DNA methylation affects gene expression but not global chromatin structure in Escherichia coli

DNA methylation affects gene expression but not global chromatin structure in Escherichia coli | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results DNA methylation affects gene expression but not global chromatin structure in Escherichia coli Willow Jay Morgan , Haley M. Amemiya , View ORCID Profile Lydia Freddolino doi: https://doi.org/10.1101/2025.01.06.631547 Willow Jay Morgan 1 Department of Biological Chemistry, University of Michigan Medical School , Ann Arbor, MI 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Haley M. Amemiya 2 Cellular and Molecular Biology Program, University of Michigan Medical School , Ann Arbor, MI 48109, USA 3 MOMA Therapeutics , Cambridge MA 02140 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lydia Freddolino 1 Department of Biological Chemistry, University of Michigan Medical School , Ann Arbor, MI 48109, USA 2 Cellular and Molecular Biology Program, University of Michigan Medical School , Ann Arbor, MI 48109, USA 4 Department of Computational Medicine and Bioinformatics, University of Michigan Medical School , Ann Arbor, MI 48109, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lydia Freddolino For correspondence: lydsf{at}umich.edu Abstract Full Text Info/History Metrics Preview PDF ABSTRACT The activity of DNA adenine methyltransferase (Dam) and DNA cytosine methyltransferase (Dcm) together account for nearly all methylated nucleotides in the Escherichia coli K-12 MG1655 genome. Previous studies have shown that perturbation of DNA methylation alters E. coli global gene expression, but it is unclear whether the methylation state of Dam or Dcm target sites regulates local transcription. In recent genome-wide experiments, we observed an underrepresentation of Dam sites in transcriptionally silent extended protein occupancy domains (EPODs), prompting us to hypothesize that EPOD formation is caused partially by low Dam site density. We thus hypothesized that a methylation-deficient version of MG1655 would show large-scale aberrations in chromatin structure. To test our hypothesis, we cloned methyltransferase deletion strains and performed global protein occupancy profiling using high resolution in vivo protein occupancy display (IPOD-HR), chromatin immunoprecipitation for RNA polymerase (RNAP-ChIP), and transcriptome abundance profiling using RNA-Seq. Our results indicate that loss of DNA methylation does not result in large-scale changes in genomic protein occupancy such as the formation of EPODs, indicating that the previously observed depletion of Dam sites in EPODs is correlative, rather than causal, in nature. However, loci with dense clustering of Dam methylation sites show methylation-dependent changes in local RNA polymerase and total protein occupancy, but local transcription is unaffected. Our transcriptome profiling data indicates that deletion of dam and/or dcm results in significant expression changes within some functional gene categories including SOS response, flagellar synthesis, and translation, but these expression changes appear to result from indirect regulatory consequences of methyltransferase deletion. In agreement with the downregulation of genes involved in flagellar synthesis, dam deletion is characterized by a swimming motility-deficient phenotype. We conclude that DNA methylation does not control the overall protein occupancy landscape of the E. coli genome, and that observable changes in gene regulation are generally not resulting from regulatory consequences of local methylation state. IMPORTANCE Previous studies of E. coli chromatin structure revealed a statistical association between the presence of silenced, highly protein occupied regions of the genome and depletion of modification sites for Dam methyltransferase. Here, we show that loss of DNA methylation does not substantively affect global chromatin structure in E. coli , thus demonstrating that the previously observed correlation was not causal. However, we observed specific methylation-dependent changes in gene expression, particularly affecting the SOS response, flagellar synthesis, and translation. These effects appear to be indirect regulatory consequences of methyltransferase deletion. Our work clarifies the role of methylation in chromatin structure and regulation, providing new insights into the mechanistic basis of gene expression and chromatin structure in E. coli . INTRODUCTION DNA methylation in bacteria has well-supported roles in phage defense, chromosomal replication, DNA repair, and the regulation of gene expression 1 . Most examples of bacterial methyltransferases are involved in restriction-modification phage defense systems, which involve the methylation of target sequences in bacterial DNA to protect against the endonuclease activity of restriction enzymes which are synthesized by the bacteria to cleave non-methylated phage DNA 2 , 3 . Methylated DNA generally results from the activity of methyltransferases which cleave the methyl group from S-adenosyl-L-methionine (SAM) and transfer it to adenine or cytosine 4 , 5 . DNA methyltransferases have been found a wide range of bacterial species to be involved in chromosomal maintenance, replication, DNA repair, cell cycle control, and virulence 6 – 13 . There are also some examples of bacterial DNA methylation influencing local binding of regulatory proteins in promoters which impacts transcription 14 – 19 , but it is unclear whether this is a common phenomenon in Escherichia coli 1 , 20 – 23 . In the case of commonly studied E. coli K-12 laboratory strains, two major methyltransferases are known: Dam and Dcm. DNA adenine methyltransferase (Dam) in Escherichia coli catalyzes the formation of N 6 -methyladenine in the target motif 5’-G A TC-3’ 24 . Dam is conserved within Enterobacteriaceae. Over 99% of methylated adenines at ∼19,000 sites in the genome of the E. coli K-12 strain MG1655 result from Dam activity 1 . Dam is characterized as an orphan methyltransferase 25 as there is no known cognate restriction enzyme that cleaves at Dam methylation sites in E. coli K-12. Dam methylation has been implicated as a critical element of chromosomal maintenance, as the methylation state of ∼11 Dam target sites at the origin of replication (oriC) regulates initiation of chromosomal replication 26 – 28 . During replication, Dam sites throughout the chromosome are processively methylated whilst lagging the replication fork, producing a transient hemi-methylated state utilized by DNA repair machinery to differentiate between the template and newly-synthesized strands in the event that mismatch repair is needed 29 – 32 . E. coli DNA cytosine methyltransferase (Dcm) methylates the inner cytosine in its 5’-C C WGG-3’ target motif 33 , and Dcm is conserved within Escherichia . Dcm appears to be responsible for all cytosine methylation at ∼12,000 sites in the E. coli K-12 genome 1 , 22 . Taken together with Dam, these two methyltransferases produce nearly the entire E. coli K-12 methylome 1 , 34 . Dcm is known to have a cognate restriction enzyme, Eco RII, which is not found in K-12 strains 35 . The role of Dcm methylation in other cellular functions is less well-characterized than in the case of Dam. While there are few to no changes in growth dynamics when dcm is deleted, there does appear to be a fitness benefit associated with Dcm methylation in long-term stationary phase 22 , 36 . Dcm is also involved in Very Short Patch (VSP) repair in E. coli K-12, where the repair-associated endonuclease Vsr nicks double-stranded DNA at the Dcm target motif when the inner methylated cytosine is deaminated to thymine (5’-C T WGG-3’) 37 . It is unclear whether the methylation state of the cytosine residue impacts this VSP repair process. Perturbation of DNA methylation alters E. coli global gene expression to some extent, but the mechanisms by which the methylation state of Dam or Dcm sites regulates local transcription are not fully understood 20 – 22 , 38 , 39 . In one example of DNA methylation acting as a transcriptional regulator, two nucleoid-associated proteins (NAPs) and Dam compete for binding to the promoter of the virulence-associated pap operon in uropathogenic E. coli 16 , 17 . NAPs are promiscuous DNA-binding proteins that confer chromosomal structure and act as global regulators 40 , 41 . There are other characterized examples of the methylation state at Dam motifs in NAP binding sites influencing NAP binding affinity – and in some cases, gene expression – across different bacterial strains 11 , 18 , 19 , 42 . A potential mechanism is that DNA methylation-dependent alterations of DNA-protein interactions result from the protrusion of the methyl group into the major groove producing DNA curvature 39 , 43 , 44 . The full extent to which DNA methylation altering NAP occupancy contributes to gene expression changes in E. coli K-12 is unknown. A recent study analyzing total protein occupancy data – produced by high resolution in vivo protein occupancy display (IPOD-HR) – reported an underrepresentation of Dam target motifs in extended protein occupancy domains (EPODs) 45 , 46 . EPODs are ≥1 kilobase regions of the genome that have a continuously high protein occupancy signal; EPODs can be considered functional analogs to eukaryotic heterochromatin as EPODs are primarily formed by dense clusters of NAPs that coat DNA and silence local transcription 46 , 47 . Our observation that Dam sites are underrepresented in EPODs – in addition to the regulatory cross-talk demonstrated with the pap operon and other examples – led us to speculate that there is a causal relationship between DNA methylation state and protein occupancy which contributes to the formation of EPODs in E. coli K-12 MG1655. We thus hypothesized that a methylation-deficient version of MG1655 would show large-scale aberrations in chromatin structure (in particular, the formation and locations of EPODs) which might alter the regulation of silenced wild-type genomic regions. To test for such changes, we cloned single deletion mutants of Dam and Dcm ( Δdam and Δdcm , respectively) and a double deletion mutant of both Dam and Dcm ( Δdam/Δdcm ), and we performed global protein occupancy profiling (using the IPOD-HR method 46 ) and transcriptome abundance profiling (using RNA-Seq) on these strains to produce global protein occupancy profiles and identify EPOD locations. Our results indicate that, relative to wild-type cells, DNA methylation-deficient mutants of E. coli K-12 MG1655 are not characterized by large-scale changes in genomic protein occupancy such as the formation of EPODs. Thus, the reduced abundance of Dam sites in EPODs does not cause EPOD formation (at least, not through reduced methylation density), but rather, is likely the consequence of some shared feature of these regions. However, we have identified a small number of loci with dense clustering of Dam methylation sites for which our data shows methylation-dependent changes in local RNA polymerase and total protein occupancy. Our transcriptome profiling data indicates that deletion of dam and/or dcm results in significant expression changes within some functional gene categories including SOS response, flagellar synthesis, and translation, but these expression changes appear to result from indirect regulatory consequences of methyltransferase deletion rather than being due to perturbation of interactions between DNA methylation and regulatory proteins at gene promoters. As such, there are no changes in local transcription associated with the dense clusters of Dam sites. Dam deletion mutants were, however, characterized by a swimming motility-deficient phenotype which is likely associated with the downregulation of genes involved in flagellar synthesis. Thus, we find that DNA methylation does not control the overall protein occupancy landscape of the E. coli genome, and that changes in gene regulation are generally an indirect effect of loss of Dam methylation, rather than a direct regulatory consequence of local methylation state. METHODS Bacterial strain construction The “WT” parental strain of Escherichia coli K-12 MG1655 was obtained from Dr. Haley Amemiya, who sourced it from Hani Goodzari (Tavazoie Lab, then at Princeton University) in 2009 as described in Amemiya et al. , 2022 47 . This “WT” isolate is isogenic with ATCC 700926 except for an IS1 insertion in dgcJ 47 , 48 . Δdam, Δdcm , and Δdam/Δdcm strains were constructed from the parental “WT” via P1 transduction of a FRT-flanked kanR marker from corresponding knockout strains in the Keio collection 49 , 50 . The kanR marker was excised through electroporating the pCP20 helper plasmid – which encodes for Flp recombinase – leaving a small scar in place of the indicated genes’ original open reading frames 51 . Isolated transformants were grown overnight at 42℃ to remove the temperature-sensitive pCP20, and these overnight cultures were non-selectively purified on LB plates grown overnight at 37℃. Candidate colonies were replica plated onto LB and selective plates to confirm the loss of the kanR marker and pCP20 plasmid. Sanger sequencing verified the deletion of the indicated gene with the replacement of a small scar. ΔlrhA and Δdam/ΔlrhA strains were constructed through P1 transduction of FRT-flanked kanR from corresponding Keio collection strain followed by pCP20-mediated recombination as described above. All constructs were verified using Sanger sequencing through AZENTA Life Sciences GENEWIZ ® . Media and culture conditions LB (Lennox) media (10 g/L tryptone, 5 g/L yeast extract, 5 g/L NaCl) was used for the above cloning, recovery of cryogenically-preserved E. coli cells, and culturing for motility assays. 15 g/L bacteriological agar was added for plates. MOPS-RDM corresponding to the fully supplemented version of MOPS defined medium in Neidhardt et al. , 1974 52 (with 0.4% glucose as a carbon source) was used to grow E. coli cells for IPOD-HR, motility assays, and RNAP-ChIP. 15 g/L bacteriological agar was added to the MOPS-RDM recipe to make plates. Minimal MOPS media was made as specified in Neidhardt et al. , 1974 52 using 0.2% w/v glucose as a carbon source. PYE (Peptone Yeast Extract; 2 g/L peptone, 1 g/L yeast extract, 1mM MgSO 4 , pH 6.0) was used to grow Caulobacter crescentus cells to produce a spike-in reference for IPOD-HR. 20 g/L bacteriological agar was added for plates. Cell growth and harvest for IPOD-HR Our procedures for IPOD-HR largely follow those described in Amemiya et al ., 2022 47 . Cryogenically preserved cells were streaked onto a plate and isolated colonies were subsequently grown in the same media as used for plating (MOPS-RDM for E. coli , PYE for C. crescentus ) overnight at 37°C with shaking at 200 rpm. The culture was back-diluted into fresh, prewarmed media to an OD600 of 0.003 the next day. The culture was grown to the target OD600 of 0.2 and a 500 µL aliquot (for RNAseq) was taken and added to 1 mL of RNAprotect Bacteria Reagent (Qiagen, Hilden, Germany) and preserved according to the manufacturer’s instructions. The remainder of the culture was treated with a final concentration of 150 μg/ml rifampicin and returned to the same growth conditions for another 10 minutes. The cultures were then rapidly pipetted into 50-mL conical tubes and mixed with concentrated formaldehyde/sodium phosphate (pH 7.4) buffer sufficient to yield a final concentration of 10 mM NaPO4 and 1% w/v formaldehyde. Crosslinking proceeded for 5 min at room temperature with 300 rpm shaking, and then quenched with an excess of glycine (final concentration 0.333 M) for 10 min with 300 rpm shaking at room temperature. Cells were then chilled on ice for 10 minutes and then pelleted and washed twice with ice-cold phosphate-buffered saline (PBS). The resulting pellets were dried by pipetting residual liquid, and the tubes were then snap-frozen in a dry ice-ethanol bath before being stored at −80°C. Cell lysis and DNA preparation When resuspending frozen cell pellets, two pellets (taken from a single biological replicate) of each sample were separately resuspended with spike-in, sonicated, and then combined into one tube immediately after sonication. Individual frozen C. crescentus spike-in cell pellets were resuspended in 600 µL of 1x IPOD lysis buffer (10 mM Tris HCl, pH 8.0; 50 mM NaCl) containing 1x protease inhibitors (Roche Complete Mini, EDTA free, Roche Diagnostics GmbH, Mannheim, Germany) and 1.5 µL of ready-lyse (Epicentre, Madison, WI). The spike-in resuspension was used to resuspend one of the sample cell pellets, and then the resuspended cells were incubated for 15 minutes in a 30°C water bath. Sonication was then performed on all samples using a Branson digital sonifier with a microtip at 25% amplitude for four pulses of 5 seconds with a 5 second rest between each pulse; samples were kept in an ice/water bath during sonication. The two separate tubes for each biological sample were then combined. DNA digestion was performed by adding to the sonicated lysates 120 μg RNase A (Thermo Fisher Scientific, Waltham, MA), 12 μL DNase I (Fisher product #89835), 10.8 μL 100 mM MnCl2, and 9 μL 100 mM CaCl2, and then incubating on ice for 30 minutes. The digestion was quenched with 100 μL of 500 mM EDTA (pH 8.0), followed by clarification by centrifuge for 10 minutes at 13,000 rpm at 4°C. Aliquots were taken from the clarified lysate for IPOD-HR interface extraction, RNA polymerase chromatin immunoprecipitation, and cross-linking reversal and recovery of DNA as previously described 46 . The procedures described in that reference were replicated here, apart from all the 2-minute centrifuging steps instead being done in 4 minutes. For DNA recovery, standard phenol-chloroform extraction as ethanol precipitation as described in Ausubel F, 1998 53 , and the dried DNA was resuspended in 100 μL of TEe (10 mM Tris pH 8.0; 0.1 mM EDTA pH 8.0) for input samples, 20 μL of TEe for RNAP-ChIP samples, and 50 μL of TEe for IPOD samples. RNA isolation and sequencing preparation RNA pellets were removed from −80°C and then immediately resuspended in 100 μL TE buffer (10 mM Tris pH 7.5; 1 mM EDTA). The resuspended pellet was treated with 1 μL lysozyme (Ready-Lyse; Lucigen, Ltd.) and incubated for 10 minutes at 4°C, followed by treatment with 10 μL proteinase K and incubation for 10 minutes at room temperature with vortexing every 2 minutes. RNA was then isolated using a Zymo RNA Clean and Concentrate 5 Kit twice for each sample, with a DNase digestion (25 μL eluate from first Zymo clean-up, 58 μL nuclease-free water, 10 μL 10X DNase Reaction Buffer, 2 μL RNase inhibitor, 5 μL Baseline Zero DNase) at 37°C for 30 minutes in-between Zymo kit clean-ups. Samples were then ribo-depleted using a NEBNext rRNA Depletion (Bacteria) Kit according to manufacturer’s instructions – with the exception that 10 μL of RNA sample, containing 1 μg of RNA, was used for probe hybridization. Following ribo-depletion, samples were again cleaned-up with the Zymo Clean and Concentrate 5 Kit and then prepared for sequencing using the NEBNext Ultra II Directional RNA Sequencing Kit and then sequenced as described below for DNA samples. Preparation of next-generation sequencing (NGS) libraries DNA samples were prepared for Illumina sequencing using NEBNext Ultra II Library Prep Kit (NEB product #E7103) and NEBNext Muliplex Oligos for Illumina (96 reactions, NEB product #E6442S). Deviations from manufacturer’s directions to account for low average fragment sizes are described in Freddolino et al. , 2021 46 . All libraries were sequenced on an Illumina NextSeq instrument. Analysis of NGS data, read quality control and preprocessing, DNA sequencing and protein occupancy calling, and feature calling were performed as previously described in Freddolino et al. , 2021 46 . However, we used here a more recent version of the IPOD-HR pipeline ipod_v2.5.7 which can be obtained from https://github.com/freddolino-lab/ipod (matching commit e2c2889 in that repository). Some of the software used in IPOD-HR version 2.5.7 includes: cutadapt v3.5 54 , trimmomatic v0.39 55 , bowtie2 v.2.4.4 56 , and samtools v1.1.4 57 ; definition files for building a singularity container exactly matching our workflow are available on the github repository noted above. A summary of the changes introduced between the IPOD-HR version utilized in Freddolino et al ., 2021 46 and the verison utilized here are summarized as follows: Following quantile normalization, each replicate of each data type (IPOD, ChIP, input DNA) was median normalized to 100. A pseudocount of 0.25 was then added to each datum. Log 2 ratios of IPOD or ChIP data relative to input data were calculated for each set of paired replicates. The log 2 ratios were converted to robust z-scores and log 10 p-values for visualization as described in Freddolino et al ., 2021 46 . 95% confidence limits and mean estimates were calculated for log 2 ratios, log 10 p-values, and robust z-scores using jackknife sampling of the scores for all three biological replicates of each data type. EPOD calling was performed similar to as described in Freddolino et al ., 2021 46 with the following deviations: EPOD seed regions were identified as any region at least 1,024 basepairs in length over which the median of a 768 bp rolling median exceeded the overall 90 th or 75 th percentile in the case of strict or loose EPOD calling, respectively, of a 256 bp rolling median over the entire chromosome. EPODs from all biological replicates of each given condition were “merged” into single, contiguous genomic intervals to assess the degree to which EPODs from replicate conditions overlap. EPODs were called separately at the biological replicate level, and then EPOD locations with low reproducibility were dropped from analysis based on an upper limit of 0.05 for the irreproducible discovery rate 58 . Methylation motif flagging To characterize the occupancy changes observed across genotypes relative to the methyltransferase target sites, we scanned each base pair of the E. coli U00096.3 genome and assigned each base pair a motif flag. Methyltransferase target sites were identified based on whether a sequence of base pairs matched the target motif of each methyltransferase: “GATC” for Dam, “CCTGG” or “CCAGG” for Dcm. Scanning for these motifs was done using the motifs package from Biopython 59 . The output of this python v3.10.2 script was a listing of each methyltransferase target motif location in the genome. This information was used to create a file containing a list of every single base pair in the genome accompanied by an appropriate motif flag indicating membership to a methyltransferase target site. IPOD-HR and RNAP-ChIP occupancy at individual and clustered methylation sites To capture more of the genomic context surrounding methylation sites, the slop command from bedtools v2.30 60 was used to add 50 bp extensions to the start and end positions of each methylation site feature. The IPOD-HR and RNAP-ChIP occupancy scores at the extended methylation site features were found using bedtools intersect . Violin plots of the occupancy scores at methylation sites were made using seaborn v0.11.2 61 . The density of methylation sites at genomic loci was determined by counting the number of methylation sites within each extended methylation site, and this count was then added as a flag to the extended methylation site. Occupancy subtractions between genotypes were done to highlight occupancy changes unique in mutants relative to wild-type. To subtract occupancy between genotypes, negative values were adjusted to “0” for all genotypes and then wild-type occupancy was subtracted from each mutant occupancy track. Read end analysis To identify the read ends of each input sample, bedtools genomecov -ibam with the -5 and -3 arguments was called on each BAM file output by the IPOD-HR alignment pipeline with the -bg argument used to produce bedgraph files. The 5’ and 3’ read ends were then combined into one file for each sample and the read end count at each position was normalized by the total number of million read ends within each sample. 100 basepair flanks were added to each end of the “7 Dam Site” cluster 100 basepair windows and bedtools intersect was used to find the normalized read end counts at these dense Dam site clusters. Heatmaps were generated using seaborn . EPOD analysis Symmetrized overlap distance was calculated as shown in Equation 1 and as previously described in Amemiya et al. , 2022 47 . Overlapping “strict” and “loose” EPODs were found using bedtools intersect . The frequency of Dam or Dcm sites in EPODs for each condition was calculated using bedtools intersect and then divided by the genomic total frequency of Dam or Dcm sites, respectively. Equation 1 : Calculation of Symmetrized Overlap Distance, which quantifies the overlap between two EPOD sets A and B , as previously described in Amemiya et al. , 2022 47 . X Y is the fraction of condition Y ’s strict EPODs which are genomically overlapped (in at least 1 basepair position) with the loose EPODs in condition X . For analysis of the representation of Dam sites in EPODs while controlling for the AT% of EPODs, each strict EPOD called for wild-type (“genomic EPODs”) was assigned to one of 10 evenly populated bins that were discretized based on AT content (AT%). 1000 random shufflings of the EPOD genomic locations (“shuffled EPODs”), allowing the shuffled locations to overlap original EPOD locations (“overlaps”) or not (“no overlaps”), were produced using bedtools shuffle . The shuffled EPODs were then assigned to the AT% bins, and within each AT% bin the Dam sites per kilobase for shuffled EPODs and genomic EPODs were compared by Poisson regression using R v4.1 62 and plotted with ggplot2 v3.3.5 63 . RNAseq analysis The NGS data produced from Illumina sequencing of the RNA samples was processed through the IPOD-HR pipeline as described above up to but not including the alignment step. The Rockhopper v2.0.3 RNAseq analysis system 64 – 66 was then utilized to align the processed RNAseq reads to the U00096.3 genome and identify transcripts. Rockhopper returns q-values which represent the statistical significance of differential expression of each transcript between conditions. The log 2 -fold change in transcript values were calculated, with directionality assigned based on whether mutant transcript value was greater (positive) or less (negative) than the wild-type transcript value; plots were generated using matplotlib v3.5.1 67 . iPAGE We applied the iPAGE software previously described 68 to perform gene set enrichment analysis. To produce the data for running iPAGE, the q-values from the Rockhopper analysis described above which compared the transcripts between our mutant and wild-type genotypes were log 10 -transformed and assigned directionality (positive or negative) based on whether the transcript is higher (positive) or lower (negative) in abundance in the mutant genotype relative to wild-type. The directional log 10 q-values were fed into iPAGE as a continuous variable, and so iPAGE created equally populated discrete bins to rank the directional log 10 q-values and calculate their representation within each bin for all transcripts associated with a given GO term. Motility regulon expression analysis The regulons of each regulator of flhDC were identified using the transcription factor to gene pairing database reported through RegulonDB 69 . Expression changes between each of the methyltransferase mutants and wild-type were found using the log 2 -ratios of Rockhopper-derived transcript expression values for each regulator and each target. The degree to which changes in expression of regulon components are consistent with the reported mode of action for each regulator-target pair (activator or silencer; dual regulators were ignored) was calculated by adjusting the sign of the log 2 -ratios (positive if the regulator’s mode of activity matches the expression change of the target, otherwise negative). The mean of directional log 2 -ratios was calculated to then evaluate the coherence of the regulator’s expression change with the concerted expression change across the regulator’s entire known regulon, here referred to as the “concerted log 2 -fold change in expression of regulon”. This value can then be compared to the log 2 -fold mutant versus wild-type expression change of the regulator for each regulon to assess whether there is evidence for a coherent increase or decrease in regulatory activity across a given regulon. Motility assays Tryptone motility plates were made with 10 g/L of tryptone, 5 g/L of NaCl, and 3 g/L bacteriological agar, similar to those used in previously described motility assays 70 . MOPS-RDM and MOPS-Minimal media were also prepared and turned into motility plates by adding 3 g/L bacteriological agar and otherwise following the recipes listed above. Plates were poured evenly by serological pipette (20 mL/plate) and left on bench to dry overnight and then stored at 4C the next day. Plates were not used if more than 2 months had passed since pouring them. Cryogenically preserved cells were streaked out on standard LB-agar plates, and then isolated colonies were grown in media of the same type for 12 hours in the case of LB/Tryptone broth and MOPS-RDM and 24 hours in the case of MOPS-Minimal. At the end of these growth periods, 1 mL of each culture was pelleted in a microcentrifuge for 3 minutes at 16,100xg at 4C. The supernatant was discarded, and the pellets were gently resuspended in 100 uL of sterile PBS pH 7.4. OD600 measurements were taken, and then additional PBS volumes were added to each resuspended pellet to normalize all samples to 80% of the OD600 of the lowest sample. Any condensate on the lid of the plates was wiped off using a sterile replica plating velvet. A small filter disk soaked with 10 uL of 10mM aspartic acid (as a chemoattractant) was then added to the center of MOPS-Minimal plates. 1 uL of each OD-normalized sample was then spotted onto all motility plates. We note to take care that the pipet tip should almost touch – but not break the surface tension – of the motility agar before dispensing the sample. After the plates were spotted, the plates were left facing up (media on bottom) and carefully parafilmed. The plates were then collectively placed into a Ziploc plastic bag containing some damp paper towels to prevent drying of plates. The bagged plates were then transferred to the 37C incubator for at least 12 hours before imaging at the first timepoint. After collecting the first timepoint, the plates were flipped upside-down (media on top) and were subsequently imaged every ∼2 hours. To normalize the brightness of the motility plate images, the median brightness of each image was found using imagemagick v7.1.04 71 convert with the -colorspace gray argument. The median of median brightnesses across all images was then calculated, and each image was adjusted in brightness to the value of the median of image-wise median brightness values using the imagemagick convert -evaluate Multiply argument. RESULTS Dam sites are statistically depleted in extended protein occupancy domains while controlling for AT content We previously found, in wild-type E. coli K-12 MG1655, that there is a statistically significant underrepresentation of Dam sites within regions of the genome covered by EPODs relative to non-EPOD regions 46 . To control for the difference in AT% between EPODs and non-EPOD regions and address the possibility that the depletion of Dam sites in EPODs is caused by the AT-richness of EPODs, we compared the frequency of Dam sites within EPODs to the frequency of Dam sites at other loci of the same length as each EPOD (“shuffled” EPODs). We assigned each wild-type EPOD (“genomic” EPODs) to 10 equally populated bins defined by AT%, assigned 1000 permutations of shuffled EPODs to these AT% bins, and we performed Poisson regression analysis with terms for “genomic” (real) versus “shuffled” EPODs as well as AT% bin membership. Our results show that “genomic” EPODs contain significantly fewer Dam sites than “shuffled” EPODs while incorporating for the AT% bin membership term, and this is true both when shuffled EPODs are restricted from being shuffled to the positions originally occupied by genomic EPODs (−0.069 regression coefficient estimate for “genomic” EPODs, −0.120 to −0.017 95% confidence interval, 0.0104 p-value) and when shuffled EPODs are permitted to overlap genomic EPOD positions (−0.110 coefficient estimate for “genomic” EPODs, −0.170 to −0.064 95% confidence interval, 1.6 x 10 -5 p-value). We performed this same Poisson regression analysis for Dcm sites and found no sign of anticorrelation for when we do not permit “shuffled” EPODs to overlap “genomic” EPODs (−0.011 coefficient estimate for “genomic” EPODs, −0.072 to 0.050 95% confidence interval, 0.734 p-value) and statistically significant anticorrelation – but much weaker than the anticorrelation for Dam sites – when we do allow “shuffled” EPODs to overlap “genomic” EPODs (−0.065 coefficient estimate, −0.130 to −0.004 95% confidence interval, 0.038 p-value). These results show that even after controlling for AT% the “genomic” EPODs have significantly fewer Dam sites than expected by random chance, thus reinforcing the basis of our hypothesis that there may be an association between EPOD formation – and more generally, protein occupancy – and DNA methylation (i.e., that the presence of Dam methylation might inhibit the NAP binding that gives rise to EPODs). Loss of DNA methylation minimally alters protein occupancy on the E. coli K-12 MG1655 genome To characterize a set of protein binding events that may be dependent on DNA methylation state, we utilized the IPOD-HR methodology to profile changes in the global protein occupancy of the E. coli K-12 MG1655 chromosome when either or both of the genes encoding the two primary methyltransferases, dam and dcm , are deleted. IPOD-HR has been previously described and applied to characterize global protein occupancy changes in MG1655 NAP deletion mutants 46 , 47 . The IPOD-HR methodology involves crosslinking and Illumina sample preparation like other protein-DNA extraction and enrichment methodologies such as chromatin immunoprecipitation followed by sequencing (ChIP-seq) 72 . To produce a global profile of all protein occupancy across the genome, IPOD-HR utilizes physicochemical principles to enrich crosslinked protein-DNA complexes at an aqueous-organic interface during phenol/chloroform extraction. ChIP for RNA polymerase (RNAP-ChIP) is also performed on the same biological samples as used for IPOD-HR to remove the RNAP signal from the IPOD-HR occupancy profile. Producing RNAP-ChIP data allows us to isolate changes in occupancy of RNA polymerase (which we have observed to be correlated with gene expression changes) and the subtraction of the RNAP-ChIP signal from the IPOD-HR total protein signal highlights changes in transcription factor and NAP occupancy that might otherwise be obscured by replacement with RNAP 46 . We also performed RNAseq in parallel with IPOD-HR and RNAP-ChIP on E. coli MG1655 (WT) and our methyltransferase deletion mutants ( Δdam, Δdcm , and Δdam/Δdcm ) to characterize changes in gene expression that may result from changes in protein occupancy when DNA methylation is perturbed. To explore the possibility of a global change in protein occupancy local to DNA methylation sites when the primary DNA methyltransferases are deleted, we first identified every potential Dam or Dcm site based on the appearance of their target motifs (5’-GATC-3’ or 5’-CCWGG-3’, respectively) in the E. coli K-12 MG1655 U00096.3 sequence. To capture the genomic context around each methylation site, we captured the 50 basepairs (bp) both upstream and downstream of each Dam or Dcm site, which generated 104 bp windows centered on each Dam site and 105 bp windows centered on each Dcm site. An association between methylation state and protein occupancy was then made by creating a distribution of the means of IPOD-HR or RNAP-ChIP occupancy scores within each methylation site window and comparing across genotypes ( Figure 1A ). We applied one-sample version of the Bayesian Estimation Supersedes the t-Test (BEST) analysis method to generate 95% credible intervals which for all conditions had a range of less than 0.1 and found that the distribution of occupancy scores is stable across genotypes. Overall, we observe no substantial differences in local IPOD-HR or RNAP-ChIP protein occupancy across all DNA methylation sites when dam and/or dcm are deleted. Download figure Open in new tab Figure 1: (A) Distribution of IPOD-HR and RNAP-ChIP occupancy scores in genomic windows centered on each Dam (104 bp window) or Dcm (105 bp window) target motif. (B) Symmetrized Overlap Distances calculated as described in Amemiya et al. , 2022 47 to assess similarity in global EPOD composition between strains. A value of 0 indicates that all EPODs between the two strains overlap. (C) Normalized fraction of total Dam sites found within EPODs for each strain. The fraction of Dam sites within EPODs was normalized by the fraction of total genomic basepairs covered by EPODs, and 95% confidence intervals were calculated through jackknife resampling of the EPOD genomic positions. The y-axis is log- scaled. The statistical under-representation of Dam sites in EPODs also motivated us to explore how EPOD locations change relative to DNA methylation sites when dam and/or dcm are deleted. To characterize the set of EPOD locations for each genotype, EPOD calling was performed on IPOD-HR data as previously reported with some minor modifications (as noted in Methods) 46 . Symmetrized Overlap Distances (SODs) were calculated as previously described in Amemiya et al ., 2022 47 to analyze similarity in the set of EPOD locations between genotypes ( Figure 1B ). A SOD score of “0” represents perfect overlap of EPOD locations between sets and a score of “1” represents zero overlap in EPOD locations between sets. Given that the SOD scores between any compared genotypes were 0.04 or lower, we observe negligible changes in the set of EPOD locations when dam and/or dcm are deleted. We additionally found no substantial change between genotypes in the fraction of Dam or Dcm target sites within EPODs ( Figure 1C ), although a slight decrease in the fraction of Dam sites in EPODs is apparent. These results indicate that there are few to no changes in large-scale protein occupancy features, such as EPOD formation, when DNA methylation is perturbed through methyltransferase deletion. To evaluate whether the loss of methylation signals might still alter gene expression within EPODs when compared to non-EPOD regions of the genome, we performed RNA-seq experiments on all four strains included in our study and calculated the median mutant versus wild-type log 2 FC of transcripts within EPODs and subtracted the median mutant versus wild-type log 2 FC of transcripts outside of EPODs. We found a small but significant decrease in median expression of transcripts within EPODs versus outside of EPODs when dam and both dam and dcm deleted, but there is no difference in median expression between within and outside of EPODs when just dcm is deleted (median difference of mutant versus wild-type log 2 FC of transcripts within EPODs minus outside of EPODs and Wilcoxon two-sided rank-sum p-value: 0.06 and 1.1 x 10 -5 for Δdam , 0.00 and 0.95 for Δdcm , 0.15 and 3.2 x 10 -8 for Δdam/Δdcm ). Thus, there is a small relative decrease in transcription inside vs. outside of EPODs when dam is deleted, although especially considering the absence of systematic occupancy changes, the mechanism and biological significance of these changes remains unclear. Additional analysis of our RNA-seq results is provided below. Protein occupancy signal in dam deletion mutants is decreased at dense clusters of Dam target sites Considering that multiple DNA methylation events in close genomic proximity could induce a greater degree of DNA curvature 73 , we hypothesized that genomic regions with dense clusters of methylation sites may experience more pronounced protein occupancy changes when DNA methylation is perturbed. In addition, it has been shown that certain DNA-binding proteins such as SeqA preferentially bind to regions with multiple proximal Dam sites 21 , 74 . These considerations led us to consider how the change in IPOD-HR occupancy differences between methyltransferase deletion mutants and the wild type might vary as a function of local methylation site density. Here we observe a negative correlation between mutant-specific IPOD-HR signal and Dam Site Density when dam is deleted. In other words, we find less total protein occupancy at dense clusters of Dam sites when dam is deleted, whereas locations with lower Dam site densities are unaffected, as are Dcm sites. Our analysis defined twelve unique loci containing a Dam site annotated with a Dam Site Density of 6 as well as three unique loci with a Dam site annotated with a Dam Site Density of 7; we refer to these regions collectively as “high-density Dam site clusters”. Each of these high-density Dam site clusters appear within ORFs and are thus absent from promoters or intergenic regions. Despite the Δdam -associated increase in RNA polymerase at most of these high-density Dam site clusters, there is only one locus which presents with a significant change in proximal gene expression, which is the oriC- adjacent gene mnmG (Rockhopper q-values for transcript count of mnmG : ∼.0017 for Δdam vs. WT, ∼1.0 for Δdcm vs. WT, ∼.00036 for Δdam/Δdcm vs. WT). Overall, it appears that the Δdam -dependent change in RNAP and total protein occupancy at high density Dam site clusters are not impacting expression of known local transcripts (see GEO dataset GSE279866). One locus of interest featuring a cluster of seven Dam sites is at the terminal end of the selB coding region ( Figure 2BC ). Here we observe Δdam -dependent peaks in RNAP occupancy both at the Dam site cluster as well as at the promoter immediately upstream of selB . However, transcript levels of selB do not change substantially in Δdam genotypes (Rockhopper mutants versus wild-type log 2 FC / q-values of selB : −0.21 / 0.69 for Δdam vs. WT, 0.072 / 0.27 for Δdcm vs. WT, −0.16 / 3.6 x 10 -4 for Δdam/Δdcm vs. WT). The gene with a promoter immediately downstream of the Δdam -dependent RNAP-ChIP peak at the selB Dam site cluster, yiaY , is not transcribed in any of our genotypes (see GEO dataset GSE279866). There is a decrease in IPOD-HR signal associated with the increase in RNAP-ChIP signal at the selB Dam site cluster in Δdam strains, but we cannot find any reports on what protein may bind to this region. Download figure Open in new tab Figure 2: (A) Distribution of changes in mean IPOD-HR or RNAP-ChIP occupancy scores in 104 bp windows centered on each Dam target motif; positive scores indicate higher occupancy in the indicated mutant relative to WT. “Site Density” on the x-axis refers to the number of Dam sites within each window. Asterisks represent p-values of < 0.01 by Wilcoxon signed rank test with Bonferroni correction. At the higher site densities (6 and 7 site density) there is a lack of statistical power due to a small number of loci with such high methylation site densities. (B) Genomic context of selB showing 512 bp rolling mean of IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Brown boxes above markers on the “Dam Sites” tracks indicate “7 Dam Site Density” clusters of interest. Genes are differentially colored based on their membership to functional gene clusters. The dashed box designates the locus which is shown in panel C. (C) Zoomed in view of selB (corresponding to the boxed region of panel B ) showing IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. We also identified a 7 Dam site cluster at the terminal end of prpE ( Figure 3AB ), which is comparable to the selB case in that it features an increase in RNAP-ChIP signal and a decrease in IPOD-HR signal in our Δdam genotypes. In contrast to the selB case, for prpE the Δdam -dependent peak in RNAP-ChIP appears ∼200 basepairs downstream of the Dam site cluster, and so the RNAP-ChIP peak is proximal to the promoter for codB , which shows modest but not statistically significant increases in transcript levels in all of our methyltransferase deletion strains (Rockhopper mutants versus wild-type log 2 FC / q-values of codB : 0.30 / 1.0 for Δdam vs. WT, 0.45 / 1.0 for Δdcm vs. WT, 0.31 / 0.11 for Δdam/Δdcm vs. WT). Given that there is a similar magnitude of upregulation of codB in Δdcm as compared to Δdam , the upregulation of codB may not actually be associated with the Δdam -dependent increase in RNAP-ChIP, but rather is likely statistical noise. prpE is not actively transcribed in any of our strains (see Supplementary Data). Download figure Open in new tab Figure 3: (A) Genomic context of prpE showing 512 bp rolling mean of IPOD-HR (blue occupancy trace) or RNAP- ChIP (red occupancy trace) robust z-scores. Brown boxes above markers on the “Dam Sites” tracks indicate “7 Dam Site Density” clusters of interest. Genes are differentially colored based on their membership to functional gene clusters. The dashed box designates the locus which is shown in panel B . (B) IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores in the immediate vicinity of prpE . (C) As in panel A , showing the genomic context around recBD . (D) As in panel B , for the boxed region indicated in panel D . There are two proximal 7 Dam site clusters within the recB and recD coding regions ( Figure 3CD ). We find this notable because of the involvement of dam , recB , and recD in E. coli DNA mismatch repair, and the fact that previous attempts to delete both dam and recB found such a strain to be inviable 75 . Despite an increase in the RNAP-ChIP signal at the recBD promoter in our Δdam genotype, the transcript levels of recB in fact show a small decrease in our dam mutant strains (Rockhopper mutants versus wild-type log 2 FC / q-values of recB : −0.043 / 0.65 for Δdam vs. WT, 0.082 / 0.14 for Δdcm vs. WT, −0.087 / 7.7 x 10 -4 for Δdam/Δdcm vs. WT) and recD (Rockhopper mutants versus wild-type log 2 FC / q-values of recD : −0.31 / 0.0052 for Δdam vs. WT, −0.057 / 0.075 for Δdcm vs. WT, −0.31 / 7.1 x 10 -5 for Δdam/Δdcm vs. WT). The recD Dam site cluster is in the middle of the gene body while the 7 Dam site cluster in recB is a few hundred basepairs downstream of a putative recB promoter (defined by the presence of an RNA polymerase ChIP peak), and the IPOD-HR occupancy at both sites is substantially decreased in our Δdam genotype. Rifampicin – at the concentration added to our cells used for RNAP-ChIP – prevents promoter clearance, which leads to a build-up of RNAP at active promoters 76 , 77 . We thus hypothesized that the Δdam -dependent RNAP-ChIP peak observed at these Dam site clusters may result from RNAP that has been directly recruited for transcription – perhaps of a small RNA. However, there does not appear to be any increase in RNA-seq reads local to the RNAP-ChIP peaks at these Dam site clusters in Δdam strains ( Supplementary Figure 1 ). We also considered that these RNAP-ChIP peaks may result from RNAP being stalled, possibly at a Dam methylation-directed repair site due to the increase in DNA damage and upregulation of SOS response in Δdam strains 78 – 80 . To investigate the possibility of DNA damage, we produced heatmaps of the normalized frequency of read ends at some of the 7 density Dam site clusters, but none of the heatmaps show a Δdam -dependent pattern in read end accumulation local to the RNAP-ChIP peaks ( Supplementary Figure 2 ), indicating no clear signature of increased strand breaks near the Dam site cluster (although any such accumulation may well have been obscured anyway by the fragmentation steps inherent to our purification protocols). Taken together, these results indicate that RNAP may be directly recruited to – but not actively transcribing – the high-density Dam site clusters in Δdam strains, or RNAP may be stalled but not because of DNA strand breaks; additional investigation would be required to distinguish between these possibilities. Methyltransferase deletion globally perturbs expression of multiple large regulons While global protein occupancy is generally stable across most methylation sites when DNA methylation is perturbed, deletion of dam and/or dcm has been associated with some changes in gene expression 21 , 22 , 38 , 81 . We produced RNAseq data which was analyzed through the Rockhopper analysis suite 64 – 66 and found gene expression changes across many operons in the Δdam and Δdam/Δdcm genotypes, while the Δdcm strain showed relatively fewer significant changes in gene expression ( Figure 4 ). In the Δdam single mutant, the most positively expressed genes relative to wild-type are associated with DNA damage response, and the most down-regulated genes are primarily members of the gatYZABCDR operon which is involved in galactitol catabolism 82 ( Table 1 ). Both the most up-expressed and most down-expressed genes in Δdcm encode gene products for transmembrane transport. Aside from the genes already represented in the single deletion mutants, the double deletion mutant Δdam/Δdcm is characterized by upregulation of maltooligosaccharide catabolism proteins encoded by malP and malQ 83 as well as downregulation of isoleucine and valine biosynthesis through ivbL 84 . Download figure Open in new tab Figure 4: (A) Δdam , (B) Δdcm, and (C) Δdam/Δdcm versus wild-type change in expression of genes. Purple genes are reduced in transcript abundance – while green genes are increased in abundance – in the mutant relative to wild-type. Some genes of interest are labeled by name. Below each volcano plot is shown gene set enrichment analysis for RNA-seq data across the indicated genotype (relative to wild type) for a selected subset of gene ontology terms. iPAGE reports the representation of directional log10(q-values) across the genes annotated with each Gene Ontology (GO) term – thus, a redder bin indicates an over-representation of genes from the specified GO-term (row) at that expression change bracket (column). View this table: View inline View popup Table 1: Transcripts showing the largest changes in abundance in each mutant genotype relative to wild-type, as calculated using Rockhopper. We then performed gene set enrichment analysis on our RNAseq data which further supports gene expression changes across multiple gene ontology (GO) categories in methyltransferase deletion mutants ( Figure 4 ). We found a decrease in expression of several genes associated with flagellum-dependent motility which was consistent in all methyltransferase mutants relative to wild-type. Previous findings that activation of SOS response, which is associated with DNA damage repair, occurs upon dam deletion were also reproduced here, likely caused by interference with hemimethylation-dependent mismatch repair and/or perturbation of normal replication initiation 21 , 85 . We also detected differential expression of gene products involved in maintaining transposons across all methyltransferase mutants, which supports previous findings that dam methylation impacts transposase expression and transposition activity 86 , 87 . Genes relating to translation and amino acid biosynthesis have also been reported to significantly change in expression in Δdam strains 20 , 80 , and our methyltransferase mutants all show substantial expression changes in translation and amino acid biosynthesis ( Supplementary Figure 3 ), but the mechanistic basis underlying the relationship between transcription of translation-related and DNA methylation has not been elucidated. Previously identified instances of local methylation-sensitive regulation are recapitulated in our data To systematically investigate the concordance of our data with previous reports of correlations between methylation and protein occupancy in E. coli strains 18 , 19 , 22 , 88 – 91 , we first reviewed the available literature to compile a list of candidate genes which had previously been studied in the context of potential contributions of Dam methylation to their cis-regulatory logic, requiring that those genes had (a) been investigated for change in transcript level based on methylation state of immediately upstream Dam or Dcm sites, or (b) had been reported as having persistently unmethylated immediately upstream Dam or Dcm motifs ( Table 2 ). We included data arising both from genetic deletions of dam or dcm , and from 5-azacytidine (5-aza) treatments, which blocks the addition of methyl groups to cytosine by Dcm 23 , 92 . View this table: View inline View popup Table 2: Summary of candidate genes for regulation by local Dam and Dcm methylation based on available literature, compared with our IPOD-HR, RNAP-ChIP, and RNAseq results. The “Experiment” column briefly describes the conditions in the citation that perturb methylation, and “Transcription effect” refers to the directionality of the change in expression (and methodology used to measure expression change) for the “Gene” when methylation is lost as reported in the citation. “X” indicates an observable change in occupancy pattern for the respective mutant relative to wild-type, and “?” indicates noisy signal at that locus which precludes interpretation. RNAseq results are also reported with respect to mutant versus wild-type. Militello et al ., 2014 23 identified that in both dcm deletion strain and in WT cells subjected to 5-azacytidine treatment, sugE is derepressed – our results agree with this finding as despite the Δdcm -dependent decrease in RNAP-ChIP occupancy at the sugE promoter ( Figure 5AB ) there is still an increase in sugE transcript levels in our Δdcm genotypes ( Figure 5C ). Conversely, Militello et al ., 2014 23 reported that there are several Dcm sites in the sugE promoter and body but the genome of E. coli MG1655 does not contain any Dcm sites proximal to sugE , and so any relation between Dcm activity and sugE expression does not appear to result from occupancy changes dependent on local methylation state, but rather is likely an indirect regulatory effect. Download figure Open in new tab Figure 5: (A) Genomic context of sugE showing 512 bp rolling mean of IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Genes are differentially colored based on their membership to functional gene clusters. The dashed box designates the locus which is shown in panel B. (B) Genomic locus of sugE showing IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. (C) Mutant versus wild-type change in expression where the red and blue arrows and gray “N/A” indicate the expected change in expression based on curated literature for sugE 23 , osmE 92 , dnaA 89 , flu 18 , 90 , glnS 88 , and ppiA 91 . Asterisks indicate statistical significance with a q-value less than 0.05 as calculated by Rockhopper. In a similar study, Militello et al ., 2016 92 found that 5-azacytidine treatment increases transcript levels of recN , dinD , dinG , rsmI , dinB , rmuC , and recA , and decreases transcript levels osmE and yqeC . Our dcm deletion results are consistent with these findings (in terms of the sign of the log fold change upon dcm deletion) for all of the genes showing increased transcript levels in 92 except for dinG, which we find to drop in expression in our Δdcm strain, albeit not significantly ( Table 2 ). For osmE and yqeC , our Δdcm strain shows repression in agreement with the 5-aza study, but yqeC is not expressed in any of our strains under our conditions. Therefore our dcm deletion genotype mostly recapitulates the expression changes observed from 5-aza treatment in Militello et al ., 2016 92 except for in the case of dinG where we report an opposing impact on gene expression. Several similar datasets have been obtained to study specific instances of regulation of transcription by Dam. Braun and Wright 1986 89 conducted an in vivo β-Galactosidase activity assay and S1 nuclease mapping in addition to in vitro transcription run-off experiments which all supported that loss of Dam methylation in the dnaA promoter leads to repression of dnaA . We find repression of dnaA in our Δdam genotypes, but we could not identify any protein occupancy change proximal to the dnaA promoter between our wild-type and Δdam strains in our IPOD-HR results, possibly due to the competition between DnaA and SeqA for binding to this region 93 , 94 (as IPOD-HR would not distinguish between the two factors) or the fact that all of our experiments are ensemble averages over actively growing populations. Correnti et al ., 2002 90 and Wallecha et al. , 2002 18 both investigated antagonism between OxyR and Dam methylation in the promoter of flu where loss of methylation led to OxyR binding which led to flu repression. Our data supports that dam deletion is associated with flu repression, but further assessment of OxyR-methylation antagonism is made difficult due to noisy IPOD-HR and RNAP-ChIP signal at the flu promoter which may be due to issues in data processing and quantitation as a result of repetitive sequence from the transposable element present in that region. Plumbridge and Söll 1987 88 performed in vivo β-Galactosidase activity assays which showed that dam deletion as well as mutation of Dam sites in the promoter leads to derepression of glnS . Here we find, consistently, that glnS is more strongly expressed in our Δdam strains. It is, however, notable that there is a marginal increase in RNAP-ChIP occupancy at the glnS promoter in only the Δdam single deletion mutant and IPOD-HR occupancy at this locus is roughly static across our genotypes ( Figure 6AB ). Download figure Open in new tab Figure 6: (A) Genomic context of glnS showing 512 bp rolling mean of IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Genes are differentially colored based on their membership to functional gene clusters. The dashed box designates the locus which is shown in panel B. (B) Genomic locus of glnS showing IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. (C) Genomic context of ppiA showing 512 bp rolling mean of IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Genes are differentially colored based on their membership to functional gene clusters. The dashed box designates the locus which is shown in panel D. (D) Genomic locus of ppiA showing IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Hale et al ., 2004 91 identified under various growth conditions the Dam sites that remain specifically unmethylated throughout the cell cycle. Of the genes reported to be proximal to these stably unmethylated Dam sites, we focus here on ppiA as in our data there are IPOD-HR occupancy changes at upstream Dam sites when dam is deleted ( Figure 6CD ). The Δdam -dependent loss in non-RNAP protein occupancy directly at Dam sites in a promoter region as seen with ppiA is precisely what we would expect to observe in a case of methylation-protein antagonism. However, it is not clear whether the Δdam -dependent occupancy change has an impact on transcription as there is a slight increase in RNAP-ChIP occupancy at the promoter in Δdam alongside a minimal – although statistically significant – decrease in ppiA transcript levels. Integrating across all previous compatible data that we could identify (as detailed above), our RNA-seq data show the same directions of expression changes as prior studies for 13/15 cases (although the changes were not always statistically significant). We did not, however, observe evidence for local changes in protein occupancy at the promoters for most of those genes in response to methyltransferase deletion (with ppiA being the primary exception), indicating that either the identities of bound proteins change but the existence of binding does not, that the regulation due to the targeted methyltransferase is indirect, or that we are not sensitive in our assay to any changes that might occur. dam deletion mutants show loss of motility and downregulation of flhDC One of the genomic loci with a dense clustering of 7 Dam sites was identified as the transcription start site of flgN , which encodes for a chaperone involved in cellular export of flagellum components 95 , 96 . Our RNAseq results show that flgN is downregulated in dam deletion mutants (Rockhopper mutants versus wild-type log 2 FC / q-values of flgN : −1.2 / 9.0 x 10 -16 for Δdam vs. WT, −0.20 / 0.017 for Δdcm vs. WT, −0.97 / 2.9 x 10 -21 for Δdam/Δdcm vs. WT). Additionally, there are mutant-specific IPOD-HR occupancy changes in the flgN promoter proximal to the Dam site cluster ( Supplementary Figure 4 ). To our knowledge there is no experimental evidence for what regulatory proteins might act on the promoter immediately upstream of flgN , but two distal upstream promoters that impact flgN expression have previously been found to be occupied by CsgD and FlhDC 97 – 99 . While csgD transcript levels remain near-zero for all our genotypes, flhC and flhD transcript levels are decreased in our Δdam strains, and FlhDC was previously shown to activate expression of flgN 99 , thus providing a plausible path of information flow from dam deletion to decreased flgN transcription. We next analyzed our datasets for information on the expression and protein occupancy of flhDC . While there is protein occupancy in the flhDC promoter, the occupancy pattern there appears to differ only marginally based on genotype, suggesting no major changes in the binding of regulatory factors upstream of flhDC ( Figure 7AB ). We examined the expression levels of the large set of known flhDC regulators across our genotypes to infer what regulators may be responsible for the occupancy signal in the flhDC promoter ( Figure 7C ). While this is an indirect inference, we also observed the mutant versus wild-type expression change across the regulons respective to each flhDC regulator to check which regulators of flhDC likely changed substantially in activity in each mutant (which would be indicated by the presence of changes in expression across the regulon of a factor that were coherent in sign with consideration of the effect of that regulator). The mutant versus wild-type log-fold change in expression of each regulon component was made positive if the change in expression matched the reported mode of regulation for the regulator-target pair or negative if the expression change opposed the annotated regulatory mode. The mean of these sign-changed log-fold expression changes within each regulon were then average to calculate the concerted log-fold change in expression of regulon for each regulator of flhDC ( Figure 7D , Supplementary Figure 5 ), with a more positive change indicating stronger evidence for systematic changes throughout the regulon of a given upstream factor in line with its known regulatory effects. Download figure Open in new tab Figure 7: (A) Genomic context of flhDC showing 512 bp rolling mean of IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Genes are differentially colored based on their membership to functional gene clusters. The dashed box designates the locus which is shown in panel B. (B) Immediate surroundings of flhDC (boxed region from panel A ) showing IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. (C) Regulatory diagram including mutant versus wild-type log-fold expression change for selected subset of regulators of flhDC . The lines ending in flat bar arrowheads indicate repression. (D) Concerted log 2 -fold change in expression of the regulon of a selected subset of the regulators of flhDC . (E) Representative motility assay plates for LB/Tryptone, MOPS-RDM, and MOPS-Minimal conditions. The black circle in the center of the MOPS-Minimal plate is a filter disc soaked in aspartic acid which acts as a chemoattractant. Of the known regulators of flhDC, LrhA stands out as having particularly high regulatory coherence scores in both of our strains lacking dam ( Fig. 7D ). LrhA is a repressor of flhDC transcription, and lrhA expression is increased in Δdam strains which is congruent with flhDC downregulation in Δdam genotypes 100 , and with the broader expression changes in the LrhA regulon in dam deletion mutants relative to WT. Fur and OmpR are also repressors of flhDC 101 , 102 with increased expression in Δdam strains, but there is only a weaker degree of concerted log-fold change in OmpR regulon expression for dam deletion mutants relative to wild-type, and the Fur regulon does not show signs of strong concerted expression changes( Figure 7CD ). To characterize how loss of DNA methylation impacts flagellar motility, we performed swimming motility assays with wild-type, Δdam, Δdcm , and Δdam/Δdcm strains. To further characterize how loss of LrhA regulation of flhDC impacts motility relative to the motility impact associated with dam deletion, we additionally tested ΔlrhA and Δdam/ΔlrhA strains for swimming motility. While previous swimming motility assays have utilized tryptone-based motility plates 70 , the use of MOPS-RDM in our IPOD-HR and RNAseq procedures motivated us to develop MOPS-RDM and MOPS-glucose motility plates. Across all media types used for the motility assays, dam deletion mutants consistently demonstrated a substantial loss of motility as observed by the swimming distance of cells over time ( Figure 7E ), as would be expected based on our RNA-seq results. lrhA deletion mutants appear to have a small loss of motility in all tested media conditions, and the Δdam/ΔlrhA strain displays slightly less swimming motility than the Δdam strain. While we observe decreased LrhA expression and regulatory activity in Δdam strains, the swimming motility phenotypes (and particularly the lack of apparent epistasis between dam and lrhA deletions) do not provide further insight into the nature of the relationship between LrhA and Dam in regulation of motility; one thing that is clearly apparent is that the loss of motility of Δdam mutants cannot be attributed solely to the increase in LrhA activity, since the phenotype persists in the Δdam/ΔlrhA double knockout. DISCUSSION Based on previous observations of a depletion of Dam methylation sites in extended, transcriptionally silent regions of high protein occupancy in the E. coli genome, we hypothesized that Dam methylation might play a global role in regulating the spread of NAP occupancy to control where EPODs occur, by inhibiting NAP occupancy in regions with relatively high Dam site densities. Contrary to our initial hypothesis, our results indicate that DNA methylation state (at least that arising from the native E. coli K12 DNA methyltransferases) does not substantially impact the global pattern of protein occupancy and EPOD formation at methylation sites. Thus, we observe that the genome-wide association between Dam sites and EPODs 46 is not causal, but likely reflects other evolutionary constraints acting on long-term versus newly acquired genomic regions. Many EPODs in E. coli K-12 MG1655 have been observed to be associated with prophages and transposable elements 47 , which were likely acquired more recently and may have experienced less selective pressure, over less time, for containing Dam sites relative to more native regions of the genome 86 , 87 , 103 . While our data does not demonstrate a global pattern of DNA methylation state regulating local protein occupancy, there may still be a small number of methylation sites where antagonism exists between DNA methyltransferases and regulatory proteins. We also note that our tested conditions were limited to standard growth in rich medium (MOPS-RDM), and thus it is possible that changes in occupancy that would occur under other growth conditions are missed. For example, our cells were harvested at exponential phase but Dcm methylation appears to have a more biologically significant impact during stationary phase 22 , 36 , 104 . Another limitation of note is that deletion of methyltransferases is not expected to produce stably hemimethylated Dam sites which might more specifically interact with some DNA-binding proteins relative to unmethylated or fully methylated sites (as is the case with SeqA) 105 – 107 , and thus we might miss changes in occupancy arising specifically from the presence of hemimethylated regions. Nevertheless, we did find that some protein occupancy changes caused by dam deletion are associated with loci featuring a dense clustering of methylation sites – such sites do show a decrease in total protein occupancy and an increase in RNA polymerase occupancy for dam deletion strains. Genomic regions with multiple proximal Dam sites have been previously identified as a potential regulatory element due to the poor processivity of Dam over such regions, resulting in hemimethylated sites 1 , 108 , 109 . Stably hemi- or unmethylated Dam sites are rare relative to fully methylated sites, and so they have been predicted to form specific associations with DNA-binding proteins such as SeqA 1 , 28 . However, loss of methylation at the dense Dam site clusters we identified does not appear to generally result in differential regulation of known local transcripts. We thus conclude that the Δdam -dependent changes in protein occupancy associated with dense clusters of Dam sites are primarily driven by the increased presence of RNA polymerase which is not transcriptionally active under our conditions, possibly due to increased promoter binding/transcriptional initiation without promoter clearance. We observed the presence of an RNAP-ChIP peak proximal to all of our observations of high-density Dam site clusters at the ends of gene bodies, and we speculated that this RNAP-ChIP peak results either from direct recruitment of RNAP or stalling of RNAP at this site during Dam-associated mismatch repair 28 , 78 , 79 . Our samples for RNAP-ChIP and IPOD-HR are treated with rifampicin before crosslinking, and rifampicin inhibits promoter clearance of RNA polymerase 46 , 76 , 77 . Thus, we hypothesized that RNA polymerase recruited to an upstream promoter could read through a gene, get stalled at the Dam site cluster, and then be prevented from dissociating from the DNA by rifampicin until formaldehyde crosslinking. While our read end analysis did not support the presence of DNA damage at these loci, any potential direct signature of accumulations of strand breaks could easily have been masked by our sample workup, and we still suspect that stalling of the RNAP by DNA repair machinery is possible due to other aspects of dysregulated replication in Δdam strains such as asynchronous replication initiation and DNA base mismatches 80 , 110 . Contradicting the alternative scenario in which RNAP might be directly recruited to these Dam site clusters, we found a lack of changes in local transcript levels; however it is still possible that RNAP could be recruited for transcription of sRNAs 111 but this transcription is not active (e.g. due to lack of promoter clearance) under our conditions, or that we were not able to detect these small transcripts. To characterize another example of a dense Dam site cluster that shows substantial changes in protein occupancy, we considered a cluster occurring near the flagellar chaperone gene flgN . Our investigation was motivated by the presence of a dense Dam site cluster at the promoter of flgN as well as Δdam -associated downregulation of flagellum synthesis genes, which led us to characterize the regulatory network governing flagellar synthesis and swimming motility in our methyltransferase mutants. We focused on the master regulator FlhDC since it is a regulator of flgN , and flhDC expression is decreased in Δdam strains. To explore a possible causal relationship between DNA methylation and protein occupancy leading to Δdam -associated changes in the regulatory network governing flagellum synthesis, we expanded our investigation to include regulators of flhDC . Based on analysis of expression changes in the regulons of each flhDC regulator, we identified LrhA as the most likely regulator of flhDC to be differentially regulating its targets in response to methyltransferase deletion, but characterization of the swimming motility for lrhA and dam deletion strains did not reveal a clear regulatory relationship between LrhA and dam deletion. We also note that there are multiple transposable elements that may be incorporated upstream of flhDC , and the presence of these transposable elements has been shown to impact flhDC expression and flagellum-based motility 86 , 112 ; we did not, however, observe any consistent pattern in our samples of changes in transposable elements around the flhDC promoter (data not shown). While the full nature of the relationship between DNA methylation and motility remains elusive, here we have demonstrated that loss of Dam methylation is associated with substantial loss of swimming-based motility. While our observations suggest that loss of lrhA leads to a decrease in swimming motility, Lehnen et al. , 2002 transduced an insertionally inactivated lrhA into the MG1655 background and found that functional loss of lrhA leads to an increase in swimming motility 100 . Our laboratory strain of MG1655 has an IS1 insertion in the coding region of dgcJ 47 , 48 which is a gene encoding for a diguanylate cyclase that has been associated with regulation of swimming motility 113 . It is thus possible that an epistatic interaction between lrhA and dgcJ explains the discrepancy in swimming motility phenotype resulting from functional loss of lrhA between our findings and those of Lehnen et al . 2002 100 , particularly given the importance of cyclic di-GMP for regulating flagellar motility 114 . In globally surveying the impact of loss of DNA methylation on gene expression and protein occupancy in E. coli K-12 MG1655, our results indicate that although loss of dam and/or dcm leads to statistically and biologically significant changes in gene expression associated with observable phenotypes – such as loss of swimming motility – these changes appear to result primarily from global physiological effects of dam or dcm loss rather than being due to transcriptional regulatory consequences of losing local DNA methylation signal. Our observations of protein occupancy changes at methylation sites are primarily at loci with exceptionally dense clustering of Dam sites where we observe an increase in RNAP occupancy, but we find this pattern to be of no consequence to local transcriptional output. We thus conclude that DNA methylation is not a biologically significant factor in local gene expression or global chromatin structure for E. coli K-12 MG1655 under our tested conditions. Future studies that aim to address the question of whether there is any regulatory interplay between NAP or transcription factor occupancy and DNA methylation in MG1655 would be well-served by either testing a wider range of growth conditions or employing site-specific perturbation of methylation status without altering DNA sequence (e.g. with a tethered methyltransferase or demethylase) 115 , 116 . SUPPLEMENTARY FIGURES Download figure Open in new tab Supplementary Figure 1: Genomic context of (A) selB , (B) prpE , and (C) recBD showing 51 bp rolling mean of RNAseq reads per tens of millions of reads (Transcripts (PTMR)) that were aligned to the positive (green occupancy trace) and negative (purple occupancy trace) strands. Brown boxes above markers on the “Dam Sites” tracks indicate “7 Dam Site Density” clusters of interest. Genes are differentially colored based on their membership to functional gene clusters. Download figure Open in new tab Supplementary Figure 2: Normalized read ends, as calculated by the count of read ends at each genomic position divided by the total number of million read ends within each sample, around the “7 Dam Site Density” at the (A) selB , (B) prpE , (C) recB , and (D) recD loci in each of 3 replicates for each genotype. Download figure Open in new tab Supplementary Figure 3: Gene set enrichment analysis for RNA-seq data across the indicated genotypes (relative to wild type). RNAseq data was analyzed using Rockhopper to produce q-values which assess statistical significance in expression change of each gene between strains. Directionality for expression change, where positive values indicate higher expression in the mutant relative to wild-type, was applied to the magnitudes of the log10(q-values). The values are divided into 21 evenly populated bins. iPAGE reports the representation of directional log10(q-values) across the genes annotated with each Gene Ontology (GO) term – thus, a redder bin indicates an over-representation of genes from the specified GO-term (row) at that expression change bracket (column). Download figure Open in new tab Supplementary Figure 4: (A) Genomic context of flgN showing 512 bp rolling mean of IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Brown boxes above markers on the “Dam Sites” tracks indicate “6 Dam Site Density” clusters of interest. Genes are differentially colored based on their membership to functional gene clusters. The dashed box designates the locus which is shown in panel B. (B) Genomic locus of flgN showing IPOD-HR (blue occupancy trace) or RNAP-ChIP (red occupancy trace) robust z-scores. Download figure Open in new tab Supplementary Figure 5: Heatmap representing the expression change in the regulon of regulators of flhC . We take the log ratio of mutant and wild-type expression values generated by Rockhopper for each gene in the regulon of the indicated flhC regulator. Directionality is then applied to determine whether expression changes in the regulon are consistent with the regulatory mode and expression of the regulator. E.g., genes that decrease in expression and are repressed by their regulator are “concerted” and thus contribute positively to the averaged log-fold change in expression. ACKNOWLEDGMENTS This work was supported by NIH R35 GM128637 (to L.F.). H.A. was additionally supported by the University of Michigan Cellular and Molecular Biology Training Program (T32GM007315). 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