 in phosphate-limiting conditions compared to rich medium (PYE), determined by HinfI-qPCR analysis. Phosphate starvation (6h) significantly increases the methylation level of P169 but not P1149. (C) Beta-galactosidase activity of P169–lacZ and P169*–lacZ (GANTCs mutated to GTNTCs as in Fig 4A) in WT and ΔmucR1/2 cells in rich medium and phosphate-limiting conditions. Phosphate starvation induces transcription from P169–lacZ and P169*–lacZ independently from the presence of MucR1/2. Values are expressed as percentages (activity in WT cells grown in PYE set at 100%). (D) Beta-galactosidase activity of P1149–lacZ in WT and ΔmucR1/2 cells in rich medium and phosphate-limiting conditions. Phosphate starvation does not significantly affect the activity of P1149–lacZ. Values are expressed as percentages (activity in WT cells grown in PYE set at 100%).

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To explore if environmental signals can also affect local hypomethylation patterns, we took advantage of the fact that expression of CCNA_00169 (also known as elpS) is induced upon phosphate starvation of C. crescentus cells [44, 45]. Accordingly, we compared the P169 methylation patterns by HinfI-qPCR analysis of gDNA from WT cells grown in standard medium (PYE) and phosphate-limiting conditions. This revealed a significant increase in P169 GANTC methylation in phosphate-limiting conditions compared to PYE (Fig 6B) and we observed a commensurate induction of P169-lacZ and P169*-lacZ that was MucR1/2 independent (Fig 6C). Both the increase in P169 GANTC methylation and P169-lacZ activity are dependent on the phosphate-responsive transcriptional regulator PhoB (S4B and S4C Fig), suggesting that PhoB can facilitate methylation of MucR-protected GANTCs at P169. The result that no significant increase of the P1149 methylation or P1149-lacZ activity was seen when WT cells were grown in phosphate-limiting conditions compared to standard PYE medium (or in ΔphoB::Ω cells compared to WT) argues against the possibility that changes in CcrM expression or activity underlie the modified methylation pattern of P169 (Fig 6B and 6D; S4B and S4C Fig). Thus, P169 provides an example of an environmental override for a promoter subject to local hypomethylation control by the cell cycle transcriptional circuitry.

Discussion

The correlative changes between human genetic variability and local (hypo)methylation prompt the question if and how such patterns are regulated by the cell cycle and/or environmental cues. Taking advantage of bacterial genomes that are small enough for full-methylome analysis by cutting-edge REC-Seq and SMRT-sequencing, we show that local m6A-hypomethylation exists in two different α-proteobacterial lineages and that conserved cell cycle factors govern its establishment in both systems. While in γ-proteobacterial lineages transcriptional regulators are also known to compete with the Dam m6A methylase to occlude certain methylation sites, local hypomethylation patterning has not been explored in the context of the transcriptional circuitry controlling progression of the (α-proteo)bacterial cell cycle. In eukaryotes methylation heterogeneity involves 5-methyl-cytosines introduced at CpG dinucleotides [2], but recently m6A marks, instated by unknown mechanisms, have also been reported [68]. Reliable detection of methylation sites by SMRT-sequencing requires extensive (25-fold) coverage for adenine methylation and even higher coverage for cytosine methylation (250-fold coverage needed in some instances) [5]. Non-methylated sites are only reliably detected by elimination of sites on which methylation is detected, thus leaving an element of uncertainty for those sites classified as non-methylated based on the absence of the kinetic signature for methylation. By contrast, REC-Seq with a methylation sensitive restriction enzyme was used here to enrich for non-methylated sites in α-proteobacteria by HinfI cleavage. The continuum of scores we detected in these experiments points towards the use of REC-Seq in detecting loci whose methylation is variable within a culture, for example phase variable loci [48, 49]. HinfI REC-Seq revealed the occurrence of non-methylated GANTCs in at least two α-proteobacterial genomes. Subsequent genetic analyses showed that the determinants controlling hypomethylation are conserved in these bacteria, but they are not encoded in eukaryotic genomes. However, at least one component, MucR, possesses an ancestral zinc-finger-type DNA binding domain [22], a protein domain which is also wide-spread in developmental regulation of eukaryotes [50]. The fact that MucR regulates expression of virulence and cell cycle genes [1720], has recently been shown to confine genetic exchange by generalized transduction to G1-phase in C. crescentus via transcriptional regulation [41] and is responsible for hypomethylation of specific loci on the chromosome or megaplasmids thus raises the possibility that zinc-finger proteins may control (epigenetic) DNA transactions including local hypomethylation during the eukaryotic cell cycle as well. In bacteria local methylation changes may correlate with altered virulence behaviour and may underlie cell cycle-control in pathogens, endosymbionts or other microbial systems. Methylation is known to influence virulence functions in γ-proteobacteria, often by imposing bistability from phase-variable virulence promoters in subpopulations via transcriptional regulators such as Lrp, Fur or OxyR [5, 9, 42, 48, 5155]. Phenotypic heterogeneity in antibiotic drug tolerance in vivo (a state known as persistence), which is acquired in a low fraction of bacterial cells, may also underlie epigenetic changes induced stochastically by methylation, either deterministically (during the cell cycle) or environmentally. Although no phase-variable promoters are currently known for the α-proteobacteria, these bacteria offer the possibility to investigate the relationship of local hypomethylation with cell cycle control, as both C. crescentus and S. meliloti are synchronizable and exhibit comparable cell cycle control systems and transcription [1315, 56]. However, as binding of MucR to DNA is not impaired by methylation, the mechanisms underlying the increase in transcription of target genes induced by methylation in α-proteobacteria (Fig 4D–4F; Fig 5G) are likely to be different from those described for γ-proteobacteria. Moreover, the α-proteobacteria lineage includes the Rickettsiales order encompassing obligate intracellular pathogens, endosymbionts and the extinct proto-mitochondrion from which the modern day mitochondria descended [57]. As MucR and CcrM orthologs are not encoded in most Rickettsiales genomes, the determinants of hypomethylation in this order must be different, if they do exist. Interestingly, endosymbionts from the genus Wolbachia might provide a possible exception. Their genomes encode an unusual putative DNA methylase in which a C-terminal pfam01555 methylase-domain is fused to a pfam02195 ParB-like nuclease domain found in DNA-binding proteins and plasmid replication factors [58]. The sheltered niche of obligate intracellular Rickettsia contrasts with that of free-living relatives that are exposed to major environmental fluctuations.

In summary, our work shows that environmental regulatory responses like that to phosphate limitation, which is particularly pertinent for bacteria living in aquatic ecosystems as C. crescentus, are superimposed on (direct or indirect) hypo- or hyper-methylation control cued by the cell cycle. As many hypomethylated sites occur upstream of genes encoding transcription factors (see S1 Table) and transcription factors are often auto-regulatory, it is conceivable that local hypomethylation is often induced by cis-encoded site-specific DNA-binding proteins that can compete with DNA methylases for overlapping target sites. The mechanism of DNA binding and temporal regulation of MucR remain to be elucidated in detail in order to reveal why MucR shields certain target sites from methylation by CcrM. Our work on MucR-dependent hypomethylation by HinfI REC-Seq along with the comprehensive analysis of hypomethylated sites in other α-proteobacterial genomes [10] indicates that the functions controlled by hypomethylated promoters are distinct and generally not conserved among different α-proteobacteria. This suggests that hypomethylation does not play a major role in the regulation of the α-proteobacterial cell cycle, even though conserved cell cycle transcriptional regulators govern hypomethylation patterns. If it is largely serendipitous which sites MucR shields from methylation, it seems plausible that such hypomethylation control systems mediate species-specific transcriptional adaptations in response to stresses via MucR, CcrM or other variables that influence their binding, either directly or indirectly. For example, cell cycle controlled changes in local chromosomal topologies mediated by DNA replication or nucleoid-associated factors [59, 60] could exclude DNA methylases from specific target sites.

Materials and Methods

Strains and growth conditions

Caulobacter crescentus NA1000 [61] and derivatives were grown at 30°C in PYE (peptone-yeast extract) or M2G (minimal glucose). For phosphate starvation, Caulobacter cells were grown in 1/5X PYE (5-fold diluted PYE except 1 mM MgSO4 and 1 mM CaCl2, supplemented with 0.2% glucose). Sinorhizobium meliloti Rm2011 and derivatives were grown at 30°C in Luria broth (LB) supplemented with CaCl2 2.5 mM and MgSO4 2.5 mM. Escherichia coli S17-1 λpir and EC100D were grown at 37°C in LB. Swarmer cell isolation, electroporations, bi-parental matings and bacteriophage φCr30-mediated generalized transductions were performed as previously described [6265]. Nalidixic acid, kanamycin, gentamicin and tetracycline were used at 20 (8 for S. meliloti), 20, 1 (10 for E. coli and S. meliloti) and 1 (10 for E. coli and S. meliloti) μg/mL, respectively. Plasmids for β-galactosidase assays were introduced into S. meliloti by bi-parental mating and into C. crescentus by electroporation. Strains and plasmids constructions are detailed in the S1 Text file.

Extraction of genomic DNA and methylation by qPCR (HinfI-restriction/qPCR)

Genomic DNA was extracted from mid-log phase cells (10 ml). Aliquots of DNA (0.5–1 μg) were digested with HinfI restriction endonuclease and used to determine the methylation percentage by Real-Time PCR. Real-time PCR was performed using a Step-One Real-Time PCR system (Applied Biosystems, Foster City, CA) using 0.05% of each DNA sample (5 μl of a dilution 1:100) digested with HinfI, 12.5 μl of SYBR green PCR master mix (Quanta Biosciences, Gaithersburg, MD) and primers 10 μM each, in a total volume of 25 μl. A standard curve generated from the cycle threshold (Ct) value of the serially diluted non-digested genomic DNA was used to calculate the methylation percentage value for each sample. Average values are from triplicate measurements done per culture, and the final data was generated from three independent cultures per strain and condition. The primers used for Real-Time PCR are listed in Table B in the S1 Text file.

Genome-wide methylation analyses

SMRT (single-molecule real-time) sequencing libraries were prepared from gDNA extracted from the four samples (C. crescentus and S. meliloti WT and mucR mutant strains) using the DNA Template Prep Kit 2.0 (250bp–3Kb, Pacific Biosciences p/n 001-540-726). Sequences generated by the Pacific Bioscience RSII were aligned to the C. crescentus NA1000 or S. meliloti Rm2011 genomes [37, 66, 67] using Blasr (https://github.com/PacificBiosciences/blasr) and the modification and associated motifs patterns were identified applying the RS_Modification_and_Motif_Analyisis protocol in SMRT Analysis (https://github.com/PacificBiosciences/SMRT-Analysis/wiki/SMRT-Analysis-Software-Installation-v2.2.0). For each aligned base, a statistics measured as interpulse duration (IPD) combined with a modification quality value (QV) would mark the methylation status. On the one hand, a minimum QV of 45 is required for a position to be marked as methylated; on the other hand, a maximum QV between 10 and 30 (depending on the observed kinetic detections background in the sample), coupled with the requirement that such a score is observed on both strands, would mean that a position, in an otherwise methylated motif, is unmethylated.

For REC-Seq (restriction enzyme cleavage–sequencing) 1 μg of genomic DNA from C. crescentus NA1000 and S. meliloti Rm2011 was cleaved with HinfI, a blocked (5’biotinylated) specific adaptor was ligated to the ends and the ligated fragments were then sheared to an average size of 150–400 bp (Fasteris SA, Geneva, CH). Illumina adaptors were then ligated to the sheared ends followed by deep-sequencing using a Hi-Seq Illumina sequencer, and the (50 bp single end) reads were quality controlled with FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). To remove contaminating sequences, the reads were split according to the HinfI consensus motif (5’-G^ANTC-3’) considered as a barcode sequence using fastx_toolkit (http://hannonlab.cshl.edu/fastx_toolkit/) (fastx_barcode_splitter.pl —bcfile barcodelist.txt —bol —exact). Most of the reads (more than 90%) were rejected, and the reads kept were remapped to the reference genomes [37, 66, 67] with bwa mem [68] and samtools [69] to generate a sorted bam file. The bam file was further filtered to remove low mapping quality reads (keeping AS > = 45) and split by orientation (alignmentFlag 0 or 16) with bamtools [70]. The reads were counted at 5' positions using Bedtools [71] (bedtools genomecov -d -5). Both orientation count files were combined into a bed file at each identified 5’-GANTC-3’ motif (where reverse counts > = 1 at position N+1 and forward counts > = 1 at position N-1) using a home-made PERL script. The HinfI positions in the bed file were associated with the closest gene using Bedtools closest [71] and the gff3 file of the reference genomes [72]. The final bed file was converted to an MS Excel sheet (S1 and S2 Tables) with a homemade script. For the MboI-based REC-Seq, the strategy was identical except that a different adaptor was used for ligation after cleavage and the MboI consensus motif (5’-^GATC-3’) was used as barcode for filtering of V. cholerae O1 biovar El Tor [73] and E. coli K12 Ec100D gDNA mapped onto the MG1655 genome [74].

ChIP-Exo on C. crescentus synchronized cells

C. crescentus WT cells for ChIP-Exo were taken at different time points after synchronization (10, 40, 70 and 100 minutes). After cross-linking, chromatin was prepared as previously described [17]. ChIP-Exo was performed with 2 μl of polyclonal antibodies to MucR1 at Peconic LCC (http://www.peconicgenomics.com) (State College, PA), which provided standard genomic position format files (BAM) as output using the SOLiD genome sequencer (Applied Biosystems). A custom Perl script was then used to calculate the sequencing (read) coverage per base (per-base coverage) for each ChIP-Exo sample. Next, we computed the enrichment ratio (ER) for each promoter region. To this end, the Perl script extracted the per-base coverage of a 600 bp region for each ORF (from -500 to +100 from the start codon for each ORF annotated in C. crescentus genome) and calculated the average coverage for each of these regions. The resulting value was then normalized with respect to the coverage of all the intergenic regions. This was done (by the Perl script) by selecting all the intergenic regions in the C. crescentus genome, merging them and extracting the per-base coverage for all these intergenic regions. The coverage was averaged for windows of 600 bp, shifting each window by 100 bp, and the mean of all resulting values was computed. The ER for each promoter region was therefore calculated as the ratio between the average coverage of the promoter region divided by the mean obtained for the intergenic regions.

Transcriptional start sites mapping by exact mapping of transcriptome ends (EMOTE)

The transcription start sites in the NA1000 WT and the ΔmucR1/2 mutant were determined by TSS-EMOTE (Transcription Start Specific Exact Mapping Of Transcriptome Ends), a global assay that reveals the sequence of the 20 first nucleotides of 5’-triphosphorylated RNA in a sample based on an XRN-1 digest of transcripts lacking the 5’ triphosphate ends [46]. The TSS-EMOTE protocol and analyses were performed according to the scheme in S3 Fig and the detailed protocol described in [75]. We used a Worst-Case (i.e.) smallest difference) model to compare the number of Unique Molecular Identifiers between the two pairs of biological replicates (i.e. mutant vs. wild-type) and provide additional information about relative expression for each of the detected TSSs. The full list of detected TSSs is shown in S5 Table and TSSs at the relevant genomic loci are indicated by black (sense) and green (antisense) arrows in Fig 2A.

β-galactosidase assays

β-galactosidase assays were performed at 30°C. Cells (50–200 μl) at OD660nm = 0.1–0.5 were lysed with chloroform and mixed with Z buffer (60 mM Na2HPO4, 40 mM NaH2PO4, 10 mM KCl and 1 mM MgSO4, pH 7) to a final volume of 800 μl. Two hundred μl of ONPG (o-nitrophenyl-β-D-galactopyranoside, stock solution 4 mg/ml in 0.1 M potassium phosphate, pH 7) were added and the reaction timed. When a medium-yellow colour developed, the reaction was stopped by adding 400 μl of 1M Na2CO3. The OD420nm of the supernatant was determined and the Miller units (U) were calculated as follows: U = (OD420nm*1000)/(OD660nm*time [in min] *volume of culture used [in ml]). Error was computed as standard deviation (SD) of at least three independent experiments.

qChIP assay on S. meliloti

Samples for qChIP assay were prepared from mid-log phase S. meliloti cells as previously described [17]. Two microliters of polyclonal antibodies to MucR2 were used for the immunoprecipitation.

Real-time PCR was performed as described for HinfI-restricted genomic DNA, using 0.5% of each ChIP sample (5 μl of a dilution 1:10). A standard curve generated from the cycle threshold (Ct) value of the serially diluted chromatin input was used to calculate the percentage input value for each sample. Average values are from triplicate measurements done per culture, and the final data was generated from three independent cultures per strain. The primers used for SMa1635 and SMa2245 loci were the same as for the determination of the methylation percentage of these loci (Table B in S1 Text file).

Immunoblots

For immunoblots, protein samples were separated on SDS polyacrylamide gel, transferred to polyvinylidene difluoride (PVDF) Immobilon-P membranes (Merck Millipore) and blocked in PBS (phosphate saline buffer) 0.1% Tween20 and 5% dry milk. The anti-sera were used at the following dilutions: anti-CcrM (1:10’000) [26], anti-MucR1 [17] (1:10’000), anti-MucR2 [17] (1:10’000). Protein-primary antibody complexes were visualized using horseradish peroxidase-labelled anti-rabbit antibodies and ECL detection reagents (Merck Millipore).

Plasmids, primers, synthetic fragments and strains constructions are described in the S1 Text file.

Data access

Deep-sequencing data are deposited in Gene Expression Omnibus database (GEO: GSE79880).

Supporting Information

S1 Text. Strains and plasmids construction.

Table A. Strains and plasmids used in this study. Table B. Oligonucleotides used in this study.

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S1 Fig. Controls for methylation percentage by HinfI-qPCR and MucR-dependent antisense transcription.

(A) Methylation percentage of control loci in the WT and ΔmucR1/2 strains, as determined by HinfI-qPCR: PnagA (hypomethylated, MucR-independent), PxylX (no GANTCs), PpodJ (fully methylated, MucR-independent). The controls show that MucR does not affect the methylation of sequences that are not its direct targets. (Note that as PxylX contains no GANTCs, the label on the y-axis should not be interpreted as methylation but cleavage percentage). (B) Methylation percentage of P1248 and P3426 determined by HinfI-qPCR analysis. The graphs show that these sequences are hypomethylated in the ΔmucR1/2 compared to the WT strain, as predicted by the SMRT-sequencing and REC-Seq. This suggests that other DNA-binding proteins, directly or indirectly MucR-dependent, can also occlude GANTCs from methylation. (C) Immunoblot anti-CcrM in WT and lon mutant cells carrying an empty vector, Pvan-mucR1 or Pvan-mucR1 long (dominant negative form of MucR1 with an N-terminal extension). Over-expression of MucR1 does not affect the steady state levels of CcrM (arrow). Note that CcrM levels are elevated in the lon mutant strain as the protein is stabilized. Molecular size standards are indicated on the right as blue lines with the corresponding values in kDa. (D) Methylation percentage of the parS locus in C. crescentus WT cells carrying the empty vector, the heterologous methylases (TA, HP, hinf) under control of Pvan on pMT335, Plac-ccrM and in the lonΩ mutant. Stabilisation of CcrM or constitutive expression of heterologous methylases does not affect the methylation state of GANTCs at the parS locus. (E) Beta-galactosidase activity of two MucR-dependent antisense promoters identified by TSS-EMOTE. Values are expressed as percentages (activity in WT cells set at 100%).

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S2 Fig. Rec-Seq comparison of GANTC methylation in WT and ΔmucR1/2.

The Rec-Seq score of each GANTC site in WT and ΔmucR1/2 C. crescentus was normalized according to the total number of reads obtained for the strains. The graph represents the difference between the normalized score obtained for the WT and the normalized score for the ΔmucR1/2 strain for each GANTC site according to the position along the chromosome. Positive values (in blue) indicate hypomethylation in the WT, whereas negative values (in red) indicate hypomethylation in the ΔmucR1/2 strain. The GANTCs verified by HinfI-qPCR are indicated.

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S3 Fig. Flowchart of the EMOTE assay.

RNA is shown as thin lines and DNA as thick lines, double lines represent Illumina adaptors. (A) Cellular RNAs exist as primary transcripts with a triphosphorylated 5’end (PPP, red line) and processed transcripts with either a monophosphorylated 5’end (P, dashed grey line) or a non-phosphorylated 5’-OH end (straight grey line). The asterisk indicates the ends of interest. XRN1 is used to remove 5' monophosphorylated RNA (grey dotted line) from the total RNA samples. (B) Treatment with E. coli RppH converts the 5' triphosphorylated end of primary transcripts to a monophosphorylated 5’end, a substrate for ligation (C, D) to the Rp6 synthetic oligonucleotide with T4 RNA ligase 1, which does not accept triphosphorylated or non-phosphorylated substrates. A mock reaction is performed at this stage in the absence of RppH to control for background (non-specific) signals. (E, F) Reverse transcription generates cDNA from both non-phosphorylated and Rp6-ligated RNA. Open arrows indicate polymerase extension. (G) Only cDNA from Rp6-ligated (and therefore originally triphosphorylated) RNA is amplified by the primers that add EMOTE barcodes (xxx) and Illumina adaptors. (H) Illumina sequencing (50 nucleotides) from the “A” end (see panel G) results in reads that have the specific EMOTE barcode of the original RNA sample, the Rp6 sequence and the first 20 nucleotides of the original triphosphorylated RNA, permitting exact identification of the original 5' end.

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S4 Fig. Antibodies against C. crescentus MucR2 specifically recognize S. meliloti MucR. Methylation and induction of P169 are PhoB-dependent.

(A) Immunoblot on C. crescentus and S. meliloti total cell extracts showing that the polyclonal antibodies against C. crescentus MucR2 specifically recognize S. meliloti MucR (SMc00058). Molecular size standards are indicated on the right as blue lines with the corresponding values in kDa. (B) Methylation percentage of P169 and P1149 in phosphate-limiting conditions compared to rich medium (PYE) in WT and ΔphoB::Ω cells, determined by HinfI-qPCR analysis. The graphs show that the increase in the methylation state of P169 is dependent on the presence of the conserved transcriptional regulator PhoB. (C) Beta-galactosidase activity of P169–lacZ and P1149–lacZ in WT and ΔphoB:: Ω cells in rich medium and phosphate-limiting conditions. The induction of P169-lacZ is specifically dependent on the presence of PhoB. Values are expressed as percentages (activity in WT cells grown in PYE set at 100%).

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S1 Table. REC-Seq HinfI analysis of C. crescentus and S. meliloti genomic DNA.

In both cases, wild type and mucR mutant strains were analysed. For the GANTC sites with a score higher than 100 in the WT C. crescentus, REC-Seq data were compared to available ChIP-Seq [17] and SMRT-Sequencing data [12]. The GANTCs tested by HinfI-qPCR assay are highlighted in yellow. The 50 GANTCs with the highest score in WT C. crescentus are those shown also in Table 1.

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S2 Table. REC-Seq MboI analysis of E. coli K12 and Vibrio cholerae genomic DNA.

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S3 Table. Non-methylated GANTCs predicted from SMRT analysis of WT and mucR mutant in C. crescentus and S. meliloti genomic DNA.

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S4 Table. ChIP-Exo analysis of MucR1 occupancy at different time points (T10, T40, T70, T100) during the C. crescentus cell cycle.

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S5 Table. TSS-EMOTE analysis of transcription start sites in WT and ΔmucR1/2 C. crescentus cells.

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Acknowledgments

We thank Julien Prados for help with TSS-EMOTE bioinformatics and Laurence Théraulaz for excellent technical assistance. We thank Melanie Blokesch and Yoshiharu Yamaichi for providing V. cholerae gDNA.

Author Contributions

  1. Conceptualization: SA PR GR CF RS LF PHV.
  2. Formal analysis: GR AF RS LF.
  3. Funding acquisition: LF PHV.
  4. Investigation: SA PR GR CF AP.
  5. Methodology: SA PR GR AF CF RS LF PHV.
  6. Software: GR AF RS LF.
  7. Validation: SA PR AF LF PHV.
  8. Writing – original draft: SA PHV.
  9. Writing – review & editing: SA PR GR AF LF PHV.

References

  1. 1. Casadesus J, Low D. Epigenetic gene regulation in the bacterial world. Microbiol Mol Biol Rev. 2006;70(3):830–56. pmid:16959970
  2. 2. Schubeler D. Function and information content of DNA methylation. Nature. 2015;517(7534):321–6. pmid:25592537
  3. 3. Arber W, Dussoix D. Host specificity of DNA produced by Escherichia coli. I. Host controlled modification of bacteriophage lambda. J Mol Biol. 1962;5:18–36. pmid:13862047
  4. 4. Marinus MG, Casadesus J. Roles of DNA adenine methylation in host-pathogen interactions: mismatch repair, transcriptional regulation, and more. FEMS Microbiol Rev. 2009;33(3):488–503. pmid:19175412
  5. 5. Sanchez-Romero MA, Cota I, Casadesus J. DNA methylation in bacteria: from the methyl group to the methylome. Curr Opin Microbiol. 2015;25:9–16. pmid:25818841
  6. 6. Greer EL, Blanco MA, Gu L, Sendinc E, Liu J, Aristizabal-Corrales D, et al. DNA Methylation on N6-Adenine in C. elegans. Cell. 2015;161(4):868–78. pmid:25936839
  7. 7. Fu Y, Luo GZ, Chen K, Deng X, Yu M, Han D, et al. N6-methyldeoxyadenosine marks active transcription start sites in chlamydomonas. Cell. 2015;161(4):879–92. pmid:25936837
  8. 8. Zhang G, Huang H, Liu D, Cheng Y, Liu X, Zhang W, et al. N6-methyladenine DNA modification in Drosophila. Cell. 2015;161(4):893–906. pmid:25936838
  9. 9. Adhikari S, Curtis PD. DNA methyltransferases and epigenetic regulation in bacteria. FEMS Microbiol Rev. 2016;40(5):575–91. pmid:27476077
  10. 10. Blow MJ, Clark TA, Daum CG, Deutschbauer AM, Fomenkov A, Fries R, et al. The Epigenomic Landscape of Prokaryotes. PLoS Genet. 2016;12(2):e1005854. pmid:26870957
  11. 11. Collier J. Epigenetic regulation of the bacterial cell cycle. Curr Opin Microbiol. 2009;12(6):722–9. pmid:19783470
  12. 12. Kozdon JB, Melfi MD, Luong K, Clark TA, Boitano M, Wang S, et al. Global methylation state at base-pair resolution of the Caulobacter genome throughout the cell cycle. Proc Natl Acad Sci U S A. 2013;110(48):E4658–67. pmid:24218615
  13. 13. Panis G, Murray SR, Viollier PH. Versatility of global transcriptional regulators in alpha-Proteobacteria: from essential cell cycle control to ancillary functions. FEMS Microbiol Rev. 2015;39(1):120–33. pmid:25793963
  14. 14. De Nisco NJ, Abo RP, Wu CM, Penterman J, Walker GC. Global analysis of cell cycle gene expression of the legume symbiont Sinorhizobium meliloti. Proc Natl Acad Sci U S A. 2014;111(9):3217–24. pmid:24501121
  15. 15. Laub MT, McAdams HH, Feldblyum T, Fraser CM, Shapiro L. Global analysis of the genetic network controlling a bacterial cell cycle. Science. 2000;290(5499):2144–8. pmid:11118148
  16. 16. Hallez R, Bellefontaine AF, Letesson JJ, De Bolle X. Morphological and functional asymmetry in alpha-proteobacteria. Trends Microbiol. 2004;12(8):361–5. pmid:15276611
  17. 17. Fumeaux C, Radhakrishnan SK, Ardissone S, Theraulaz L, Frandi A, Martins D, et al. Cell cycle transition from S-phase to G1 in Caulobacter is mediated by ancestral virulence regulators. Nature communications. 2014;5:4081. pmid:24939058
  18. 18. Mirabella A, Terwagne M, Zygmunt MS, Cloeckaert A, De Bolle X, Letesson JJ. Brucella melitensis MucR, an Orthologue of Sinorhizobium meliloti MucR, Is Involved in Resistance to Oxidative, Detergent, and Saline Stresses and Cell Envelope Modifications. J Bacteriol. 2013;195(3):453–65. pmid:23161025
  19. 19. Cooley MB, Kado CI. Mapping of the ros virulence regulatory gene of A. tumefaciens. Mol Gen Genet. 1991;230(1–2):24–7. pmid:1660566
  20. 20. Mueller K, Gonzalez JE. Complex regulation of symbiotic functions is coordinated by MucR and quorum sensing in Sinorhizobium meliloti. J Bacteriol. 2011;193(2):485–96. pmid:21057009
  21. 21. Baglivo I, Russo L, Esposito S, Malgieri G, Renda M, Salluzzo A, et al. The structural role of the zinc ion can be dispensable in prokaryotic zinc-finger domains. Proc Natl Acad Sci U S A. 2009;106(17):6933–8. Epub 2009/04/17. pmid:19369210
  22. 22. Malgieri G, Russo L, Esposito S, Baglivo I, Zaccaro L, Pedone EM, et al. The prokaryotic Cys2His2 zinc-finger adopts a novel fold as revealed by the NMR structure of Agrobacterium tumefaciens Ros DNA-binding domain. Proc Natl Acad Sci U S A. 2007;104(44):17341–6. pmid:17956987
  23. 23. Domian IJ, Quon KC, Shapiro L. Cell type-specific phosphorylation and proteolysis of a transcriptional regulator controls the G1-to-S transition in a bacterial cell cycle. Cell. 1997;90(3):415–24. pmid:9267022
  24. 24. Quon KC, Marczynski GT, Shapiro L. Cell cycle control by an essential bacterial two-component signal transduction protein. Cell. 1996;84(1):83–93. pmid:8548829
  25. 25. Reisenauer A, Quon K, Shapiro L. The CtrA response regulator mediates temporal control of gene expression during the Caulobacter cell cycle. J Bacteriol. 1999;181(8):2430–9. pmid:10198005
  26. 26. Stephens C, Reisenauer A, Wright R, Shapiro L. A cell cycle-regulated bacterial DNA methyltransferase is essential for viability. Proc Natl Acad Sci USA. 1996;93(3):1210–4. pmid:8577742
  27. 27. Laub MT, Chen SL, Shapiro L, McAdams HH. Genes directly controlled by CtrA, a master regulator of the Caulobacter cell cycle. Proc Natl Acad Sci U S A. 2002;99(7):4632–7. Epub 2002/04/04. pmid:11930012
  28. 28. Wright R, Stephens C, Zweiger G, Shapiro L, Alley MR. Caulobacter Lon protease has a critical role in cell-cycle control of DNA methylation. Genes Dev. 1996;10(12):1532–42. pmid:8666236
  29. 29. Jonas K, Liu J, Chien P, Laub MT. Proteotoxic stress induces a cell-cycle arrest by stimulating Lon to degrade the replication initiator DnaA. Cell. 2013;154(3):623–36. pmid:23911325
  30. 30. Collier J, McAdams HH, Shapiro L. A DNA methylation ratchet governs progression through a bacterial cell cycle. Proc Natl Acad Sci U S A. 2007;104(43):17111–6. pmid:17942674
  31. 31. Lluch-Senar M, Luong K, Llorens-Rico V, Delgado J, Fang G, Spittle K, et al. Comprehensive methylome characterization of Mycoplasma genitalium and Mycoplasma pneumoniae at single-base resolution. PLoS Genet. 2013;9(1):e1003191. pmid:23300489
  32. 32. Fang G, Munera D, Friedman DI, Mandlik A, Chao MC, Banerjee O, et al. Genome-wide mapping of methylated adenine residues in pathogenic Escherichia coli using single-molecule real-time sequencing. Nat Biotechnol. 2012;30(12):1232–9. pmid:23138224
  33. 33. Zweiger G, Marczynski G, Shapiro L. A Caulobacter DNA methyltransferase that functions only in the predivisional cell. J Mol Biol. 1994;235(2):472–85. pmid:8289276
  34. 34. Dalia AB, Lazinski DW, Camilli A. Characterization of undermethylated sites in Vibrio cholerae. J Bacteriol. 2013;195(10):2389–99. pmid:23504020
  35. 35. Kahng LS, Shapiro L. The CcrM DNA methyltransferase of Agrobacterium tumefaciens is essential, and its activity is cell cycle regulated. J Bacteriol. 2001;183(10):3065–75. Epub 2001/04/28. pmid:11325934
  36. 36. Robertson GT, Reisenauer A, Wright R, Jensen RB, Jensen A, Shapiro L, et al. The Brucella abortus CcrM DNA methyltransferase is essential for viability, and its overexpression attenuates intracellular replication in murine macrophages. J Bacteriol. 2000;182(12):3482–9. pmid:10852881
  37. 37. Capela D, Barloy-Hubler F, Gouzy J, Bothe G, Ampe F, Batut J, et al. Analysis of the chromosome sequence of the legume symbiont Sinorhizobium meliloti strain 1021. Proc Natl Acad Sci U S A. 2001;98(17):9877–82. pmid:11481430
  38. 38. Thanbichler M, Iniesta AA, Shapiro L. A comprehensive set of plasmids for vanillate- and xylose-inducible gene expression in Caulobacter crescentus. Nucleic Acids Research. 2007;35(20):e137. pmid:17959646
  39. 39. Mahony S, Pugh BF. Protein-DNA binding in high-resolution. Crit Rev Biochem Mol Biol. 2015;50(4):269–83. pmid:26038153
  40. 40. Fioravanti A, Fumeaux C, Mohapatra SS, Bompard C, Brilli M, Frandi A, et al. DNA binding of the cell cycle transcriptional regulator GcrA depends on N6-adenosine methylation in Caulobacter crescentus and other Alphaproteobacteria. PLoS Genet. 2013;9(5):e1003541. pmid:23737758
  41. 41. Ardissone S, Fumeaux C, Bergé M, Beaussart A, Theraulaz L, Radhakrishnan SK, et al. Cell cycle constraints on capsulation and bacteriophage susceptibility. eLife. 2014;3. pmid:25421297.
  42. 42. Cota I, Bunk B, Sproer C, Overmann J, Konig C, Casadesus J. OxyR-dependent formation of DNA methylation patterns in OpvABOFF and OpvABON cell lineages of Salmonella enterica. Nucleic Acids Res. 2015. pmid:26687718.
  43. 43. Gonzalez D, Kozdon JB, McAdams HH, Shapiro L, Collier J. The functions of DNA methylation by CcrM in Caulobacter crescentus: a global approach. Nucleic Acids Res. 2014. pmid:24398711.
  44. 44. Le Blastier S, Hamels A, Cabeen M, Schille L, Tilquin F, Dieu M, et al. Phosphate starvation triggers production and secretion of an extracellular lipoprotein in Caulobacter crescentus. PLoS One. 2010;5(12):e14198. pmid:21152032
  45. 45. Lubin EA, Henry JT, Fiebig A, Crosson S, Laub MT. Identification of the PhoB Regulon and Role of PhoU in the Phosphate Starvation Response of Caulobacter crescentus. J Bacteriol. 2015;198(1):187–200. pmid:26483520
  46. 46. Redder P. Using EMOTE to map the exact 5'-ends of processed RNA on a transcriptome-wide scale. Methods Mol Biol. 2015;1259:69–85. pmid:25579580
  47. 47. Khan SR, Gaines J, Roop RM 2nd, Farrand SK. Broad-host-range expression vectors with tightly regulated promoters and their use to examine the influence of TraR and TraM expression on Ti plasmid quorum sensing. Appl Environ Microbiol. 2008;74(16):5053–62. pmid:18606801
  48. 48. Cota I, Blanc-Potard AB, Casadesus J. STM2209-STM2208 (opvAB): a phase variation locus of Salmonella enterica involved in control of O-antigen chain length. PLoS One. 2012;7(5):e36863. pmid:22606300
  49. 49. Casadesus J, Low DA. Programmed heterogeneity: epigenetic mechanisms in bacteria. J Biol Chem. 2013;288(20):13929–35. pmid:23592777
  50. 50. Klug A. The discovery of zinc fingers and their applications in gene regulation and genome manipulation. Annual review of biochemistry. 2010;79:213–31. pmid:20192761
  51. 51. Cota I, Sanchez-Romero MA, Hernandez SB, Pucciarelli MG, Garcia-Del Portillo F, Casadesus J. Epigenetic Control of Salmonella enterica O-Antigen Chain Length: A Tradeoff between Virulence and Bacteriophage Resistance. PLoS Genet. 2015;11(11):e1005667. pmid:26583926
  52. 52. Arnoldini M, Vizcarra IA, Pena-Miller R, Stocker N, Diard M, Vogel V, et al. Bistable expression of virulence genes in salmonella leads to the formation of an antibiotic-tolerant subpopulation. PLoS Biol. 2014;12(8):e1001928. pmid:25136970
  53. 53. Broadbent SE, Davies MR, van der Woude MW. Phase variation controls expression of Salmonella lipopolysaccharide modification genes by a DNA methylation-dependent mechanism. Mol Microbiol. 2010;77(2):337–53. pmid:20487280
  54. 54. Peterson SN, Reich NO. Competitive Lrp and Dam assembly at the pap regulatory region: implications for mechanisms of epigenetic regulation. J Mol Biol. 2008;383(1):92–105. pmid:18706913
  55. 55. Atack JM, Srikhanta YN, Fox KL, Jurcisek JA, Brockman KL, Clark TA, et al. A biphasic epigenetic switch controls immunoevasion, virulence and niche adaptation in non-typeable Haemophilus influenzae. Nature communications. 2015;6:7828. pmid:26215614
  56. 56. Pini F, De Nisco NJ, Ferri L, Penterman J, Fioravanti A, Brilli M, et al. Cell Cycle Control by the Master Regulator CtrA in Sinorhizobium meliloti. PLoS Genet. 2015;11(5):e1005232. pmid:25978424
  57. 57. Andersson SG, Zomorodipour A, Andersson JO, Sicheritz-Ponten T, Alsmark UC, Podowski RM, et al. The genome sequence of Rickettsia prowazekii and the origin of mitochondria. Nature. 1998;396(6707):133–40. pmid:9823893
  58. 58. Ellegaard KM, Klasson L, Naslund K, Bourtzis K, Andersson SG. Comparative genomics of Wolbachia and the bacterial species concept. PLoS Genet. 2013;9(4):e1003381. pmid:23593012
  59. 59. Rimsky S, Travers A. Pervasive regulation of nucleoid structure and function by nucleoid-associated proteins. Curr Opin Microbiol. 2011;14(2):136–41. pmid:21288763
  60. 60. Sobetzko P, Travers A, Muskhelishvili G. Gene order and chromosome dynamics coordinate spatiotemporal gene expression during the bacterial growth cycle. Proceedings of the National Academy of Sciences. 2012;109(2):E42–E50.
  61. 61. Evinger M, Agabian N. Envelope-associated nucleoid from Caulobacter crescentus stalked and swarmer cells. J Bacteriol. 1977;132(1):294–301. pmid:334726
  62. 62. Ely B. Genetics of Caulobacter crescentus. Methods Enzymol. 1991;204:372–84. pmid:1658564
  63. 63. Chen JC, Viollier PH, Shapiro L. A membrane metalloprotease participates in the sequential degradation of a Caulobacter polarity determinant. Mol Microbiol. 2005;55(4):1085–103. Epub 2005/02/03. pmid:15686556
  64. 64. Viollier PH, Shapiro L. A lytic transglycosylase homologue, PleA, is required for the assembly of pili and the flagellum at the Caulobacter crescentus cell pole. Mol Microbiol. 2003;49(2):331–45. Epub 2003/06/28. pmid:12828633
  65. 65. Viollier PH, Thanbichler M, McGrath PT, West L, Meewan M, McAdams HH, et al. Rapid and sequential movement of individual chromosomal loci to specific subcellular locations during bacterial DNA replication. Proc Natl Acad Sci U S A. 2004;101(25):9257–62. pmid:15178755
  66. 66. Marks ME, Castro-Rojas CM, Teiling C, Du L, Kapatral V, Walunas TL, et al. The genetic basis of laboratory adaptation in Caulobacter crescentus. J Bacteriol. 2010;192(14):3678–88. pmid:20472802
  67. 67. Nierman WC, Feldblyum TV, Laub MT, Paulsen IT, Nelson KE, Eisen J, et al. Complete genome sequence of Caulobacter crescentus. Proc Natl Acad Sci U S A. 2001;98(7):4136–41. pmid:11259647
  68. 68. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010;26(5):589–95. pmid:20080505
  69. 69. Li H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics. 2011;27(21):2987–93. pmid:21903627
  70. 70. Barnett DW, Garrison EK, Quinlan AR, Stromberg MP, Marth GT. BamTools: a C++ API and toolkit for analyzing and managing BAM files. Bioinformatics. 2011;27(12):1691–2. pmid:21493652
  71. 71. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2. pmid:20110278
  72. 72. Kersey PJ, Allen JE, Armean I, Boddu S, Bolt BJ, Carvalho-Silva D, et al. Ensembl Genomes 2016: more genomes, more complexity. Nucleic Acids Res. 2016;44(D1):D574–80. pmid:26578574
  73. 73. Heidelberg JF, Eisen JA, Nelson WC, Clayton RA, Gwinn ML, Dodson RJ, et al. DNA sequence of both chromosomes of the cholera pathogen Vibrio cholerae. Nature. 2000;406(6795):477–83. pmid:10952301
  74. 74. Riley M, Abe T, Arnaud MB, Berlyn MKB, Blattner FR, Chaudhuri RR, et al. Escherichia coli K-12: a cooperatively developed annotation snapshot—2005. Nucleic Acids Research. 2006;34(1):1–9. pmid:16397293
  75. 75. Prados J., Linder P., Redder P. TSS-EMOTE, a refined protocol for a more complete and less biased global mapping of trnscription start sites in bacterial pathogens. BMC genomics. 2016;17:849. pmid:27806702