www.sciencemag.org/cgi/content/full/science.1242059/DC1

Supplementary Materials for

High-Resolution Mapping of the Spatial Organization of a Bacterial Chromosome

Tung B. K. Le, Maxim V. Imakaev, Leonid A. Mirny,* Michael T. Laub*

*Corresponding author. E-mail: laub@mit.edu (M.T.L.); leonid@mit.edu (L.A.M.)

Published 24 October 2013 on Science Express DOI: 10.1126/science.1242059

This PDF file includes:

Materials and Methods Figs. S1 to S34 Tables S1 to S4 References (23–25)

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Materials and Methods Strains, media, and growth conditions E. coli and C. crescentus were grown in LB and PYE, respectively. When appropriate, media were supplemented with antibiotics at the following concentrations (liquid/solid media for C. crescentus; liquid/solid media for E. coli [μg/ml]): chloramphenicol (1/2; 20/30), kanamycin (5/25; 30/50), oxytetracycline (1/2; 12/12), spectinomycin (25/100; 50/50). Synchronizations were performed on mid-exponential phase cells using Percoll (GE Healthcare) and density gradient centrifugation. Briefly, 250 mL cultures of the wild-type CB15N or its derivatives were grown in PYE at 30 °C to an optical density at 600 nm of ~0.4 and were pelleted via centrifugation. Cells were re-suspended in 6 mL of 1x M2 salts (6.1 mM Na2HPO4, 3.9 mM KH2PO4, 9.3 mM NH4Cl, 0.5 mM MgSO4, 10 μM FeSO4, 0.5 mM CaCl2) via pipetting and 6 mL of ice-cold Percoll (Sigma) was added to the resuspension. The resulting mixture was transferred to a 15 mL Falcon tube that was subsequently centrifuged for 20 min (10000 g) at 4 °C. Swarmer cells formed a discrete band near the bottom of the tube. This band was removed via pipetting and the cells within this band were washed three times with 1 mL of ice-cold 1x M2 salts. After synchronization, swarmer cells were released into PYE for 0, 10, 30, 60 or 75 minutes before fixing with formadehyde for chromosome conformation capture and Hi-C analysis. For antibiotic treatments, swarmer cells were incubated with 25 µg/mL or 50 µg/mL novobiocin (final concentration) or 25 µg/mL rifampicin (final concentration) for 30 minutes before fixing with formadehyde. Strain construction Genes encoding SMC (CC0373), HU subunit 1 (CC1959) and subunit 2 (CC2331) were deleted using a two-step recombination process with selection for an antibiotic resistance cassette and then counterselection against sacB on the plasmid backbone. Briefly, approximately 600 base pairs (bp) flanking each side of each deleted gene was amplified by PCR. These fragments were then concatenated together with an intervening antibiotic resistance cassette (kanamycin, spectinomycin and tetracycline resistance cassettes for replacing CC0373, CC1959, and CC2331, respectively) using splice-overlap extension PCR (table S2). For deletion of the gene encoding SMC (CC0373), the tripartite cassette was cloned into pENTR-D-TOPO (Invitrogen) (table S3) in a reaction consisting of 2 µL of gel-purified PCR product of the tripartite cassette, 0.5 µL of D-TOPO reaction buffer, and 0.5 µL of pENTR-D-TOPO vector. The reaction was incubated for an hour before transformation into TOP10 E. coli cells (Invitrogen). The insert was verified by Sanger dye-termination sequencing (Genewiz) before being recombined into an integrative destination vector pNPTS-SPEC-DEST (table S3) via LR recombination reaction (Invitrogen) for gene deletion. For LR recombination reactions: 1 µL of purified pENTR harboring a deletion cassette was incubated with 1 µL of the destination vector, pNPTS-SPEC-DEST, 1 µL of LR reaction buffer, and 0.5 µL of LR Clonase enzyme in a total volume of 5 µL. The

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reaction was incubated for an hour or overnight before transformation into DH5α E. coli cells. For deletion of the gene encoding HU subunit 1 (CC1959), the tripartite cassette was instead cloned in between EcoRI and EcoRV sites of pNPTS138 (table S3) to generate the construct for gene deletion. The construct was verified by Sanger dye-termination sequencing (Genewiz) before transformation into C. crecentus cells. For deletion of the gene encoding HU subunit 2 (CC2331), the tripartite cassette was first cloned into pCR4-TOPO (Invitrogen) in a reaction consisting of 2 µL of gel-purified PCR product of the tripartite cassette, 0.5 µL of TA-TOPO reaction buffer, and 0.5 µL of pCR4-TOPO vector. The reaction was incubated for an hour before transformation into TOP10 E. coli cells (Invitrogen). The insert was verified by Sanger dye-termination sequencing (Genewiz) before being excised by EcoRI digestion. The insert was subsequently cloned into the EcoRI site of pNPTS138 to generate the construct for gene deletion. For deletion of the gene rsaA (CC1007) encoding the S-layer protein, a 600 bp region upstream and 600 bp region downstream of rsaA were amplified by PCR (table S2) and concatenated together using splice-overlap extension PCR without an intervening antibiotic resistance cassette. The PCR product was then cloned into pENTR-D-TOPO and verified for correct sequence by Sanger dye-termination sequencing (Genewiz) before being recombined into pNPTS-SPEC-DEST as described above to generate the construct for deletion. Deletions were verified using PCR with primers outside of the homologous flanking regions. The antibiotic-marked deletions were transduced into a clean CB15N background using the generalised phage ΦCR30. The ∆smc strain constructed here did not exhibit the temperature-sensitive and gross phenotypes described previously (23). We replaced the smc coding sequence with different antibiotic resistance cassettes (kanamycin or tetracycline resistance cassette) using the same primers as used previously. Our antibiotic-marked deletions were transduced into a clean CB15N background. To ensure that we did not isolate suppressor mutants, genomic DNA from the kanamycin-marked smc deletion mutant was purified and subjected to genome resequencing; we did not find any secondary mutations. To relocate rsaA along with its native promoter to the vanA locus, the coding sequence of rsaA and 210 bp upstream encompassing its core promoter were amplified by PCR (table S2) and cloned into SmaI site of pUC19 (Fermentas) (table S3). The resulting construct was verified by Sanger dye-termination sequencing (Genewiz) before being digested with NdeI and NheI to drop out the fragment containing the rsaA promoter and coding sequence. This insert was then cloned into NdeI-NheI-cut pVYFPC-6 (table S3) to produce pMT675::PrsaA-rsaA for subsequent integration at the vanA locus. The same insert was also cloned into NdeI-NheI-cut pXYFPC-1 (table S3) to give pMT533::PrsaA-rsaA for subsequent integration at the xylX locus. Integration at the vanA or xylX locus

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was confirmed using PCR with one primer outside of the vanA or xylX region and the another primer inside the rsaA coding sequence. For insertion of ΦC31 integrase attachment sites (attB or attP) (table S2) into defined locations in the C. crescentus genome, a two-step recombination process was used. Regions of approximately 600 bp upstream and downstream of the insertion point were amplified by PCR. The sequences for attB or attP were included as part of primers P2 and P3 (table S2) so that an attB or attP site was inserted between the two ~600 bp regions when they were concatenated together by splice-overlap extension PCR. These tripartite cassettes were then cloned into pENTR-D-TOPO (Invitrogen) and the inserts were verified by Sanger dye-termination sequencing (Genewiz) before being recombined into an integrative destination vector pNPTS-SPEC-DEST via LR recombination reaction (Invitrogen) for insertion into the genome. Correct insertion of attB and attP sites into the genome was confirmed by PCR. To construct a plasmid for induction of the ΦC31 integrase from the van locus, the coding sequence of ΦC31 integrase was amplified by PCR using pSET152 (gift from Lucy Fouston, R. Losick) as template (table S2-S3). The modified Caulobacter ssrA degradation tag (CCDAS4 tag: ADNDNFAEESENYADAS) was included in the reverse primer (table S2). The resulting PCR product was then cloned into pENTR-D-TOPO and sequence verified. The insert was excised using NdeI and NheI before cloning into NdeI-NheI-cut pVCFPC-5 (table S3) to give pMT571::ΦC31CCDAS4 for subsequent integration at the vanA locus by a single crossover. The ΦC31CCDAS4 at the van locus (tetracycline resistance marked) was subsequently moved to different strains by ΦCr30 transduction. Chromosome conformation capture with next generation sequencing (Hi-C) The general scheme for Hi-C analyses performed is summarized in fig. S1. In the first step, cells were fixed with formadehyde at a final concentration of 1% in PYE. Formadehyde crosslinks protein-DNA and DNA-DNA together, thereby capturing the structure of the chromosome at the time of fixation. The crosslinking reactions were allowed to proceed for 30 minutes at 25 °C before quenching with 2.5 M glycine at a final concentration of 0.125 M for 15 minutes. Fixed cells were then pelleted by centrifugation and subsequently washed twice with 1x M2 salts (6.1 mM Na2HPO4, 3.9 mM KH2PO4, 9.3 mM NH4Cl, 0.5 mM MgSO4, 10 μM FeSO4, 0.5 mM CaCl2) before resuspending in an appropriate volume of 1x TE buffer (10 mM Tris-HCl pH 8.0 and 1 mM EDTA) to a final concentration of 107 cells per µL. Resuspended cells were then divided into 25 µL aliquots and stored at -80 °C for no more than 2 weeks. Each Hi-C experiment was performed using two of the 25 µL aliquots. To each 25 µL aliquot, 2000 units of Ready-Lyse Lysozyme (Epicenter) was added and incubated for 15 minutes before addition of SDS to a final concentration of 0.25% for an additional 15 minutes to completely dissolve cell membranes and to release chromosomal DNA. The DNA was then digested with a restriction enzyme (BglII or NcoI, as noted in the text) in a total reaction of 50 µL (30 µL lysed cells, 5 µL restriction enzyme buffer 3, 5 µL 10% Triton-X, 2.5 µL restriction enzyme [50,000 units/ml BglII or NcoI], 7.5 µL autoclaved distilled water). The DNA was digested to completion by incubating at 37 °C for 3 hours. The reaction was cooled on ice before labelling overhangs with biotin-labelled dATP

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(Invitrogen) (50 µL digestion reaction, 0.9 µL dGTP, dTTP and dCTP at 2 mM each, 4.5 µL 0.4 mM biotin-14-dATP, 1.2 µL of Klenow Large fragment [5000 units/ml] and 2.8 µL water). Labelling was carried out at 25 °C for 45 minutes before addition of SDS to a final concentration of 0.5% to stop the reaction. Blunted DNA ends were ligated together in very dilute conditions so that DNA fragments that were spatially close in vivo and fixed together by formaldehyde treatment would be ligated while minimizing ligation between randomly colliding DNA fragments (fig. S1). The ligation reaction included 75 µL 10% Triton-X, 100 µL 10x T4 ligation buffer (NEB), 5 µL of 10 mg/ml BSA, 800 µL water, and 60 µL of the biotin-labeled DNA reaction. The entire reaction was cooled to 4 °C before adding 3 µL of T4 DNA ligase (2,000,000 units/mL) (NEB). The ligation reaction was incubated at 16 °C for 4 hours. The final concentration of DNA in the ligation reaction (~0.5 ng/µL) was lower than that used for a previous yeast 3C study (2.5 ng/µL) (7). Given that the genome of Caulobacter is ~3 times smaller than that of yeast, this lower concentration of DNA should give a comparable low background of random ligation products. After ligation, EDTA was added to a final concentration of 10 mM to stop the reaction and 2.5 µL of 20 mg/mL Proteinase K (NEB) was added. The reaction was then incubated at 65 °C overnight to reverse crosslinks. DNA was subsequently extracted twice by phenol/chloroform/isoamyl alcohol, precipitated by isopropanol, and then resuspended in 60 µL of water. Non-ligated, but biotin-labelled fragments were eliminated using the 3’-5’ exonuclease activity of T4 polymerase: 60 µL of purified DNA was incubated with 10 µL NEB restriction enzyme buffer 2, 1 µL 10 mg/mL BSA, 5 µL 2 mM dGTP, and 0.5 µL T4 DNA polymerase (3000 units/mL) (NEB) at 12 °C for 2 hours. DNA was then extracted using phenol/chloroform/isoamyl alcohol and precipitated using isopropanol. Purified DNA was electrophoresed through a 1% agarose gel to check for integrity and efficiency of ligation with a relatively tight band and molecular weight > 10 kb (for BglII Hi-C) indicating a good ligation. PCR was also performed to confirm amplification of a particular Hi-C junction. This amplified Hi-C junction is resistant to cutting by BglII but sensitive to digestion by ClaI (fig. S1). Purified DNA was then resolubilized in 50 µL of water for shearing. DNA was sheared to between 200 and 600 bp using the Bioruptor (Diagnogen), alternating 30 seconds on and 30 seconds off for 30 minutes. After shearing, DNA was electrophoresed through a 2% agarose gel and DNA between 300 and 500 bp was excised and purified using a Qiagen gel extraction column, with final elution in 50 µL of water. DNA was then end-repaired in a total volume of 100 µL (50 µL sheared Hi-C DNA, 10 µL 10x T4 DNA ligase buffer, 2.5 µL 10 mM dNTPs, 4 µL T4 DNA polymerase [3000 units/mL], 4 µL T4 Polynucleotide kinase [10000 units/ml], 0.75 µL Klenow large fragment [5000 units/ml] and 28.75 µL water). The reaction was incubated at 25 °C for 30 minutes. DNA was then purified using a MiElute Reaction CleanUp column (Qiagen) and eluted in 30 µL of water. Repaired DNA then had 3' A overhangs created by incubating 30 µL DNA, 4 µL 10x NEB restriction buffer 2, 4 µL 2 mM dATP, and 3 µL Klenow 3’-5’ exo- (5000 units/ml) (NEB). The reaction was allowed to proceed for 45 minutes at 37 °C. DNA was purified again using a MinElute Reaction CleanUp kit and eluted in 15 µL of water. This DNA was then ligated with standard Y-shaped Illumina

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adaptors in the following reaction: 15 µL DNA, 5 µL 20 mM Illumina Y-shaped adaptor, 2.5 µL 10x T4 ligase buffer, 1.5 µL T4 ligase enzyme (400,000 units/mL) (NEB) in a total volume of 25 µL. This reaction was incubated at 25 °C for 30 minutes. In the next step, biotin-labelled junctions were purified from non-labelled junctions using Streptavidin Dyna beads (Invitrogen). Beads (25 µL) were washed twice in NTB buffer (5 mM Tris-HCl pH 8.0, 0.5 mM EDTA and 1 M NaCl) by repeating a cycle of resuspension and pull-down by magnetic attraction. Washed beads were then introduced into the ligation mixture above and incubated with gentle agitation for 30 minutes to capture biotin-labelled DNA junctions. Beads were then pulled down magnetically and unwanted supernatant discarded. Beads with biotin-labelled DNA fragments bound were then washed twice in NTB buffer, twice in distilled water, and then resuspended in 10 µL of water. DNA bound to beads was then enriched by PCR using primers compatible with Illumina paired-end sequencing (Illumina) in a total volume of 50 µL. The PCR reaction included: 1 µL 10 mM PE1 Illumina primer, 1 µL 10 mM barcoded PE2 Illumina primer, 1 µL 10mM dNTP, 10 µL 5x Phusion PCR buffer (NEB), 1.5 µL DMSO, 35 µL water, 0.5 µL Phusion DNA polymerase (NEB) and 1.2 µL resuspended beads. PCR was carried out for 25 cycles: 10 s at 98 °C, 20 s at 60 °C, and 25 s at 72 °C. PCR products were then purified by gel extraction before sequencing on an Illumina HiSeq 2000 or Miseq (MIT BioMicroCenter). Generation of Hi-C contact maps Paired-end sequencing reads were mapped independently to the genome of Caulobacter crescentus CB15N (NCBI Reference Sequence: NC_011916.1) using Bowtie 2.1.0 and an algorithm which iteratively increases truncation length to maximize yield of valid Hi-C interactions (24). Only read pairs with both reads that were uniquely aligned to the genome were considered for subsequent steps. The Caulobacter CB15N genome was then divided into restriction fragments (700 BglII fragments and 2025 NcoI fragments). Each read of a read pair was sorted into its corresponding restriction fragment. Read pairs were classified as valid Hi-C products, non-ligation, or self-ligation products (24); only valid Hi-C products were considered below (table S4). Note that these Hi-C experiments substantially expand the set of locus-locus interactions queried relative to a prior BglII 5C study, which examined 169x169 interactions (4), to 700x700 for the BglII Hi-C data and 2025x2025 for the NcoI Hi-C data (fig. S3C). The significantly higher number of queried interactions yield a major increase in resolution. To create interaction matrices, the Caulobacter genome was first divided into 405 10-kb bins. We then assigned valid Hi-C products to the bins as follows. Recall (see fig. S1) that each valid Hi-C product involves the ligation of two restriction fragments. If both restriction fragments are contained entirely within bins (e.g. suppose fragment 1 is contained within bin A and fragment 2 is contained within bin B), we incremented the count of interactions between bins A and B by one. If one of the restriction fragments spanned two bins (e.g. suppose fragment 1 is contained within bin A but fragment 2 spans bins B and C) then we incremented the count of interactions between bin A and B by an amount proportional to the fraction of fragment 2 that overlaps bin B (and similarly for the count of interactions between A and C). Thus, each read contributes a count of

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one to the interaction map, but it can be split between bins. This means of assigning reads to bins was necessary to analyze data at a high 10-kb resolution. Interactions of a bin with itself, and with neighboring bins were discarded. For the presentation of the matrices, three discarded diagonals were later given the maximal cut-off value of 0.035. Raw Hi-C contact maps can be biased due to the uneven distribution of restriction enzyme sites (fig. S3) and, to a lesser extent, differences in GC content and the mappability of individual reads. We therefore normalized raw contact maps using an iterative normalization procedure, implemented using the hiclib library for Python (https://bitbucket.org/mirnylab/hiclib). Essentially, we converted the number of interactions, or read counts, into Hi-C scores by applying the following equation and iteratively repeating it for the resulting contact map after each cycle: mij =mij * (total reads) / (total reads in bin i * total reads in bin j). The iterative procedure was repeated until the maximum relative error of the total number of Hi-C scores in a bin was less than 10-5. Resulting matrices were normalized so that Hi-C scores for each row and column sum to 1. Subsequent analysis and visualization was done using Python and R scripts. Reproducibility of Hi-C data Independent Hi-C experiments were highly reproducible, as judged by a comparison of biological replicates (r=0.98, p<10-15) (fig. S4). Our Hi-C data also correlated well with a previous, lower-resolution chromosome conformation capture study (4) and with fluorescent repressor-operator system (FROS) results (6) for the same cell cycle stage (figs. S5-S6). The interaction matrices for the BglII and NcoI Hi-C experiments were highly correlated and showed the same general patterns (figs. S4, S7) indicating that the chromosome organization inferred by this approach is an intrinsic property of the cell and not a consequence or reflection of restriction enzyme cutting frequency. Calculating contact probability from Hi-C data The plots of contact probability as a function of genomic distance were constructed as described previously using hiclib in Python, which is a part of the Hi-C analysis pipeline ICE (24). We calculated P(s) using fragment-level-filtered, iteratively-corrected Hi-C data. All steps of the analysis were performed using the hiclib library for Python. We calculated P(s) only from intra-chromosomal arm interactions. To avoid contributions from the inter-chromosomal-arm interactions, we calculated P(s) using the intra-arm regions of a Hi-C interaction map: 200 kb to 1750 kb, and 2300 kb to 3800 kb. To eliminate biases at a fragment level, we first performed iterative correction of Hi-C data that were binned by fragment. We then divided all genomic separations into logarithmically spaced bins, starting at 5 kb and increasing by a factor of 1.12, i.e. 5, 5*1.12, 5*1.122, … , Lmax. For each bin, we calculated a sum of all Hi-C reads, and a weighted number of fragment pairs separated by that distance. The weight for each fragment pair was calculated as Bi*Bj , where Bi, Bj are per-fragment biases for the two fragments, obtained from iterative correction. In the calculation of P(s), we excluded all reads where the two sides map to different strands, as at short distances they may

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represent self-circularization products or un-ligated Hi-C molecules. We additionally excluded fragment pairs from directly neighboring fragments. To obtain P(s), we divided the sum of all reads in each bin by the weighted sum of possible fragment pairs. We then normalized P(s) such that the integral of P(s) over the range of all distances is 1. To estimate P(s) of wild-type Caulobacter swarmer cells, we used four different independent datasets obtained from wild type cells: three with BglII restriction enzyme and one with NcoI. The shaded region displayed in fig. S13, S16, S17, S25, S27 and S29 is bounded by minimum and maximum values of P(s) from four experiments. Correlation analyses Pearson correlation coefficients between Hi-C experiments were carried out as followed: two-dimensional matrices representing Hi-C contact maps were decomposed to one-dimensional vectors row-by-row. R was then used to compute the Pearson correlation coefficient between vectors. Computing correlation between Hi-C and prior 5C data for the swarmer Caulobacter cell was done as above, except that we reduced the Hi-C matrix (700x700) to the corresponding 5C (169x169) interactions before decomposing matrices into vectors. For computing correlation between fluorescent repressor-operator systems (FROS) data and our Hi-C data, we used the chromosomal loci positions measured as a function of cell length (6). We then reduced the Hi-C matrix to a vector of interactions between a restriction site closest to the origin of replication and restriction sites closest to where the FROS arrays were inserted. The correlation between vectors was calculated as above. Identification of chromosomal interaction domains (CIDs) We divided the Caulobacter genome into 10-kb bins and the contact map was represented as a matrix of interactions between bins (405x405 bin-to-bin interactions for NcoI Hi-C). Qualitatively, CIDs appeared as squares along the lower left to upper right diagonal of the Hi-C contact map or as triangles if the contact map was rotated 45 degrees clockwise. For each CID, the bin at the left-most edge interacted more frequently with bins to its right than to its left. Conversely, the bin at the right-most edge interacted preferentially with bins to its left. Bins within a CID interacted approximately equally with bins to the left and right. Thus, bins or loci near the edge of CIDs exhibited a preferred direction of interaction whereas those within a CID did not. To quantify the degree of directional preference for a given bin, we extracted the vector of interactions between that bin and bins at regular 10-kb intervals, either to the left or right, up to 100 kb. We then compared log2(Hi-C scores) of the two vectors by a paired t-test to assess whether the strength of interactions were significantly stronger in one direction compared to the other (fig. S9A). The differences in log2(Hi-C scores) between the two vectors were approximately normally distributed. The more significant the difference, the higher the absolute t-value. Negative t-value indicated a bin is interacting more with bins to the left than to the right and vice versa. A p-value of 0.05 was used as a threshold to assess statistical significance. Directional preferences for each bin along the chromosome were then represented as a bar plot with positive and negative t-values

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shown as red and green bars, respectively. For bins with t-values below -1.81 or above 1.81 (t-value at which p = 0.05, degree of freedom = 10), the bars were dwarfed down to -1.81 or 1.81 (dashed black lines), respectively, for clarity of presentation. At CID boundaries the directional preference of bins changed most dramatically (fig. S9A). Bins near the middle of a CID also changed their preferred direction but did so more gradually than those at the boundaries. CID boundaries were called mainly based on NcoI Hi-C data due to higher restriction frequency of NcoI compared to BglII (fig. S3C, table S1). CID boundaries were defined as the 20 or 30-kb region encompassing a transition from green to red on directional preference plots (fig. S9B). Although the NcoI Hi-C contact map represented 10-kb bins, 20 to 30-kb windows were chosen due to uncertainty in calling the exact position of a CID boundary from data with binned restriction sites. To calculate the distribution of restriction sites, we allocated individual restriction sites to 10-kb bins along the chromosome and then counted the number of NcoI sites at CID boundaries and at 10-kb intervals extending out from those boundaries (fig. S10A). Sites depleted of reads could result from poor mappability or from occlusion of a restriction site during protein-DNA crosslinking. We defined restriction sites having less than one percent of the total mapped reads as depleted sites. The number of depleted sites were counted for each 10-kb bin. Data were then plotted for CID boundaries and for bins at regular distance from those boundaries (fig. S10B). To determine the number of highly-expressed genes per CID boundary (fig. S10C), we first obtained absolute value gene expression data (GEO: GPL11304) and called a gene as highly-expressed if its absolute expression on Affymetrix arrays was > 4.0 (60 out of 405 10-kb bin have HEGs) for each time point during the C. crescentus cell cycle. Highly-expressed genes were then allocated into 10 kb bins along the chromosome, but if there were multiple highly-expressed genes per 10-kb bin, we only counted one to avoid bias resulting from multiple highly-expressed genes being co-operonic. Data were then plotted for CID boundaries and for bins at regular distance from those boundaries (fig. S10C). To further assess whether individual bins harbored a directional bias in their Hi-C interactions, we calculated a directionality ratio for each 10-kb bin along the Caulobacter chromosome (fig. S33). The directionality ratio was defined as a ratio of upstream Hi-C scores to the total (upstream + downstream) Hi-C scores for a given bin. Upstream and downstream scores were calculated as a sum of interactions of a given bin with bins up to 200 kb away. Directionality ratios were calculated using binned, iteratively corrected Hi-C contact maps or similarly processed simulated Hi-C contact maps (fig. S33). Calculating the Hi-C interactions enrichment for time-course Hi-C The sum of all raw Hi-C interactions was computed for each 10-kb bin along the Caulobacter chromosome. These sums were then divided by the mean of interactions of the ter region (bin 195 to bin 205). This normalization was repeated for Hi-C data collected from time points 0, 10, 30, 45, 60 and 75 min post synchronization. To

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normalize for uneven distribution of BglII restriction sites on Caulobacter genome, we divided by the value of each bin at the 0 min time point (Fig. 32C). Lowess curves were fitted to these data using R with smoother span of 0.4. Polymer model of the Caulobacter chromosome Model overview: The model of the Caulobacter chromosome contained 9,274 monomeric units connected into a semi-flexible polymer. Plectonemes that result from DNA supercoiling were modeled as long sidechains emanating from a central backbone with exponentially distributed lengths. Each spherical monomer had a diameter of 13.7 nm and contained 434 bp of DNA, or two 217 bp fragments for a plectonemic monomer. Plectonemes had an average length of 35 monomers, or 15 kb, and were spaced, on average, one-monomer apart along the main chain (fig. S12). This represents ~5-fold compaction compared to the DNA contour length, likely resulting from DNA supercoiling and the effects of DNA-associated proteins (fig. S12). This model significantly expanded on previous lower-resolution models that used either 167 or 800 monomers (4, 25) and that did not explicitly model plectonemes. The polymer in our model was confined to the volume of a cell (modelled as a cylinder 450 nm in diameter and 2500 nm in length) with a single origin tethered at one pole (Fig. 1C, S12). Plectoneme Free Regions (PFRs) were introduced at the positions of 20 highly-expressed genes, leading to emergence of CIDs (fig. S16-S17). The polymer model was equilibrated using Brownian Dynamics simulations and an ensemble of conformations was obtained (fig. S14). Parameters of the model were determined by systematically sweeping values of all five parameters simultaneously (see below), aiming to fit the scaling of the contact probability observed by Hi-C (figs. S13, S17) and to reproduce interactions between chromosomal arms and the presence of CIDs. Our approach avoids the ambiguity of using Hi-C data as distance constraints. Model details: Each spherical monomer in our simulations represents a unit of plectoneme length, with diameter equal to the diameter of a plectoneme (fig. S12). We characterized the structure of a plectoneme by a dimensionless parameter λ, representing the DNA compaction ratio in a plectoneme. This parameter defines both the monomer size, and the amount of DNA contained in each monomer. The DNA compaction ratio was chosen to equal one for minimal plectoneme compaction, i.e. with DNA packed at 4.5 bp/nm for each of the two DNA helices forming a plectoneme (we chose it to be 1.5 times the DNA packing density, or 3 bp/nm, due to the twist necessary to form a minimal plectoneme). In this parameterization, plectoneme diameter was calculated from the DNA packing density. Assuming that 20% of a monomer's volume is occupied by DNA and each monomer is a cylinder of diameter D and height D, the amount of DNA in each monomer was calculated as 2*(4.5λ)*D (bp), where the factor 2 accounts for the two DNA molecules forming a plectoneme (fig. S12). This gives a monomer diameter, D = 7.75 * λ1/2 (nm). We modeled the bacterial chromosome as an array of plectonemes attached to a central backbone every 1-3 (2 on average) monomers (fig. S12). Organization of plectonemes

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was characterized by two parameters: average plectoneme length, L, and average spacing (gap) between neighboring plectonemes, Lg (fig. S13). Lengths of individual plectonemes were chosen from an exponential distribution with a mean value L; plectonemes less than 3 monomers were not considered in our simulations. Origins of individual plectonemes were connected by spacers of lengths, chosen uniformly from [0, 2*(Lg - 1)] interval, leading to an average distance between plectonemes of Lg. We introduced Plectoneme Free Regions (PFRs) at the positions of each of the 20 genes with the highest levels of expression (fig. S17). The length of each PFR was 7 monomeric units, or about 100 nm. We note that at two loci, two or three highly expressed genes were located in very close proximity, thus creating longer PFRs. Regions before and after PFRs were never part of one plectoneme, thus preventing intra-plectoneme contacts, i.e. contacts due to plectoneme slithering between these regions. PFRs also spatially separated regions around them. Parameter searches for the polymer model We performed an extensive search in the five-dimensional parameter space to find parameters of a model that provide the best fit to P(s) and that reproduce other features of the Hi-C data including alignment of the chromosomal arms and CIDs. We explored all combinations of the following five parameters of the model: plectoneme lengths L=15, 25, 40, 55 monomers; plectoneme spacing Lg = 2, 3, 5, 9 monomers; compaction ratios λ = 2.5, 3, 3.7, 4.5; stiffness parameters S = 2, 3.5, 5, 8. For each set of parameters, we collected an equilibrium ensemble of conformations as described above. For each realization, we calculated P(s) for 7 values of the cutoff radius: Rc=2, 3, 4, 5, 6, 7, 8. The 1792 P(s) curves obtained were compared with the P(s) curve for the wild-type Hi-C data. To measure the agreement of experimental and computed P(s) curves on log-log axes we

normalized both of them: , where and , and computed a

norm ));()((var modelexp xyxyDx

−= in the 10 kb to 1 Mb range. The top 10 sets of

parameter values were: λ L Lg S Rc D 4.5 55 5 3.5 6 0.015 4.5 40 9 3.5 8 0.016 3.7 40 2 5 6 0.0187 4.5 25 2 8 5 0.0189 4.5 55 5 3.5 7 0.019 3.7 40 5 5 6 0.019 4.5 40 3 5 5 0.019 2.5 40 5 5 8 0.02 3.7 40 2 8 5 0.021 4.5 40 5 5 5 0.021 The top-scoring parameter sets clearly fell into three groups:

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λ L Lg S Rc 3.7-4.5 25-40 2-3 5-8 5-6 4.5 40-55 5-9 3.5-5 5-8 2.5 40 5 5 8 We evaluated each set of parameter values for its ability to reproduce the alignment of chromosomal arms and domains observed experimentally. The second and third parameter sets involve large spacing between neighboring plectonemes and were unable to reproduce CIDs upon introduction of PFRs. We then focused on the first parameter set and performed a fine parameter adjustment in the vicinity of the first set of parameters, varying one parameter at a time. An optimized set of parameters fit within this parameter range for the first group, and included L = 35, Lgap

= 2, Rcont = 5, S = 6 and λ = 3.5. Forces and Brownian Dynamics simulations Adjacent monomers were connected by harmonic bonds with a potential U = 25*(r - 1)2 (the energy is given in units of kT). The stiffness of plectonemes and of inter-plectoneme spacers was modeled by a three point interaction term, with the potential U = S*(1 - cos(α)), where α is an angle between neighboring bonds, and S is a parameter controlling stiffness. Neighboring monomers interacted via a shifted Lennard-Jones (LJ) repulsive potential. At high densities in a confined volume, the details of the inter-monomer interactions are negligible due to screening, and we therefore used this more computationally efficient potential. The shifted LJ potential allows for a short-range repulsion by truncating the LJ potential at its minimum and shifting the minimum to zero: U = 4 * (1/r12 - 1/r6) + 1, for r<21/6; U=0 for r > 21/6. The shifted LJ potential is one of the most computationally efficient repulsive potentials due to a very short cutoff radius. To allow chain passing, which represents activity of type II topoisomerase, we softened the shifted LJ potential by truncating the interaction energy at Ecutoff = 1 kT. To achieve this, at energies more than 0.5 Ecutoff, the LJ potential was softened via: Usoftened = 0.5 * Ecutoff * (1 + tanh(2*U/Ecutoff - 1)). To avoid numerical instabilities in the calculation of U at r ~ 0, the interaction radius r was truncated at r = 0.3 via: rtruncated = (r10 + 0.310)1/10, which introduced a negligible shift in the final softened potential. Our polymer model was equilibrated using Brownian Dynamics and equilibrium ensemble of 25,000 conformations was collected from 100 independent run, with location, length, and initial conformation of each plectoneme randomly generated for each run (fig. S14). We performed polymer simulations with OpenMM - a high-performance GPU-assisted molecular dynamics API (http://simtk.org/home/openmm). The time step was chosen to be 0.1 * TLJ, where Lennard-Jones time was estimated as TLJ = (m/k)1/2, m is a monomer mass, and a spring constant k is the maximum absolute value of the second derivative of the truncated LJ potential. Andersen thermostat was used to keep the kinetic energy of the system from diverging.

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Each run of Brownian dynamics simulations consisted of 2.5*107 timesteps. We collected 250 conformations from the second half of each run. As judged by the saturation of the mean square displacement (MSD) of the monomer positions (fig. S14) the system reaches an equilibrium during the first 20% of run. Initialization, equilibration, and starting conformations To show that our simulations reach thermodynamic equilibrium, we tested whether the starting conformation is relevant to the statistics of the obtained conformational ensemble. To initialize our simulations, we created an initial conformation which fit in the bounding box, with non-overlapping monomers, in which the two chromosomal arms were aligned along the axis of the confining cylinder. We used the following algorithm to incrementally create our starting conformation on a cubic lattice. We started with a cube of the size of the length of the chain, and created a ring of width one, connecting the centers of two opposite faces of the cube. We then chose one bond at random, and tried to extend the polymer at this location by two monomers, by making a bond into a kink. To do this, we considered another bond, obtained by shifting this bond by one in a random direction perpendicular to the bond (choosing one out of 4 possible directions). If both locations of the shifted bond were free, the polymer was extended to incorporate this bond. For example, if a chosen bond was going in +z direction: … -> (0,0,0) -> (0,0,1) -> … , and we attempted to grow it in the -y direction (chosen randomly out of +x, -x, +y, -y), we would check positions (0,-1,0) and (0,-1,1). If both of them were free, the polymer sequence would be changed to … -> (0,0,0) -> (0,-1,0) -> (0,-1,1) -> (0,0,1) -> … . If at least one of them was occupied, selection of the random bond was repeated. The process was repeated till the polymer grew to the desired length. This process created a continuous circular chain. In this conformation, origins of plectonemes and the backbone monomers that are connected by bonds, were relatively far apart (usually, no more than 10 monomers). To bring them together, we used a default OpenMM algorithm to perform local energy minimization of the system before starting the Brownian Dynamics.. Since we used a soft-core Lennard Jones force for inter-monomer repulsion, which allows monomers to pass through each other, we did not worry about any topological violations (e.g. plectonemes forming knots) which arose during this brute-force initiation process. Since we only sampled conformations after the system had reached an equilibrium, and the system was not constrained topologically, the details of the initial conformation were not relevant, and were chosen out of convenience. To test whether the results of our modeling process were indeed independent of the starting conformation, we performed the same simulation with the mirrored starting conformation. In this setup, the ori, which is tethered at one side of the cell, is initially located at the other side. During the energy minimization step, the ori was forced to migrate back to the other side bringing both ori and ter to one side of the cell. During the simulation, the chromosome quickly oriented itself, with ori remaining where it is and tethered, and ter migrating to the other side (Fig. S15). After 20% of the run-time, the trajectory was indistinguishable from a normal run. These simulations demonstrated that even with an inverted initial conformation, the entire chromosome can reorient itself to the correct equilibrated state. This also shows that the choice of the starting conformation did not affect the results of our simulations.

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To test whether conformations from 100 runs were sufficient to obtain a reproducible interaction map, we compared maps obtained for the first and the last 50 runs. We found that maps generated for these ensembles were nearly indistinguishable and were correlated with Pearson correlation r=0.974 (fig. S14). Note that we collect 250 conformations for each run, producing an equilibrium ensemble of 25,000 conformations for each model. Calculating P(s) for polymer models A plectoneme consists of two DNA double-helices, running in opposite directions. Each monomer of a plectoneme thus covers two different genomic regions separated by at most a plectoneme length. To properly account for contacts formed by these regions, we first separated each monomer of a plectoneme into two monomers corresponding to different genomic positions, but occupying the same position in space. For consistency, we counted each monomer from an inter-plectoneme spacer twice to reflect the same DNA content per monomer. This procedure was modified to reflect partial unwinding of plectonemes in novobiocin-treated cells and Δhup1Δhup2 cells (see below). To calculate P(s) and to generate simulated contact maps we assumed that the Hi-C protocol detects contacts between monomers located within a contact radius, Rcont. Varying this parameter influenced P(s) significantly, as it changed the relative contributions of intra-plectoneme and inter-plectoneme contacts. P(s) was calculated from polymer conformations obtained after the system reached thermodynamic equilibrium. Contacts were binned logarithmically as a function of distance s; bin locations started with s = 4, and multiplicatively increased in distance with a step size of approximately 1.12 up to the maximum value of N-100, where N is total chain length. Bin positions were rounded down to the nearest integer and repeated bins locations were discarded, yielding a sequence of (4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 18, 21, 24, 28, …, N/1.12, N-100). For each bin, the number of observed contacts within this distance range was aggregated over all conformations, and divided by the total number of monomer pairs belonging to this distance range. Finally, contact probability was normalized such that the integral of P(s) over a considered range of distances equals one. Modeling wild-type chromosomes For the wild-type model, each monomer has a diameter of 13.7 nm and contained 434 bp of DNA for the backbone, or two 217 bp fragments for a plectoneme monomer. This represents ~10-fold compaction compared to the DNA contour length of 434 bp. Plectonemes had an average length of 35 monomers, or 15 kb, and were spaced, on average, one monomer apart along the main chain (fig. S12). The total number of monomeric units per chromosome was 9,274. We observed some degree of twisting of the chromosome arms in the simulated chromosomes (fig. S34), as also suggested by 5C (4), likely as a result of confinement within the cell rather than any specific pattern of interactions.

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Modeling gyrase inhibition and deletion of hup1 and hup2 To model the effect of inhibiting DNA gyrase, we assumed that the reduction of DNA supercoiling would disrupt and partially unwind plectonemes and increase the inter-plectoneme spacing. To account for an increased inter-plectoneme distance, we performed additional simulations with an inter-plectoneme distance of 10 monomers (5-fold increase relative to the wild-type model). To account for plectoneme loosening due to the loss of supercoiling, we modified the procedure which converts positions of monomers to positions of genomic regions forming a plectoneme. After the conformations were obtained from Brownian dynamics simulations, we displaced each strand of the plectoneme away from the plectoneme core by a random offset. Computationally, this was achieved by displacing each genomic region on each strand of a plectoneme independently by 2.2 monomers (~30 nm) on average in the x, y, and z directions, while preserving the chain continuity. First, we created an Nx3 matrix of random normally-distributed offsets for each coordinate of each monomer, with a standard deviation of 7. Then, we convoluted the matrix along the long axis with a 1D gaussian kernel with a standard deviation of 3, yielding a matrix of random displacements with a standard deviation of 2.2. Convolution was necessary to ensure continuity of a resulting polymer chain, as seen in fig. S27A. For the model of ∆hup1∆hup2 cells, we introduced random displacements of the two strands of a plectoneme, with a standard deviation of 3.2. Next-generation sequencing of total genomic DNA We extracted total genomic DNA from the same samples used to conduct the Hi-C time course. Cells were lysed with lysozyme and Triton-X. Protein-DNA crosslinks were reversed by incubation at 65 °C overnight together with Proteinase K (NEB). DNA was then isolated by phenol/chloroform/isoamyl alcohol extraction using the same procedure as described above for Hi-C. Purified DNA was sheared and prepared into libraries for Illumina single-end sequencing using the same procedure as described for paired-ends sequencing of Hi-C above. DNA was sequenced on an Illumina HiSeq 2000 (MIT BioMicroCenter). Following replication progression by sequencing The progression of DNA replication was followed by total genomic DNA sequencing. Because the population was synchronized, regions closer to the replication origin were duplicated first and should theoretically produce twice as many reads as unreplicated regions. After sequencing total genomic DNA from C. crescentus cells collected at time points 0, 10, 30, 45, 60 and 75 min post synchronization, we mapped reads to the C. crescentus NA1000 reference genome using Bowtie (bowtie-bio.sourceforge.net), quantified the number of reads for every 1-kb segment along the chromosome and normalized to the total number of successfully-mapped reads in each FASTQ file, i.e. we computed the number of reads per kb per million mapped reads (RPKPM) along the Caulobacter genome. A Lowess curve was then fitted to these data using R with smoother span of 0.1, generating a RPKPM curve (fig. S30). The inflection point of the

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RPKPM curve indicated the location of replication forks. We estimated that DNA replication in C. crescentus cells grown in PYE medium progressed at a rate of ~33 kb/min. To determine the ratio of replicated to unreplicated regions, i.e. the DNA content enrichment (fig. S32D), we divided the RPKM values of each point along the chromosome to that of the ter region (bin 195 to bin 205). The ratios ranged between 1 and 2.04. Recombination assays A serine recombinase from Streptomyces phage ΦC31 binds to its specific attB or attP attachment sites. This ΦC31-attB complex interacts with a ΦC31-attP complex to form a synapse which mediates DNA digestion and re-ligation. This leads to deletion of the intervening DNA between the attB and attP sites if these sites are oriented in the same direction (fig. S11A). The recombination between attB and attP into attL and attR is irreversible. ΦC31-attB and ΦC31-attP complexes are brought together by random collision, hence the rate of recombination can serve as a proxy for the frequency at which two sites interact with one another. The attP site was inserted into bin 155 (genomic position: 1,543,548) on the C. crescentus chromosome. attB sites were each subsequently inserted into bin 147 (genomic position: 1,459,528) or into bin 163 (genomic position: 1,625,779) on the chromosome of the strain already containing attP inserted at bin 155 (attB sites are in the same direction as attP). The distance between attB and attP in both cases is ~80 kb (fig. S11B-C). A gene encoding a ClpXP-degradable ΦC31 integrase was integrated onto the chromosome at the vanA locus. A degradable version of ΦC31 (ΦC31CCDAS4) was used instead of a wild type ΦC31 to ensure no leaky expression from the van promoter when not induced. Strains TLS141 (Bin155::attP Bin163::attB) and TLS142 (Bin155::attP Bin147::attB) were grown to OD600 = 0.2 before inducing the ΦC31 integrase for 30 min by addition of vanillate to a final concentration of 50 µM. Cells were collected before and 30 min after induction by centrifugation and frozen in liquid nitrogen before DNA extraction by Blood and Tissue DNA kit (Qiagen). For the quantitative analysis of recombination, fluorescence real-time PCR was perform using double-stranded DNA dye SYBR Green (Biorad) on a Lightcycler system (Roche). Each qPCR reaction contained 1 µL of genomic DNA, 0.5 µL of a primers mix at a concentration of 10 µM, and 5 µL of iQ SYBR Green supermix (BioRad) in a 10 μL reaction. Samples were amplified by incubating at 95 °C for 5 min followed by 45 cycles of 95 °C for 10 sec, 55 °C for 10s and 72 °C for 20 sec followed by fluorescence data collection. Cycle threshold (Ct) values were calculated using Roche Lightcycler software (Roche). qPCR primer pairs are listed in table S2. The amount of recombination was normalized to the control ruvA values (table S2). The DNA template used to generate qPCR standard

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curves was a mixture of wild-type C. crescentus genomic DNA, a DNA fragment encompassing bin 147-bin 155 deletion junction (amplified using primers: 147-155Standard_F, 147-155Standard_R and genomic DNA from TLS142 30min after induction as template) and a DNA fragment encompassing bin 163-bin 155 deletion junction (amplified using primers: 163-155Standard_F, 163-155Standard_R and genomic DNA from TLS141 30min after induction as template) with known molar ratio. Expression analyses Briefly, wild type C. crescentus, TLS87, and TLS91 were harvested when reaching OD600 = 0.2 and their RNA was extracted using RNeasy Mini Kit (Qiagen). 1 μg of total RNA was reverse transcribed into cDNA using Superscript-II (Invitrogen), RNA template degraded was with RNAse H (NEB), and nucleotides were removed using the QIAquick PCR purification kit (Qiagen). Quantitative PCR was then used to quantify the relative expression of rsaA (primer listed in table S2) relative to a control ruvA. A sample where no reverse transcriptase was added was used as control for genomic DNA contamination. Determination of subcellular positions of chromosomal loci by fluorescent repressor-operator system (FROS) C. crescentus strains with (lacO)n arrays inserted at pilA, pleC, podJ and (tetO)n array inserted near the origin of replication were previously described (6). A tetracycline-resistant-cassette-marked Δsmc allele was introduced into these strains by generalized transduction. These strains were grown to OD600=0.4 in the presence of appropriate antibiotics and glucose before the cells were collected by centrifugation and washed of residual glucose twice with PYE. These cells were resuspended in PYE plus antibiotics and xylose and then grown for another 90 min to induce the expression of YFP-TetR and CFP-LacI. Cells were then synchronized and the swarmer cell population isolated. These swarmer cells were then either laid on an agarose pad for microscopy or were incubated with 20 µg/ml novobiocin or 25 µg/ml rifampicin for 30 min before observation by microscope. Phase contrast (150 ms exposure) and fluorescence images (3000 ms exposure) were collected. MicrobeTracker (http://microtracker.org) was used to detect the cell outline and polarity was set manually using YFP fluorescent images as guidance. Spot positions were recorded using SpotFinderM from the MicrobeTracker suite.

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Fig. S1. Schematic protocol of Hi-C. Chromatin conformation was preserved by crosslinking protein-DNA and DNA-DNA interactions. Chromatin was digested by restriction enzyme (BglII or NcoI). Restriction sites are shown as dashed lines. Resulting sticky ends were filled in with biotin-dATP (violet dots). Ligations were performed in dilute conditions to favor linkage of DNA ends captured by the same crosslinking event. After ligation, BglII/NcoI sites were destroyed and ClaI/NsiI created. DNA was then sheared and biotinylated junctions were purified by streptavidin beads (grey dots) for subsequent paired-end sequencing.

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Fig. S2. A schematic Caulobacter cell cycle showing the changes in morphology as well as DNA replication and segregation. The origin of replication (magenta dot) and the terminus (blue dot) reside at opposite poles of the swarmer cells. After replication initiation, one origin remains near the stalked pole while the other moves rapidly to the opposite pole (6, 23). Caulobacter is well-suited to Hi-C analysis as cells are easily synchronized, enabling the isolation of large population of cells with nearly identical chromosome content.

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Fig. S3. Hi-C contact maps before and after normalization, and restriction enzyme site bias. The Caulobacter genome was divided into 10-kb bins. For a given bin, the number of Hi-C interactions or scores made by that bin and number of restriction sites residing within that bin was determined. (A) Comparison of digestion frequency and Hi-C interactions for BglII Hi-C before (left panel) and after (right panel) normalization. (B) Comparison of digestion frequency and Hi-C interactions for NcoI Hi-C before (left panel) and after (right panel) normalization. Red dashed lines represent least squares best fits. (C) Digestion frequency of NcoI (total of 2025 sites in the Caulobacter genome) and BglII (700 sites).

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Fig. S4. Pearson correlation between Hi-C replicates. Normalized Hi-C matrices decompose into vectors to calculating Pearson’s correlation coefficients. (A) Comparison of biological replicates performed on the same day. (B) Comparison of biological replicates performed on different days. (C) Comparison of biological replicates performed using BglII and NcoI. Red dashed lines represent least squares best fits.

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Fig. S5. Comparison of Hi-C with previous 5C data for Caulobacter swarmer cells. Hi-C interactions were reduced to the corresponding interactions queried by 5C (4). (A) Contact maps of Caulobacter swarmer cells, as measured by 5C (left panel) and by BglII Hi-C (right panel). (B) Pearson correlation of the 5C and Hi-C data sets in panel A.

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Fig. S6. Comparison of Hi-C with previous FROS data. FROS foci distances were represented as fractions of cell length (6) on the vertical axis. Hi-C interactions between the restriction site closest to the origin of replication and restriction sites closest to where FROS arrays were inserted are represented on the horizontal axis. (A) Comparison of BglII Hi-C data to FROS data. (B) Comparison of NcoI Hi-C data to FROS data. Red dashed lines represent least squares best fits.

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Fig. S7. Normalized NcoI (A) and BglII (B) Hi-C contact maps of Caulobacter swarmer cells with 10-kb bins. The Hi-C contact maps were rotated 45 degrees so that the most prominent diagonal from the square matrix is horizontal. The resulting triangles were truncated at the top, producing a trapezoid shape. Two directional preference plots are displayed below each rotated contact map showing either the full directional preference values or truncated values. Leftward and rightward preferences are shown as green and red bars, respectively. For the truncated directional preference plot, bins having directional preference exceeding the threshold value (dashed black lines) and hence statistically significant were dwarfed down to the threshold value. The title of each contact map indicates the Pearson's correlation coefficient (r) when compared to the wild-type NcoI dataset and the corresponding p-value (p).

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Fig. S8. Normalized NcoI Hi-C contact map showing the presence of highly-expressed genes at boundaries between chromosomal interaction domains (CIDs). Hi-C contact maps for the right arm (A) and the left arm (B) of the chromosome were rotated 45° clockwise. Directional preference plots are displayed below. Left- and right-ward preferences are shown as green and red bars, respectively. CIDs, representing regions of densely interacting loci, manifest as triangles (outlined in yellow) in the contact map. CIDs were numbered from 1 to 23. Highly-expressed genes at CID boundaries are listed. Genes encoding hypothetical proteins are listed by their Genbank ID.

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Fig. S9. Directional preference analysis. (A) An example of an abrupt switch in the preferred direction of interactions at the transition between two CIDs. Following the strength of Hi-C interactions made by a given bin with those on the right (solid line) and those on the left (dashed line), we observed a sudden switch from favoring leftward interactions (bin 17) to favoring rightward interactions (bin 18). A paired t-test method based on these vectors of interaction frequencies was used to call CID boundaries (see supplementary methods). NcoI Hi-C data with 10-kb binning was used for CID assignment due to its higher resolution. (B) Example of CID boundaries and nested CID in a section of the NcoI Hi-C contact map. Hi-C contact map together with directional preference plot for genomic position 2.5 to 2.9 Mb shows CIDs 14 and 15 (marked with yellow solid line). CIDs 14 and 15 are nested within a larger domain (marked with yellow dashed line). Rectangles on the directional preference plot show putative boundaries between CIDs. Solid lines below the directional preference plot show the extent of each CID. Leftward and rightward preferences are shown as green and red bars, respectively. For bins having directional preference exceeding the threshold (dashed black lines), the bars were dwarfed down to the statistically significant threshold value.

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Fig. S10. Distribution of restriction sites, reads-depleted sites and highly-expressed genes (HEGs) at CID boundaries and at distance from boundaries. (A) Number of NcoI sites versus distance from CID boundary. (B) Number of reads-depleted sites versus distance from CID boundary. Reads-depleted sites could have resulted from poor mappability or occlusion of restriction site by Protein-DNA crosslinking. (C) Number of HEGs versus distance from CID boundary. If there were multiple HEGs per 10-kb bin, we only counted one to avoid bias resulting from multiple highly-expressed genes being co-operonic. Fisher’s exact test was used to test the null hypothesis that there is no difference in number of restriction sites, reads-depleted sites or HEGs between CID boundary bins and non-boundary bins. P-value (p) is included in each plot.

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Fig. S11. Frequency of DNA-DNA interactions, as assessed by ΦC31 integrase-mediated recombination. (A) Schematic indicating that ΦC31-attB and ΦC31-attP complexes can form a synapse that mediates the excision of the intervening DNA region (orange). The probability of interaction between two loci is directly proportional to the recombination rate between attB and attP sites at those loci. Recombination events can be detected by qPCR employing primers on opposite sides of the att sites (black and magenta arrows). (B) Hi-C contact map for a segment of the chromosome (1.2 Mb-1.8 Mb) rotated 45° clockwise with the directional preference plot beneath. The site attB was inserted within either bin 147 (black arrow) at genomic position 1,459,528 or bin 163 (black arrow) at genomic position 1,625,779. The site attP was inserted within bin 155 (grey arrow) at genomic position 1,543,548. Bins 147 and 155 are locate within the same CID whereas 155 and 163 are in neighboring CIDs. (C) Genomic distance, (D) Hi-C interaction scores, and (E) recombination frequencies between bins in the same or different CIDs.

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Fig. S12. Schematic polymer model of the chromosome. (A) Each plectoneme is composed of two DNA duplexes running in opposite directions. (B) Each monomer in the model comprises 217 bp from each duplex, representing ~5-fold compaction compared to the uncompacted DNA contour length. (C) Plectonemes are packed into fibers, and are separated by plectoneme-free regions (PFR). An average plectoneme length is 35 monomers. (D) Fibers of plectonemes and plectoneme-free regions are packed inside a cylinder the same approximate volume as a Caulobacter cell. One actual equilibrium realization of chromosome model is shown here. The chromosome backbone is shown in black and is polarly anchored (magenta dot) with a handful of plectonemes (various colors) emerging from the backbone with the rest of the plectonemes in light grey. (E) A schematic polymer model of the chromosome indicating polarly anchored origin (magenta dot), chromosome backbone (black), and plectonemes (colored and grey loops).

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Fig. S13. Parameter search for the polymer model of the chromosome. (A) Five parameters describing the polymer models: plectoneme length, gap length between adjacent plectonemes, diameter of plectonemes, flexibility of plectonemes, and contact radius. (B) Contact probability P(s) plots obtained by varying one or several parameters in the best-fitting plot of the parameter search. (C) Contact probability P(s) plots, obtained by varying all parameters. Each parameter combination is shown with a thin semi-transparent line to indicate the density of lines in different regions. The total number of combinations is 1728. Colored lines are example set of parameters which show good (green), mediocre (yellow) and poor (red) agreement with the Hi-C data.

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Fig. S14. Equilibration of the polymer model. (A) Plot of the mean square displacement of monomers as a function of time. The mean square displacement saturates at 20 minutes. (B) Contact probability, P(s), plotted as a function of genomic distance and at different time points. P(s) does not change significantly between t=100 and t=500, indicating thermodynamic equilibrium. (C) A split-test, showing simulated heatmap computed from the first 50 starting conformations (left), and the second 50 conformations (right). The two heatmaps are visually very similar. They correlate (element-by-element) with Spearman r= 0.973, and Pearson r=0.992. For observed over expected (i.e. each diagonal is divided by its mean), Spearman r=0.940, showing that correlation coefficient is not dominated by the decay of contact probability, and reflects true similarity of the maps.

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Fig. S15. Polymer model results are independent of starting condition. A normal simulation of a polymer model (left panel) was compared to that of the inverted simulation (right panel) where a starting conformation was mirrored before performing the simulation. In the inverted simulation, ori (magenta dot) is originally located on the right, but is tethered to the left boundary of the cell. During energy minimization, this constraint has to be satisfied. Therefore, ori migrates to the left, brings with it a short stretch of a chromosome. Over the course of simulation the chromosome realigns itself, and after 20% of the trajectory the chromosome conformation is indistinguishable from the normal simulation. We only included the last 50% of each trajectory for our analysis. This simulation indicates that the last 50% of the trajectory will be independent of the starting conformation even in an extreme inverted starting conformation. The chromosome is colored continuously from blue (ori) to green (ter), to red (ori).

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Fig. S16. Effect of Plectoneme Free Regions (PFRs) on the formation of chromosomal interaction domains (CIDs) in polymer simulations. (A) Introducing a single PFR creates a prominent CID boundary in the simulated Hi-C data. (B) CIDs of different sizes and strength emerge in simulations due to clustering of actively expressed genes. A prominent CID boundary on the right (surrounded by other CIDs), was formed by two highly expressed genes and is stronger than the CID boundary on the left with one highly expressed gene. Variation in the strength of CID boundaries, hence the emergence of nested domains may result from variation in PFR length (as seen in simulations). (C) Plectoneme fiber backbone for the wild type model. Plectoneme fiber backbone is shown in black, Plectoneme Free Regions (PFRs) are shown in gray. Only first 5 monomers of each plectoneme are shown to highlight location of plectoneme attachment to the backbone. (D) P(s) of models with CIDs compared to experimental data.

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Fig. S17. Partitioning of the Caulobacter chromosome into chromosomal interaction domains (CIDs). (A) Normalized NcoI Hi-C contact map for Caulobacter swarmer cells displaying relative contact frequencies for pairs of 10 kb bins across the genome. Axes indicate the genome position of each bin. (B) Normalized Hi-C map for the equilibrated polymer model with plectoneme-free regions near highly-expressed genes. (C) Plot of contact probability as a function of genomic distance between restriction sites. Four independent repetitions of Hi-C for wild type (shaded grey area) are compared to simulated Hi-C data (blue) from panel B. Simulated Hi-C data are based on a model in which parameters were adjusted to provide a strong fit with the experimental Hi-C data (see Supplementary Methods).

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Fig. S18. Normalized BglII Hi-C contact maps for (A) untreated (B) rifampicin-treated and (C) novobiocin-treated Caulobacter swarmer cells. Concentration of drugs used and the duration of incubation are indicated at the top of each panel. Directional preference plots are displayed below the rotated contact map. Leftward and rightward preferences are shown as green and red bars, respectively. The title of each plot indicates Pearson’s correlation coefficient (r) when compared to the wild-type and the corresponding p-value (p).

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Fig. S19. Comparison of experimental and simulated Hi-C contact maps. Experimentally obtained and simulated Hi-C interaction maps are shown side by side. An insert on the shaded area indicates contact probability P(s) for a given heatmap (blue line) which is compared to the wild-type experimentally obtained P(s) (black).

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Fig. S20. Simulated Hi-C contact maps for (A) wild type untreated, (B) rifampicin treated, (C) novobiocin treated and (D) ∆hup1∆hup2 Caulobacter swarmer cells. Directional preference plots are displayed below the rotated contact map. Leftward and rightward preferences are shown as green and red bars, respectively. The title of each plot indicates Pearson’s correlation coefficient (r) when compared to the simulated wild-type dataset and its corresponding p-value (p).

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Fig. S21. Effect of inhibiting transcription on CID boundaries. Normalized BglII Hi-C contact map for (A) untreated and (B) rifampicin-treated Caulobacter swarmer cells, displaying interaction scores for all pairs of 10-kb bins across the genome. A region from 1-2 Mb, rotated 45° clockwise and enlarged, is shown below each contact map. Simulated Hi-C data from a model of the same region is shown for comparison. Schematics indicate the inclusion or omission of plectoneme-free regions at regions of high transcription in the model. A section of the chromosome model highlighting two domains (red and green) separated by a plectoneme free region (purple) is shown in panel A.

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Fig. S22. Relative expression level of rsaA, encoding the S-layer protein, relative to a ruvA control. For wild-type, ∆rsaA, and a strain in which rsaA and its native promoter were introduced at the van locus (no induction by vanillate) the mRNA level of rsaA was measured using qRT-PCR and expressed relative to a control, ruvA. Samples where no reverse transcriptase was added were included to control for contamination from genomic DNA. Microarray data showing expression of rsaA relative to ruvA is included. Reverse transcription qPCR shows that rsaA remains highly expressed after relocation from its native locus to the van locus.

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Fig. S23. Effect of relocating a highly-expressed gene on CID boundary formation. Normalized Hi-C contact maps of (A) wild type and ΔrsaA + xyl::PrsaA-rsaA. Only region of the chromosome containing the xyl locus is shown. (B) Normalized Hi-C contact maps of wild type, ΔrsaA, ΔrsaA + xyl::PrsaA-rsaA. Only region of the chromosome containing the native rsaA locus is shown.

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Fig. S24. Effect of novobiocin or rifampicin treatment on survival. Caulobacter were treated with 50 µg/ml novobiocin or 25 µg/ml rifampicin for 30 min before drugs were washed away with plain media and cells plated out on non-antibiotic plates to determine their colony forming ability.

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Fig. S25. Plots of chromosomal contact probability as a function of genomic distance between restriction sites. (A) Chromosomal contact probability for untreated samples of wild-type swarmer Caulobacter versus novobiocin-treated or rifampicin-treated samples. (B) Chromosomal contact probability for wild-type swarmer Caulobacter cells versus Δhup1Δhup2 or Δsmc cells.

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Fig. S26. The positions of four chromosomal loci in Caulobacter swarmer cells under different drug treatments or genetic backgrounds. Boxplots show the positions of the origin of replication (ori), pilA, pleC, and podJ, as determined by a fluorescent repressor-operator system. The positions are expressed as a fraction of cell length. The inset shows schematic positions of these loci on the circular C. crescentus genome. The table shows positions of the above loci as mean fraction of cell length ± standard deviation.

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Fig. S27. Polymer model of the chromosome upon gyrase inhibition by novobiocin. (A) Plectoneme fiber backbone for the gyrase inhibition model is shown in black, Plectoneme Free Regions (PFRs) are shown in gray. Most of the plectoneme backbone is made of plectoneme spacers (black). Only the first 5 monomers of each plectoneme are shown to highlight locations of plectoneme attachment to the backbone. (B) Illustration of an increased plectoneme spacing in the gyrase inhibition model, and plectoneme loosening in comparision to the wild type model. Note that plectoneme loosening does not change the packing density. (C) Simulated Hi-C map for gyrase inhibition polymer model (right) compared to experimentally-obtained Hi-C map (left). Notice the significantly weaker domains (CIDs) in both interaction matrices. (D) Comparison of contact probability between wild-type and gyrase inhibition models. Gyrase inhibition models were modifications of a wild-type model with increased inter-plectoneme spacing and plectoneme loosening.

45

Fig. S28. Normalized BglII Hi-C contact maps for (A) wild-type (B) Δhup1Δhup2 and (C) Δsmc swarmer cells. Directional preference plots are displayed below the rotated contact maps. Leftward and rightward preferences are shown as green and red bars, respectively. The title of each plot indicates Pearson’s correlation coefficient (r) when compared to the wild type and the corresponding p-value (p).

46

Fig. S29. A polymer model for Δhup1Δhup2 Caulobacter chromosome. (A) Illustration of an increased plectoneme loosening in the polymer model for Δhup1Δhup2 Caulobacter chromosome in comparison to the wild type model. (B) Contact probability P(s) of the simulated model of Δhup1Δhup2 Caulobacter in comparison to the experimentally measured P(s). (C) Simulated Hi-C map for Δhup1Δhup2 (right) compared to experimentally-obtained Hi-C interaction map (left).

47

Fig. S30. Hi-C contact maps of Caulobacter cells at various cell cycle stages. Hi-C was performed for cells at 0, 10, 30, 45, 60, and 75 minutes post-synchronization. Hi-C contact maps here show the natural logarithm of interaction scores to enhance the color of lower scoring interactions. The progression of DNA replication was followed by counting the number of sequencing reads per kb per million mapped reads (rpkpm) along the Caulobacter genome. Cartoon strips show the portions of the chromosome that have already been replicated (red) or remain to be replicated (black). Cartoons on the side of each plot show Caulobacter at cell cycle stages corresponding to the time points from 0 to 75 minutes.

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Fig. S31. Continued on next page

49

Fig. S31. Rotated BglII Hi-C contact maps of Caulobacter at various developmental stages. Hi-C was performed for cells at 0, 10, 30, 45, 60 and 75 minutes post-synchronization. Directional preference plots are displayed below the rotated contact maps. Leftward and rightward preferences are shown as green and red bars, respectively. The title of each plot indicates Pearson’s correlation coefficient (r) compared to the 0-minute Hi-C data and the corresponding p-value (p).

50

Fig. S32. Possible states of the newly replicated DNA. Recently replicated sections of the chromosome could re-establish their chromosomal interactions concurrently with, or shortly after, DNA replication and segregation (A) or could be delayed until replication and segregation also finish (B). In the latter case, newly replicated DNA would remained unstructured until replication and segregation is completed. Black lines indicate the Caulobacter chromosome together with CIDs (orange and green ovals). The origin and terminus of replication are indicated as red and blue dots, respectively. (C) Plot of Hi-C interactions (relative to t=0) for each locus across the genome as a function of cell cycle progression. (D) Plot of total DNA content (relative to t=0) for each locus across the genome as a function of cell cycle progression, indicating replication fork progression from the origin (genome positions ~0 and 4Mb) towards the terminus (genome position ~2 Mb). These Hi-C interaction enrichment curves follow the curves for enrichment of replicated DNA. We conclude that CIDs must be re-established concurrently with, or shortly after, DNA replication rather than waiting until replication and segregation are completed.

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Fig. S33. Directionality ratios for experimental and simulated datasets. Directionality ratios were calculated using the 10-kb binned, iteratively corrected Hi-C contact map, or similarly processed simulated Hi-C contact map. The directionality ratio is the ratio of upstream Hi-C scores to total (upstream + downstream) scores for a given bin. Upstream and downstream scores were calculated as a sum of interactions of a given bin with bins up to 200-kb away. The title of each plot indicates Pearson’s correlation coefficient compared to the wild type dataset (replicate 1) and the corresponding p-value. Vertical lines indicate positions of the top 20 highly expressed genes, used to construct PFRs in simulated wild type and simulated novobiocin treatment datasets.

52

Fig. S34. Clustering of the backbones of plectoneme fibers. Conformations of 200 backbones of plectoneme fibers were obtained from 100 runs of the wild-type polymer model (midpoint and end of each run). Since different backbones were of slightly different lengths, each backbone was linearly interpolated to 1000 monomers. A 200x200 distance matrix was obtained by finding, for each pair of conformations, an optimal rotation around the center of the cell, which minimizes a mean squared distance between the two conformations. Distance matrices were then clustered using the default scipy k-means clustering algorithm with k=6, yielding 6 clusters of 24 to 40 conformations each. Backbones within each cluster were rotated to minimize their distance to the first conformation of each cluster. Each of the 6 images shows backbones from each cluster, with each backbone colored from blue to red. Four out of six clusters (1,2,4,5) show mild helicity, making one quarter to one half of a turn; two other clusters show no helicity.

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Table S1. List of chromosomal interaction domains (CIDs) CID Approximate Genome

Position (kb) 1 3800-170 2 180-360 3 370-520 4 530-670 5 680-770 6 780-860 7 880-1000 8 1010-1160 9 1170-1430 10 1440-1580 11 1590-1930 12 1940-2170 13 2180-2570 14 2580-2750 15 2760-2870 16 2880-3010 17 3020-3140 18 3150-3270 19 3280-3380 20 3390-3490 21 3500-3640 22 3650-3680 23 3690-3790

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Table S2. Primers Primers for deletion of smc (CC0373)

Del_smc_P1 CACCcgacaccaaggcgctggacgg Del_smc_P2 gattttgagacacaacgtggctttgccgttgatcctgtaggtggag

Del_smc_P3 gagtttttctaatcagaattggtt tgcaacatgctggacgagatg

Del_smc_P4 gtgctgttcacgctgaagggcgc

Primers for amplifying the kanamycin resistance cassette

kanR_del_smc_F aaagccacgttgtgtctcaaaatc

kanR_del_smc_R aaccaattctgattagaaaaactc

Primers for deletion of hup1 (CC1959)

Del_hup1_P1 cagGATATCgcaagggtcgtatgcagatcaccg

Del_hup1_P2 gcgggtcaagtgtggtcatgtttctctcccagcc

Del_hup1_P3 gccgcttcgcaacagctaggcggcatatcgatcg

Del_hup1_P4 agcGAATTCacctgcccgcgtccgtcatg

Primers for amplifying the spectinomycin resistance cassette

specR_del_hup1_F catgaccacacttgacccgcagttgcaaacc

specR_del_hup1_R cctagctgttgcgaagcggcgtcggcttgaac

Primers for deletion of hup2 (CC2331)

Del_hup2_P1 cttggcatcacctttcagca

Del_hup2_P2 accgagaaagggtgacgattgaggttttttgcgattgaagcg

Del_hup2_P3 caacgagccgatcgctgagccttcagagaggggccg

Del_hup2_P4 gcgagctcgggcgaggcct

Primers for amplifying the tetracycline resistance cassette

tetR_del_hup2_F cggtatcgataagcttgatatcgaattc

tetR_del_hup2_R ctgcaggaattcaagaagttcctattc

55

Primers for deletion of rsaA (CC1007)

Del_rsaA_delP1 CACCacggtagaactcggccagctcctg

Del_rsaA_delP2 ggcgagcgtcaggacgtacagcaaattcctatatagcgttccttg

Del_rsaA_delP3 gtcctgacgctcgcctaagcgaac

Del_rsaA_delP4 ctctcggacctggtccatgtcgcgg

Primers for construction of pMT675::PrsaA-rsaA or pMT533:: PrsaA-rsaA

prsaA_cds_rsaA_NdeI_F CACCATATGttataaagcctcgcgcgttgaccg

prsaA_cds_rsaA_NheI_R GCTAGCttaggcgagcgtcaggacttcggtg

ΦC31 attachment sites

attB cgggtgccagggcgtgcccttgggctccccgggcgcgtac

attP gtagtgccccaactggggtaacctttgagttctctcagttgggggcgtag

Primers for insertion of attP into bin 155 (NA1000 genomic position: 1,543,548)

bin155_attP_P1 CACCtgacctgtccaacccctatgtcgtg

bin155_attP_P2 gagagaactcaaaggttaccccagttggggcactactcagacgccgtgggccaggac

bin155_attP_P3 gtaacctttgagttctctcagttgggggcgtagtctttcagagggccagtcataaag

bin155_attP_P4 tgacaacgcgaacatgcgcaaggttg

Primers for insertion of attB into bin 147 (NA1000 genomic position: 1,459,528)

bin147_attB_P1 CACCgtcgcggccctgaccgccgaaggg

bin147_attB_P2 ggggagcccaagggcacgccctggcacccgtcagatgatcgtcatgccgccgtc

bin147_attB_P3 cgtgcccttgggctccccgggcgcgtacccgtcgacaggcgcctttcagcgg

bin147_attB_P4 ccccgcgccgacatagggcttaacg

56

Primers for insertion of attB into bin 163 (NA1000 genomic position: 1,625,779)

bin163_attB_P1 CACCgtcatgttgatcatcggttcggcg

bin163_attB_P2 cccggggagcccaagggcacgccctggcacccgcctcgactggtggagcccgggag

bin163_attB_P3 ccagggcgtgcccttgggctccccgggcgcgtacttacttaacctcggtgacgaac

bin163_attB_P4 aactgaactggcgctacgccgcc

Primers for construction of pMT571::ΦC31-CCDAS4

NdeI_phiC31_F CACCCATatgacacaaggggttgtgaccgg

CCDAS4_phiC31_int_R1st gattcttccgcgaagttatcgttatccgccgccgctacgtcttccgtgccgtc

CCDAS4_phiC31_int_R2nd GCTAGCttacgacgcatccgcgtagttttcagattcttccgcgaagttatcgt

qPCR primers

qPCR_ruvA_F cgagtgaggaagccgtagag

qPCR_ruvA_R gaccctgttgcacatcgag

qPCR_147-155_rec_F cagatcatcgtcgtcgacgg

qPCR_147-155_rec_R gcgttcgcctttatgactgg

qPCR_155-163_rec_F tggccttgctgcttctc

qPCR_155-163_rec_R ctggtgaatctgcagaaggt

147-155Standard_F gtcgcggccctgaccgccgaaggg

147-155Standard_R tgacaacgcgaacatgcgcaaggttg

163-155Standard_F tgacctgtccaacccctatgtcgtg

163-155Standard_R aactgaactggcgctacgccgcc

qPCR_rsaA_F gtacgcgactcaaaccca

qPCR_rsaA_R gaagaactggtaggtctggatg

Upper case text in primers highlights segments used for cloning (Gateway cloning determinants or restriction sites).

57

Table S3. Plasmids and strains Strains/Plasmids Description Source Plasmids pUC19 General purpose cloning vector Fermentas pENTR-D-TOPO ENTRY vector for gateway cloning

(kanR) Invitrogen

pKOC3 integrating vector with FRT flanked tetracycline resistance cassette (chlR)

Lab collection

pNPTS138 Integrating vector with sacB (kanR) Lab collection pNPTS-SPEC-DEST Integrating vector with sacB, destination

vector for gateway cloning (specR) Lab collection

pVYFPC-6 (i.e. pMT675) Integrating vector to the vanA locus (chlR)

M. Thanbichler

pXYFPC-1 (i.e. pMT533) Integrating vector to the xylX locus (specR)

M. Thanbichler

pVYFPC-5 (i.e. pMT571) Integrating vector to the vanA locus (tetR)

M. Thanbichler

pSET152 Streptomyces integrative vector, source of ΦC31 integrase

Gift from Lucy Foulston-Losick lab

pNTPTS138::KO_hup1 Construct for deletion of hup1 (specR) This study pNPTS::KO_hup1 Construct for deletion of hup2 (tetR) This study pNPTS::KO_smc Construct for deletion of smc (kanR) This study pNPTS::KO_rsaA Construct for in-frame deletion of rsaA This study pMT675::PrsaA-rsaA For single-cross-over integration of

rsaA and its native promoter to the vanA locus

This study

pNPTS::Bin155_attP For insertion of attP site into C. crescentus genome

This study

pNPTS::Bin147_attP For insertion of attB site into C. crescentus genome

This study

pNPTS::Bin163_attP For insertion of attB site into C. crescentus genome

This study

pMT571::ΦC31-CCDAS4 For inducing a ClpXP-degradable ΦC31 integrase from the van locus

This study

Strains C. crescentus CB15N Synchronizable derivative of wild-type

CB15 Lab collection

TLS53 CB15N Δsmc (kanR) This study TLS57 CB15N Δhup1 (specR) Δhup2 (tetR) This study TLS87 CB15N ΔrsaA This study TLS91 CB15N ΔrsaA vanA::PrsaA-rsaA (chlR) This study TLS141 Bin 155::attP Bin163::attB

van::ΦC31_CCDAS4 This study

TLS142 Bin 155::attP Bin147::attB This study

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van::ΦC31_CCDAS4 TLS147 CB15N ΔrsaA xylX::PrsaA-rsaA (specR) This study TLS148 CB15N ΔrsaA vanA::empty pMT675

(chlR) This study

E. coli DH5α General cloning strain Invitrogen E. coli TOP10 General cloning strain for pENTR-D-

TOPO constructs Invitrogen

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Table S4. Hi-C datasets

Hi-C datasets Restriction enzyme

Number of qualified reads

C. crescentus swarmer cells replicate 1 BglII 7153164 C. crescentus swarmer cells replicate 2 BglII 8237605 C. crescentus swarmer cells replicate 3 NcoI 12850294

Δhup1 Δhup2 C. crescentus swarmer cells BglII 3044569 Δsmc C. crescentus swarmer cells BglII 6631194

C. crescentus swarmer cells + 20 µg/ml novobiocin for 30 min BglII 5847618

C. crescentus swarmer cells + 25 µg/ml rifampicin for 30 min BglII 6297385

C. crescentus ΔrsaA + vanA::PrsaA-rsaA BglII 7708164 C. crescentus ΔrsaA + xylX::PrsaA-rsaA BglII 3477633

C. crescentus ΔrsaA + vanA::empty pMT675 BglII 3376799 C. crescentus 0 min post-synchronization BglII 6652977

C. crescentus 10 min post-synchronization BglII 5350803 C. crescentus 30 min post-synchronization BglII 3535323 C. crescentus 45 min post-synchronization BglII 3110936 C. crescentus 60 min post-synchronization BglII 1857223 C. crescentus 75 min post-synchronization BglII 3110936

Genomic DNA sequencing datasets Number of reads

C. crescentus 0 min post-synchronization 6,223,562 C. crescentus 10 min post-synchronization 15,587,367 C. crescentus 30 min post-synchronization 19,131,301 C. crescentus 45 min post-synchronization 25,388,482 C. crescentus 60 min post-synchronization 21,371,908 C. crescentus 75 min post-synchronization 24,098,546

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