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.
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 [6–8]. 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 [13–16] (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 [17–20]. 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 [15,17,25–27]. 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]. 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.
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. 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. 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]. 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). 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. 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). 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. 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. 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. 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.
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 [6–8]. 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 [17–20], 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, 42,48,51–55]. 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 [13–15,56]. 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.
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 [62–65]. 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. 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. 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]. 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. 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 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. 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). 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. Deep-sequencing data are deposited in Gene Expression Omnibus database (GEO: GSE79880).
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.
sequencing techniques
chemical compounds
caulobacter
phosphates
cell cycle and cell division
cell processes
dna transcription
prokaryotic models
regulator genes
model organisms
gene types
sequence motif analysis
epigenetics
dna
synthesis phase
molecular biology techniques
bacteria
dna methylation
chromatin
research and analysis methods
sequence analysis
chromosome biology
caulobacter crescentus
gene expression
chemistry
chromatin modification
dna modification
molecular biology
biochemistry
cell biology
nucleic acids
genetics
biology and life sciences
physical sciences
organisms
Cell Cycle Constraints and Environmental Control of Local DNA Hypomethylation in α-Proteobacteria