Role of special crosslinks in structure formation of bacterial DNA polymer.
Abstract
Using data from contact maps of the DNApolymer of E. Coli (at kilobase pair resolution) as an input to our model, we introduce crosslinks between monomers in a beadspring model of a ring polymer at very specific points along the chain. By suitable Monte Carlo Simulations, we show that the presence of these crosslinks leads to a particular architecture and organization of the chain at large (micron) length scales of the DNA. We also investigate the structure of a ring polymer with an equal number of crosslinks at random positions along the chain. We find that though the polymer does get organized at the large length scales, the nature of the organization is quite different from the organization observed with crosslinks at specific biologically determined positions. We used the contact map of E. Coli bacteria which has around million base pairs in a single circular chromosome. In our coarsegrained flexible ring polymer model, we used monomer beads and observed that around crosslinks are enough to induce the largescale organization of the molecule accounting for statistical fluctuations caused by thermal energy. The length of a DNA chain of an even simple bacterial cell such as E. Coli is much longer than typical proteins, hence we avoided methods used to tackle protein folding problems. We define new suitable quantities to identify large scale structure of a polymer chain with a few crosslinks.
pacs:
87.15.ak,82.35.Pq,82.35.LrI Introduction
The organization of chromatin at mesoscopic length () scales has been a topic of intense research in this decade Dekker et al. (2013); LiebermanAiden et al. (2009); Joyeux (2015); Dekker and Mirny (2016); Bickmore and van Steensel (2013); Dixon et al. (2012); Maxim V. Imakaev (2015); Chaudhuri and Mulder (2012); Jonathan D. Halverson and Grosberg (2014); Grosberg (2016); Andreas Hofmann (2015); Ramakrishnan et al. (2015); Fudenberg et al. (2016a); Rosa and Everaers (2008); Pombo and Nicodemi (2014); Chiariello et al. (2016a); Iyer and Arya (2012), specially after the work of Liebermann Aiden et. al. LiebermanAiden et al. (2009) where the authors mapped out the spatial proximity maps of DNA segments of human genome (each segment of length 1 Mega Base Pairs) inside nucleus using a technique called HiC: highthroughput sequencing. The experimental studies provide a contact map of DNA segments LiebermanAiden et al. (2009); Le et al. (2013); Bickmore and van Steensel (2013); Dixon et al. (2012); Cagliero et al. (2013); PhillipsCremins et al. (2013). A contact map is a color map that shows which DNA segments, numbered are spatially close to other DNA segments j () with high/low frequency. The question is whether with this information, can one predict the spatial organization of the DNA chain which is expected to help in identifying the biological consequences.
The physics approach is of course to consider chromatin as a polymer chain, and the chromatin within the nucleus as a collapsed polymer coil Dekker and Mirny (2016); Maxim V. Imakaev (2015); Imakaev et al. (2015); Mirny (2011); Vologodskii (2009); Pombo and Nicodemi (2014); Chiariello et al. (2016a); Sachs et al. (1995); Marenduzzo et al. (2006); Bohn and Heermann (2010); Barbieri et al. (2012); Fudenberg et al. (2016a, b); Goloborodko et al. (2016); Rubinstein and Colby (2003). The resolution of HiC experiments has increased to 1 kiloBP (kilobase pair) which is still above the persistence length of naked dsDNA (approximately, nm with 150 BPs) as reported for bacterial cells Le et al. (2013). However, inside cells around 150 BPs of DNA wrap around histonelike proteins (for bacterial cells) to form higher order structure Rob Phillips (2008), and the persistence length of a DNA polymer chain is still debated in vivo Maeshima et al. (2010).
Anyways, DNA organization at large length scales can be viewed as the organization of a flexible polymer. The large length scales of dsDNA in question are nmmicrons, such that a DNAsegment consisting of a kilo to mega BPs can be considered as a coarsegrained monomer in a beadspring model of the polymer chain. Our work aims to elucidate the structure of chromatin at this length scale, or equivalently that of a flexible polymer with added constraints. Generically we show that adding spatial constraints by crosslinking a minimal number of specific monomers along the length of chain can lead to the organization of an entire long ringpolymer into a specific structure, but there are structural fluctuations due to .
Much of the research is focussed on structure and organization of the chromatin during interphase stage Maxim V. Imakaev (2015); Imakaev et al. (2015); Mirny (2011); Jonathan D. Halverson and Grosberg (2014); Rosa and Everaers (2008); MateosLangerak et al. (2009): the stage of the cell cycle when the cell does not divide into daughter cells. It is also known that the individual chromosome is not arranged as a random walk polymer. From the data of contact map, we observe that some DNA segments have a much higher spatial association with other chain segments and show up as the presence of socalled Topologically Associated Domains (TADs) LiebermanAiden et al. (2009); Le et al. (2013); Dixon et al. (2012) in the contact map. An actual chromatin is not just a long polymer chain within a nucleus or within a cell (for bacteria) but there are also various proteins and enzymes doing various functions of the cell. For example, there are DNAbinding proteins which attach two different and specific segments of DNA chain together and the enzyme topoisomerase which allows chains to cross each other by suitably cutting and rejoining chains Vologodskii (2009). Polymer physics principles are suitably adapted to incorporate effects of proteins and enzymes related activity when investigating the origin or reasons of formation of TADs Maxim V. Imakaev (2015); Imakaev et al. (2015); Mirny (2011); Jonathan D. Halverson and Grosberg (2014); Ramakrishnan et al. (2015); Dekker et al. (2013). Furthermore, when studying DNApolymer organization in the interphase stage, physicists (and we) assume the system to be in a state of local equilibrium so that principles of statistical mechanics can be applied.
Studies have shown that the organization of chromatin is a fractal globule rather than an equilibrium coil Imakaev et al. (2015); Mirny (2011). The understanding is that segments of DNA get locally collapsed to form unentangled crumpled sections of the coil, such that within a segment there are many contacts, and fewer number of contacts between collapsed neighboring segments. These then show up as TADs in the contact map.
In the last years, there have been more detailed polymeric models which can reproduce the experimentally measured TADs for sections of DNA. The most successful of them are the SBS (Strings and Binders) model Chiariello et al. (2016b); Gilbert et al. (2017) and the loop extrusion model Fudenberg et al. (2016c); Alipour and Marko (2012); Naumova et al. (2013). In the SBS model, monomers along a chain have the same size but have distinct affinities of attraction for freely diffusing bindermolecules. There are as many distinct kinds of binder molecules as different kinds of monomers. Monomers of the same kind but separated along the chain contour can get attached to the same binder molecule to result in the formation of loops. Some parameters, such as the number of different kind of monomers or the number of monomers of each kind, can be optimally chosen to reproduce and fit the TADs of a particular segment of a DNA by solving a multidimensional optimization problem. In the loop extrusion model, there are boundary elements (BE monomers) at specific sections along the chains. A pair of special monomers (LEmonomers), which probabilistically bind with each other to extrude loops of variable lengths by diffusing/translocating along the length of the chain, but LEmonomers are constrained to remain bounded between the BEmonomers. Again, a search through a large parameter space leads to optimal TADs with a quantitative match with experimental data. Both models seem to crucially depend on the formation and contact between suitably sized loops at appropriate locations, which in turn results in a match with experimental contact map data. Other researchers Rousseau et al. (2011); BaÃ¹ et al. (2011) use optimization algorithms with weighted constraints to get an idea of the large scale structure of biomolecules.
Instead of investigating the origin of TADs where some headway has already been made , we ask a different question. Given the contact map, can we predict the global spatial organization of a polymer? Is there even any organization of the entire macromolecule? Note that the contact map does not give information of spatial organization of polymer, it just gives the frequency of finding different DNAsections in proximity. In this study, We assume that polymersections with the highest frequencies of contact to be permanently crosslinked and investigate if crosslinks (CLs) above a minimal number and at special biologically welldetermined locations along the chain (as determined from the contact map) play a vital role in giving shape and structure to the entire DNApolymer. We compare the organization of a polymer with CLs at biologically determined positions along the contour (Biocrosslinks: BC) with polymer organization with equal number of crosslinks between monomers, but the monomers to be crosslinked are chosen at random (random crosslinks RC). The question is whether an equal number of minimal constraints at random positions give ”structure” to the polymer? We generate ten independent random configurations of crosslinks (CLs) and compare ”structure” of polymers for ten independent RCs and 1 BC.
It is easier to work with simpler systems, e.g. DNA of bacteria such as Escherichia coli (E. coli) which is a ring polymer. Bacterial cells have no nucleus, the number of chromosomes is typically or per cell, and the DNAs are much shorter. Bacterial DNA also shows TADs, and they have DNAbinding proteins Dixon et al. (2012). We choose a beadspring flexible polymer model of E. Coli, a bacteria with a single chromosome for our studies. The question is how do we determine if a polymer, which is expected to be unstructured, is structurally organized or not.
We start our Monte Carlo (MC) simulations from independent initial configurations of ring polymers, without taking into account that crosslinked monomers should be in proximity. However, crosslink potentials are applied between monomer pairs from a particular CLset. We then allow the chain to relax to equilibrium for each case in independent MC runs. If we observe that the DNA polymer relaxes to almost the same “structure” within statistical fluctuations in each case, then we could claim that the polymer is organized. Also, we try out with fewer CLs and check the minimal number of CLs required to achieve organization of the polymer. To check for the organization, we had to come up with different structural quantities which we describe in the next sections. Our hypothesis of special crosslinks in DNApolymers can be further established by testing it on more bacterial chromosomes.
We have not included confinement effects due to cell walls due to two reasons: (a) the bacterial DNA is within the nuceloid region which occupies only % to % of the cell volume Joyeux (2015) (b) we wanted to focus only on the effects of having CLs at specific locations unencumbered by any other competing effects. Also, we do not put effects of supercoiling in our simple beadspring polymer model because proximity effects between segments due to supercoiling, if any, should show up in the contact maps.
The organization of the manuscript is as follows: the next section, viz., section II discusses the model of DNApolymer, the computational method by which we generate initial conformations, methodology to relax polymers to local equilibrium using after which we calculate ensemble averages of statistical quantities to identify spatial organization of the polymer. The next section, section III, discusses the statistical quantities and our results by which we arrive at our conclusions. We end with summarizing our conclusions section IV.
Ii Model and simulation method.
We use Monte Carlo simulations to explore the different microstates of the DNA chain. The DNA of bacteria E. coli is a ring polymer. We model E. Coli DNA with a beadspring model of ring polymer with number of monomers in the ring. Thus, each coarse grained monomer bead in our model represents BPs. The DNA modelpolymer is placed near the center of the simulation box of size with periodic boundary conditions (PBC). The quantity is the unit of length and is the average distance between two neighboring monomer beads along the chain contour. The box size is chosen to be much larger than the expected maximum diameter of the polymer coil. The diameter of monomer bead is chosen to be . The LennardJones potential, suitably truncated at and shifted (the Weeks Chandler AndersenWCA potential), is used to model the excluded volume interaction between the monomers. A harmonic spring potential connects adjacent monomers along the chain contour. , where is the distance between two monomers and is the unit of length.
We have chosen , where the thermal energy is the unit of energy in our simulations.
Using data from Cagliero et al. (2013) and subsequent analysis methods which are described in detail in the Appendix1, we obtained the frequency of finding two segments of E. ColiDNA spatially close to each other. We use this data from contact maps as an input to our simulations. The experimental resolution of the size of segments is base pairs. The model monomer in our simulations represents a DNA segment exactly of the same size as the experimental resolution. We crosslink monomers whose frequency of being in spatial proximity is greater than threshold frequency . Depending on the value of threshold frequency that we choose, we can have (a) or (b) pairs of monomers of the DNApolymer that we crosslink. We bind these pairs of monomers together by an additional spring potential with . The crosslinked monomers are held together at a distance of , but the different CLs can move with respect to each other as the chain explores different conformations.
The set of CLs from biological contactmap data, which we refer to as BC1 in the rest of the paper, are a subset of CLs which we call BC2. To analyze whether the overall mesoscale organization of the chain is determined primarily by a particular choice of CLs, we start our simulations from independent initial conditions. For example, in one of the initial conditions, the monomers of the ring polymer are arranged along a circle of radius such that one circle has monomers. The circle of monomers are stacked up to form a cylinder. Note that this will lead to monomer numbered as and the last monomer to be at a distance much larger than though it is a ring polymer. Also, the monomers which form CLs can be at distances much larger than as they get arranged along the cylinder. But these will come closer due to the presence of harmonic spring potentials acting between monomerpairs as the polymer is allowed to relax during the MC run. In two other initial conditions, we arrange the monomers in circles of radius and . For the next three initial conditions, the monomers are arranged in squares of side , and ; these squares are stacked then up. For the last three initial conditions, we arrange monomers in equilateral triangles of side , , and stack them to form a vertical column. By such initial conditions, we ensure that the monomer pairs which constitute a CL are at arbitrary positions relative to each other in space.
The question we then ask is: as the chains relax from their initial conditions to their equilibrium conformations in different Monte Carlo runs, do all of them organize themselves in some particular set of conformations (in a statistical sense), though the initial configurations were very different? If they do, we can expect that the presence of CLs to play a significant role in the organization, since a normal ring polymer is not expected to show structural organization.
We use additional techniques to allow the chain to relax slowly over Monte Carlo iterations to its equilibrium state without allowing the system to get stuck in some entangled and metastable state. We set spring constant of crosslinks at the start of the simulation and gradually ramp it in steps of every MC steps, as the CL monomers approach each other in the relaxation process. In a standard Metropolis step, a monomer attempts a displacement in a random direction, where and are random numbers. The attempt is accepted with Boltzmann probability. In addition, every iterations, we attempt displacements with . This helps chains to cross each other at times and overcome topological constraints which might arise as the chain relaxes from its initial condition.
We monitor the potential energy as the chain relaxes. The value of energy relaxes to the same value at the end of iterations from the different runs, see Figure 1. It gives us confidence that the chain conformations are not stuck at metastable energy minima. From this initial state, we evolve each of the different chain conformations in independent simulation runs over the next iterations and collect data to calculate and compare structural quantities. We carry out this comparison of statistical data from independent runs for each set of CLs, viz., chains with (a) (b) CLs.
In addition, we also carry out similar calculations starting from independent initial configurations for each of the distinct sets of the randomly chosen position of CLs (monomers which are crosslinked together are chosen randomly from the list of monomers). The effective number of CLs for RC1 and RC2 correspond to the number of CLs in BC1 and BC2. In BC1 and BC2 set of CLs, there are some CLs which are not independent. For example, monomer number and are crosslinked to monomer and , respectively. One cannot consider them as distinct CLs. The list of crosslinked monomers is given in Table1 of Supplementary section TABLE . Hence there are fewer effective CLs than the number of CLs in BC1 and BC2. Thus we compare the results of our simulation from bioCLs (BC1 and BC2) with an equal number of effective random CLs. In each of these random set of CLs, we have the same number of effective CLs as the ones obtained from biological data, which is less than the corresponding number of CLs in BC1 and BC2. Hence the list of randomly positioned CLs have just (a) effective number of CLs (we refer these as RC1) and (b) effective CLs (referred as RC2), corresponding to CLs in BC1 and CLs in BC2. We can now compare structural data obtained from polymer simulations using BC1 and RC1 on the one hand, and BC2 and RC2 on the other.
Iii Results
Now we discuss the statistical quantities which we use to investigate the structure and conformation of the ring polymer. We aim to check if statistical quantities from different runs with the same set of CLs give similar results to infer that the polymer has similar shape and conformation across runs. We further compare data from different RC1 and RC2 CL sets with data from E.Coli CL set BC1 and BC2, though in this manuscript we show data primarily from one representative RC set.
The first quantity we want to estimate is the size and extent of the polymer with CLs. To that end, we calculate the moment of inertia tensor with respect to the center of mass (CM) of the polymer coil and diagonalize the matrix to get its principal moments for each microstate. We then calculate the average principal moments . where is the largest eigenvalue and the smallest.
In Fig 2(a) we show the values of for distinct random CLsets but having the same number of CLs as RC1 and RC2. For each random CLset, the average is taken over independent initial configurations. In subplot (b) we show for Biologically determined CLs: BC1, BC2 for 9 independent initial conditions. In plot (c) for different random CL sets having same number of CLs as RC1 and RC2 is shown and in subplot (d) we show for independent initial conditions for BC1 and BC2 respectively. Here is the sum of masses of the individual monomers , is the mass of each monomer. The value of is the ratio of major and minor axes and gives a measure of shape asymmetry of the coil. Comparing the value of in 2(a), (b) we see that has a lower value for all ten RC2 sets compared to BC2 set. A plausible explanation for this difference is given later in this paragraph and confirmed by the end of this paper. Subplots Fig 2 (b) and (d) show the values of obtained from randomly determined CLs and Biologically determined CLs. The calculated value of for ring polymer without CLs and the average value is . The value of decreases as we increase the number of CLs from BC1/RC1 set to BC2/RC2 sets; this decrease in the value with increase in the number of effective constraints is expected. But interestingly, the change in the value of as we go from BC1 to BC2 is distinctly less than the decrease in as we go from RC1 to RC2. We interpret the difference between the two cases as follows: the effective CLs in BC1 are already at critical positions along the contour which give partial organization in the DNA. On increasing the number of CLs (BC2), the organization of the molecule improves along the already established framework. On the other hand, an increase in the number of random CLs leads to an overall shrinkage in the size of the coil and not necessarily to accentuate a preferred set of conformations. The lower values of for all 10 sets of RC2 compared to that in BC2 also point towards such an understanding. This idea will get further substantiated in the rest of the paper.
To get some idea of how the monomers of the polymer are distributed in space. And if there is any difference in the radial arrangement of bioCLs and random CLs, we investigate the radial distribution of monomer number densities and the normalized CL number density with the distance from the center of mass (CM) of the polymer coil. The quantities and are calculated by calculating the average number of monomers and CLs in radial shells of width from the CM of the coil, divided by the volume of each shell. The CLdensity is further normalized by the total number of CLs for the particular case under consideration to obtain . Data for and from independent runs are plotted for each of set of CLs: BC1, BC2, and one set of RC1, RC2 in Figs.3 and 4, respectively. Small standard deviation from average for monomer number densities and the normalized CL number density is an indication that the arrangement of monomers and CLs have relaxed to similar distributions and is independent of starting configuration of monomers.
Comparing subplots (a),(b) of Figs. 3 and 4 for BC1 and BC2, establishes that coils with a higher number of CLs lead to more number density of monomers and CLs at the center of the coil. As the coil gets into a more compact coil structure with increased number of CLs in set RC2 as compared to RC1, we again see an increase in the number density of monomers, CLs (suitably normalized) at the center. Comparing data for BC1 and RC1, respectively from Figs. 3 and 4, we observe that the distribution of monomers and CLs are similar at different for the 2 cases. In contrast, the normalized density of CLs at the central region is more for BC2 compared to that for RC2. Moreover, monomer density for BC2 is lower than that for RC2 at the center of the coil, whereas there are more monomers present at the periphery (for ) for BC2 as compared to RC2. Since the number of monomers in each shell is divided by the volume of the shell, the difference in the number of monomer at the periphery is just discernable from the number density plots. Lastly, the number density of monomers/CLs drops down significantly beyond a distance of from the coil’s center. Other nine sets of randomly chosen CLs also in tune with the above observations (data not shown).
To gain some more insight about the global structural organization of the DNAcoil, the simplest question to ask is whether a particular CL is always found near the center of the coil or near the periphery of the coil. To this end, we compute the probability of each of the CLs to be found in the inner,middle, and outer regions of the DNAcoil. We use the cutoff radii , (chosen from the knowledge of the value of ) and calculate the probability of finding the th CL within distance (inner region), (middle region) and (outer region), respectively, from the coil’s center of mass. If the values of for each CL has small deviation from the average value in each of the independent runs, it would indicate that the presence of CLs leads to similar organization of the DNA across independent runs. Also, we compare the probability distribution of CLs for runs with bioCLs and randomCLs to investigate if bioCLs lead to organization distinct from that obtained with randomCLs.
We carry out the same exercise for different segments of the polymer chain. The E. Coli chain with monomers is divided into segments with monomers in each of segment and the segments are labeled from as we move along the contour. We can then calculate the location of the CMs of each segment, and find out the probability of finding the CMs in the central, middle and outer region. The segments in a randomwalk polymer model (without CLs) can take any conformation, and there is no reason to believe that certain segments will preferably be found in the inner or outer regions of the coil. If the segments were completely delocalized, we would expect the polymer in different microstates to contribute to all the quantities for each segment. The question is to what extent will this basic behavior of polymer coils get modified by the presence of bioCLs and randomCLs?
Probability data about the location of CLs and segments for BC2 and RC2 is given in 5 and 6, respectively. Data for BC1 and RC1 is given in the Supplementary data section Fig.16, 17. Furthermore, from Figs 5 and 6(a),(b) and (c) we see that some CLs (e.g. the CL with index ) has the nearly equal probability of being in the inner or middle region of the coil, but very low probability to be found in the outer region. For BC2, most CLs are found in the inner and middle regions of the coil whereas for RC2 CL set there are some CLs at the periphery; refer Fig.5. On the other hand, from Fig.6 we see a larger number of segments have a finite probability to be in the outer regions for BC2 as compared to data for RC2. The data consistently shows that the position of CL, as well as segments are localized in space across different runs.
Having established that the CLs and segments of DNApolymer coil have some degree of radial organization, we try to extract more detailed structural information about the position of segments relative to each other within the coil. We calculate the probability of each CL (alternatively, each segment) to be in proximity to other CLs (alternatively, other segments). If there are no particular welldefined relative positions of CLs/segments within the chaincoil, there is no reason to expect CM of some segments (or independent CLs) to be found spatially close to each other with high probability, especially when the segments/CLs are separated along the chain contour. We define two CLs/segment’s CM to be close to each other if the distance between the CLs/segmentCMs is , which is just more than . We emphasize that we have crosslinked monomers, these constraints are at the monomer ( BP) length scale, whereas we are investigating the organization of polymer segments at a much larger lengthscale. The position of CLs, position of CM of segments are just 2 different markers of different sections of the chain and we use relative position of both to identify spatial correlations between different sections of the chain.
In Fig. 7 we show colormaps showing the average probability of finding each pair CLs at distances of for BC2, RC2 for two independent runs. As the Monte Carlo simulation evolves, at each microstate if the distance between a pair of CLs is such that , a counter for pair is incremented. The probability at the end of the MCrun is the value of , where is the number of microstates over which data is calculated for calculation. The xaxis and the yaxis represent CL indices , and the colored pixel indicates the value of . The top two colormaps of Fig.7) represent data obtained for BC2, and the bottom two colormaps Fig.7 show corresponding data from two independent runs with RC2 set of CLs. A pair of CLs which are near each other along the contour of the chain will have the distance between them by default, and will show up as high probabilities in the colormap. We set these in the calculation if the monomers constituting pair of nearby CLs are separated by less than monomers along the contour. We do this because we want to see only nontrivial correlations between different CLs. Following Fig. 7, the colormaps show probability of finding a pair of segmentCMs within distance of for BC1/RC1 and BC2/RC2 is shown in Figs.8 and 9, respectively. Note that these probability colormaps give much more detailed information than a pair correlation function , which would just give the average distance between CLs or segmentCMs.
Data showing probabilities to find segments CM within a distance of is shown in Figs.8 and 9 for BC1,RC1 and BC2,RC2, respectively. We arrive at some conclusions by comparing different pairs of probabilitycolormaps in Figs. 7,8 and 9. Firstly, comparing colormaps for data from different initial conditions, e.g. compare the top two colormaps in each of the figures which are for BC1/BC2 (or equivalently compare the bottom two colormaps which are for RC1/RC2), shows bright and dark patches at equivalent positions in the map. Thus the same set of CLs and segments are spatially near each other in both the runs, i.e. the polymer organization is similar in both the runs. Additional colormaps from two more independent runs for each set of CLs are also given in the Supplementary section for further comparison. The reference to relevant colormaps in the Supplementary section is given in the figure caption of each figure, and these further reiterates our conclusion that the structural organization of DNApolymer is similar across different runs for the same set of CLs. Thus we find further evidence of our hypothesis that the set of CLs decides the large scale structure of the polymer.
Secondly, the number of the bright pixels are much more in colormaps obtained using CL sets BC2 and RC2 (Figs.9) as compared to colormaps for BC1 (Fig. 8). It is not surprising as more constraints due to the presence of higher number of CLs lead to relatively more compact welldefined structure and a large number of CLs (or segments) near one another. With the few bright patches for BC1, RC1 CL set with effective CLs, one cannot clearly define the mesoscale conformation of the whole chain, though there are indications of the emergence of structure. However, a set of effective CLs for BC2, RC2 might be enough to deduce and define the largescale organization of DNApolymer as we now know which segments are neighbors of a particular segment.
Thirdly, comparison of colormaps for BC2 and RC2, especially in Figs. 9 show a different nature of the organization of the DNA polymer. For BC2 adjacent segments show higher propensity to be together, which can be deduced by observing that there are clusters of adjacent bright pixels. Comparatively, bright pixels are scattered more randomly in the colormaps for RC2. From the colormaps, we can clearly, observe that there is a difference in the nature of patterns for BC2 and RC2.
Fourthly and importantly, the reasons for the formation of clusters of bright pixels seen in the top two colormaps of Figs. 7 (for CLs) is not the same as that of Fig.9 (for segmentCMs). To understand the bright patches of Fig. 7, we remind the reader that the CLs are often found adjacent to each other along the chain contour for BC1 and BC2. Suppose, CL, CL and CL are next to each other along the chain. Note that then , , has been explicitly put to zero. But if CL, which is far from and along the contour, comes within a distance of from CL, then CL is also automatically close to CL and three adjacent pixels will appear in the colormap, viz., , ,. Thus, the bigger bright patches for BC2 in Fig. 7 should not necessarily be interpreted as evidence for a more organized polymer. A similar arrangement of bright/dark pixels across runs is just evidence of similar organization across different runs.
To quantify the differences in the colormaps of BC2 and RC2 in Fig. 9, we calculate the number of segments, , which are near (i.e. within distance ) to the CM of the th segment with probability . That is we count the number of nonblack pixels in the colormaps of fig.9) for a particular segment with index . Then we divide by the total number of segments to get to get an estimate of the fraction of a total number of segments which approach segment with any finite probability. It is shown in the Fig.10 for RC2 and BC2. A cutoff of for the value of is appropriate as anyways most of the colormap is black and deep red going upto yellow for very few pixels. From the figure, we observe that the value of is relatively high for RC2 set of CLs as compared to for bio BC2, this suggests for random CLset many more segments can approach a particular segment for RC2 compared to that for BC2. We interpret this as a more spatially organized structure with BC2 crosslinks, as it has fewer but welldefined neighbors as can also be checked from the colormap of Fig.9. As an example, segments with indices 7078 for BC2 are only close to their adjacent segments (bright diagonal patch in the colormap) giving relatively very low value of in Fig.10(a).
We have also obtained colormaps for the different sets of random CLs (data not shown), and for each CLset we can calculate for each segment index . Moreover, we can calculate , that is the average value of summed over all the segment indices, i.e. . Furthermore, we can calculate the mean of over independent runs for each CL set, and thereby obtain . In Fig.10(b), we plot versus the random CLset index, each set has the same number of CLs as in RC2. We compare this data with the for the one set of biologically obtained CLs: BC2. We clearly see that for each random CLsets the quantity has relatively higher value than f for BC2. Observing the differences in colormaps for BC2 and RC2, we claim that the position of CLs along the chain for DNA are not completely random. An equivalent number of CLs in random positions also give an organized structure in that the colormaps from independent runs look similar, but the nature of organization is very different from the case where biological position of CLs are chosen.
To extract further insight into the structural organization of the DNApolymer, we would next probe whether the segments are at geometrically fixed positions with respect to each other, of course accounting for thermal fluctuations. Thereby, we next calculate the angular correlations between CLs and equivalently between segment’s CMs.
To that end, we calculate the dot product of the radial vectors from the CM of the polymer coil to the respective positions of a pair of CLs () and check if the value of or , where is the angle between the two vectors. If the value of , we can say that the two CLs are on the same side/hemisphere of the coil, and increment counter by . If we decrement by . For all possible pairs of CLs, we calculate the average value of suitably normalized by the number of snapshots used to calculate the average. The value of would indicate that the pair of CLs are always on two opposite hemispheres. A value of means that the two CLs remain on the same hemisphere. We should not interpret as we cannot claim that the average angle between the radial vectors is nearly a right angle. The reason is that if the CLs are closer to the center of the DNAcoil, small positional displacements could cause the quantity to fluctuate between and and cause to average out to zero. The data for all pairs of CLs are given in Fig.11 for BC2/RC2 respectively, the corresponding data for relative angular positions for the segment’s CMs are given in Figs.12 & 13 for BC1/RC1 and BC2/RC2. As before, the top two colormaps in all the four figures are from two independent initial conditions with BC1/BC2 and the bottom two colormaps are for two independent runs with RC1/RC2.
In the colormaps of Figs. 11, 12 and 13 we see there are patches of bright and dark pixels, the size of patches are larger for BC2 compared to RC2. As mentioned before, if which is represented by orange/deep yellow color in the colormap we cannot predict the angular positions of the CLs/segment’s CMs because of the reason explained above. We can clearly see that the colormaps from independent runs starting from different initial conditions look similar.
In figure 12, comparing segmentCM colormaps in (a),(b) (for BC1) with (c),(d) (for RC1) we do not find any difference in the nature of distribution of patches. But as the number of CLs increase as we go from BC1 to BC2 and RC1 to RC2 in Fig.13, we find differences in the pattern of colormaps on comparing (a),(b) with (c),(d) corresponding to BC2 and RC2 CL sets, respectively. In contrast, for color maps (a),(b) of Fig.13 we observe large patches of bright pixels as compared to the patches in (c),(d). Large patches of bright/dark pixels for BC2 suggest adjacent segments along the chain contour are on the same/opposite hemispheres with respect to the CM of the coil. The small patches of bright and dark pixels in (c),(d) for RC2 suggest more of random distribution of different segments. The polymer is organized in both BC2 and RC2 CL sets as colormaps from independent runs look similar, but the nature of the organization is different. The reasons for large bright patches in the colormaps for CLangular positions as shown in Fig. 11 is not same as for the colormaps in Fig.13. The reasons for the difference has been explained previously for positional correlation colormaps.
Finally, we show a representative snapshot of the DNApolymer in Fig.14 (top). The polymer is colored from blue to red as we go from monomer index 1 to 4642 along the contour. This snapshot confirms what we have deduced from the previous figures of positional and angular correlations. Large sections of the chain are localized together in space. The snapshot confirms the kind of conformations expected from the colormaps of angular correlation shown in Fig.13(a) and (b). For example, the section marked Region1 representing monomers around 1750 (segment index 30) is diametrically opposite Region3 with monomer index 2990 (segment index 50). In Fig.13 (a) we see the pixel corresponding to segment indices (30,50) are black. The Region2 represents monomer numbered around 4100, segment index 71. We can see the pixels corresponding to segment indices (30,71) is yellow whereas pixels for (50,71) is white. The bottom figure shows the CL distribution in space: only one of monomers out of the pair which constitutes a CL has been plotted.
It is interesting to observe in Fig.14 (bottom) that the CLs are clumped together in space in about four aggregates. We believe that this helps in the mesoscale organization of the chain as multiple segments of the chain are pulled towards the coil’s center with multiple loops on the periphery of the coil. The peripheral loops can lead to relatively large fluctuations in the values of as seen in Fig.2. This is further validated by the Fig.6(c), where we see a large number of segments are to be found in the outer region with significant probabilities. Thus BC2 set of crosslinks leads to the reorganization of the CLs in space such that they form clusters in space with the possibility of polymer loops emanating from the CLclusters in a rosettelike structure. We interpret that loops from a particular CL cluster would be neighbours of specific other polymer segments due to the nature of arrangement, as opposed to spatial proximity to many segments as seen for RC2 in Fig.10 while comparing colormaps for BC2 and RC2.
We also calculate the distribution of length of segments between two adjacent CLs in the bio and 4 representative random sets. It is given in the Fig.15. The distribution of lengths is a fixed quantity once one chooses a particular CL set as an input to the simulation. The number of segments of length between two adjacent CLs along the chain contour has been shown on xaxis (using a bin size of 10 monomers) and the frequency density function (FDF) is plotted on the yaxis, where . We denote the number of segments of length by and is the total number of segments between CLs. Thus for RC2 as each CL is constituted of a pair of monomers, and essentially is a count of the number of monomers between two CLmonomers along the contour. For the bioCL, one particular monomer is attached with many other monomers (see supplementary material Table1) hence there is a peak at segment length value to . We also observe in the randomly chosen CL sets after segment length FDF is almost zero while in the biologically obtained CL set there are a few segments till segment length . This shows in the biological CL set there are several longer segments which can form bigger loops as compared to CLs chosen in a random manner.
Iv Discussion
The primary and new conclusions of our study is that if particular sets of monomers in a DNAring polymer are held together by suitable proteins (crosslinks at specific points in our model polymer), it leads to an organization of the polymer coil. The number of effective CLs is 82 for a ring polymer of 4642 monomers, or approximately % of the polymer chain. Moreover, the monomers which are crosslinked in the bacterial DNA are not randomly chosen from the length of the contour and lead to an organization of the ring polymer into a particular organization which is very different and distinct compared to what is obtained using an equal number of random crosslinks. Of course, the DNA polymer undergoes local conformational fluctuations due to the thermal energy but overall the structure is maintained in a statistical sense. We can deduce the presence of distinctive mesoscale organization of DNA from the calculation of three quantities: (a) radial distribution of segments, (b) positional correlations between segments and (c) angular correlations between segments. Thus we have much more detailed information of organization of different segments than can be obtained from pair correlation function. We have used CLs for our simulation of DNApolymer, but these should be considered as only effective CLs. A minimal number of CLs are required to be able to claim that there is a distinct organization of the DNApolymer since we do not obtain a welldefined structure with bioCLs (equivalently effective CLs). We can predict the 2d arrangement of different segments relative to each other with the statistical quantities obtained. We find the clusters of CLs towards the center of the coil, these CLs are pulling different segments of the chain towards the center, and many loops on the periphery, which we interpret as the rosettelike structure. We have given a possible argument of how and why the structure with relatively well localized DNApolymer segments is achieved in a polymer, but a full understanding and systematic methodology of the choice of CLpositions from the view of polymer physics can be developed only in future, when we will have access to the larger number of contact maps of many DNAs
V Supplementary Material
See supplementary material for the table of crosslinked monomers, colormaps of radial and angular correlations from additional runs and information of radial location of CLs and segments for BC1 and RC1.
Vi Acknowledgments
We acknowledge the use of computational facilities provided by DBT Alliance, project numbers IA/I/12/1/500529,IA/I/11/2500290, to C. Assisi, S. Nadkarni, M.S. Madhusudhan. We also acknowledge the use of a cluster bought using DSTSERB grant no. EMR/2015/000018 to A. Chatterji. A. C. acknowledges funding support by DST Nanomission, India under the Thematic Unit Program (grant no.: SR/NM/TP13/2016).
Vii Appendix: Generation of contact frequency map
In the field of bioinformatics, a sequence database is a biological database which is a collection of computerized nucleic acid sequences. Pairedend sequencing allows researchers to sequence both ends of a DNAfragment to generate highquality, alignable sequence data. Pairedend sequencing facilitates detection of genomic reorganization and repetitive sequence elements.
In a paired endsequencing run, the distance between the alignments of the two fragments is the length of the DNA fragment being sequenced. Aligners (software tools) use this information to better align reads when faced with a read that align to multiple regions such as those that may lie in a repeat region. To avoid this behavior, the reads are aligned in single end mode while keeping track of the pairs. We have employed the BWA Li and Durbin (2010) aligner to align reads as it has best sensitivity among short read aligners.
The aligned reads are then binned at the desired resolution (or the minimum distance between restriction sites). A 2D matrix with the required number of bins is initialized. A large fraction of the reads in a 3C library were from fragments that were not crosslinked and fall into the same bin or bins adjacent to each other. These read pairs were filtered out. The counter in the bin with the coordinates indicated by the alignment of each read in the pair is incremented for all the remaining reads. The filled matrix gives the total number of contacts between different parts of the genome and the resulting matrix is called the contact map.
To be able to compare between different runs, the contact map is normalized so that effect of varying number of sequenced reads is accounted for. Each sum of the number of contacts in each row and column in the matrix were normalized to 1. This provides a normalized contact map, which can now be used to elucidate the 3D structure of the genome and compare changes across different conditions.
Escherichia coli (E. coli) strain K12MG1622 were obtained from German collection of microorganisms and cell cultures at Leibniz institute (DSMZ).
The aligned reads were then binned at the desired resolution (or the minimum distance between restriction sites). A 2D matrix with the required number of bins was initialized. A large fraction of the reads in a 3C library were from fragments that were not crosslinked and fall into the same bin or bins adjacent to each other. These read pairs were filtered out. The counter in the bin with the coordinates indicated by the alignment of each read in the pair is incremented for all the remaining reads. The filled matrix gives the total number of contacts between different parts of the genome and the resulting matrix is called the contact map. To be able to compare between different runs, the contact map were normalized so that effect of varying number of sequenced reads is accounted for. Each sum of the number of contacts in each row and column in the matrix were normalized to 1. This provides a normalized contact map, which can now be used to elucidate the 3D structure of the genome.
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Supplementary Materials
Appendix A List of crosslinked monomers in our simulations.
In the following table, we list the monomers which are crosslinked to model the constraints for the DNA of bacteria Caulobacter Crecentus. Note that for random cross links (CL) set1 and set2 (RC1, RC2) we have fewer number of CLs, as there are fewer effective CLs in the list of CLs.
In particular while counting the number of independent CLs, one should
pay special attention to the points listed below. As a consequence, CLs of BC1 should
be counted as only independent CLs. Hence, we use just CLs in RC1, when we compare organization
of RC1 and BC1. Correspondingly, we have just CLs in RC2, instead of in BC2.

The rows corresponding to independent crosslinks of set BC1 are marked by , one can observe that the next row of CLs are adjacent to the monomers marked just previously by . These cannot be counted as independent CLs.

The rows marked by is not a independent CL at all, monomers and are trivially close to each other by virtue of their position along the contour.
This table has been generated by analysis of raw data obtained from C. Cagliero et. al., Nucleic Acids Res, 41, 60586071 (2013).
  BC1  RC1  BC2  RC2  
Serial  Monomer  Monomer  Monomer  Monomer  Monomer  Monomer  Monomer  MOnomer 
no.  index1  index2  index1  index2  Index1  Index2  Index1  Index2 
1  1  4642  1  4642  1  4642  1  4642 
2  16  2515  3739  4531  16  2515  3739  4531 
3  17  2516  3011  1610  17  2516  3011  1610 
4  20  1051  2582  4367  20  1051  2582  4367 
5  224  2731  3370  1680  20  3584  3370  1680 
6  225  2731  556  2622  21  1050  556  2622 
7  226  2730  1676  1426  21  3584  1676  1426 
8  226  3428  998  2741  224  2731  998  2741 
9  227  2728  474  2233  224  3429  474  2233 
10  227  2729  2522  533  224  4208  2522  533 
11  228  2727  1967  2490  224  4209  1967  2490 
12  228  2728  2536  616  225  2730  2536  616 
13  229  2727  769  4614  225  2731  769  4614 
14  271  4509  2  2023  226  2729  2  2023 
15  272  4508  3494  2484  226  2730  3494  2484 
16  275  1300  2534  1365  226  3427  2534  1365 
17  280  1051  3053  2256  226  3428  3053  2256 
18  291  1051  3779  2647  226  4038  3779  2647 
19  316  393  4199  4452  226  4169  4199  4452 
20  317  2172  2839  1309  227  2728  2839  1309 
21  382  1469  1385  449  227  2729  1385  449 
22  383  1469  4398  371  228  2727  4398  371 
23  527  1529  522  1434  228  2728  522  1434 
24  575  1301  3676  320  228  3946  3676  320 
25  609  2515  178  4317  229  2727  178  4317 
26  730  3763  3220  515  229  3424  3220  515 
27  731  3764  527  2992  229  3947  527  2992 
28  732  3765      229  3948  2391  1402 
29  733  735      229  4172  284  4086 
30  733  3766      229  4213  4311  2243 
31  1301  3132      229  4214  283  1687 
32  1433  1635      271  4509  1599  2420 
33  1434  1634      272  1471  4445  3365 
34  1533  3626      272  4508  1523  739 
35  1571  3667      274  1301  2721  113 
36  1572  3668      275  1300  1371  4360 
37  2728  3945      275  1301  4137  2593 
38  2729  3945      275  3130  1026  2807 
39  2730  3943      276  2291  3007  2767 
40  3429  3942      276  3130  1576  1282 
41  3471  4177      276  3367  3041  3010 
42  3620  3763      280  292  2558  2709 
43  3620  3764      280  1050  2156  3872 
44  3621  3764      280  1051  945  4229 
45  3622  3765      291  1051  4465  2873 
46  3622  3766      291  2760  1943  4488 
47  3623  3766      315  393  4286  881 
48          316  391  3282  3882 
49          316  392  3555  2445 
50          316  393  1196  40 
51          317  392  1997  3918 
52          317  568  4178  1595 
53          317  569  678  3768 
54          317  1094  3519  164 
55          317  1095  2979  4115 
56          317  2172  2871  3747 
57          382  1469  3930  4263 
58          383  1468  2787  2654 
59          383  1469  1101  831 
60          393  567  2785  1485 
61          393  1096  3477  1069 
62          393  2171  2345  795 
63          526  1529  4037  3848 
64          527  1529  395  1040 
65          527  1530  328  930 
66          575  1301  1926  2551 
67          576  3130  4440  1484 
68          576  3367  3799  4456 
69          581  1636  4129  837 
70          581  1637  1500  1352 
71          582  1636  3197  947 
72          608  2515  263  3435 
73          609  2515  2272  277 
74          688  1301  4276  702 
75          730  3763  3405  978 
76          731  3763  388  3658 
77          731  3764  2796  1022 
78          732  3621  3411  1122 
79          732  3764  861  2185 
80          732  3765  3564  1606 
81          733  735  1860  1447 
82          733  3623  904  3577 
83          733  3765     
84          733  3766     
85          734  3765     
86          734  3766     
87          738  1533     
88          738  3626     
89          782  2522     
90          1051  3585     
91          1208  1210     
92          1269  1271     
93          1301  1398     
94          1301  2102     
95          1301  2289     
96          1301  3132     
97          1301  3366     
98          1301  3652     
99          1397  2573     
100          1397  3118     
101          1433  1635     
102          1434  1634     
103          1435  1633     
104          1469  2071     
105          1470  2071     
105          1470  2071     
106          1470  2998     
107          1470  3186     
108          1470  4498     
109          1470  4508     
110          1470  4509     
111          1471  4508     
112          1533  3625     
113          1533  3626     
114          1571  3667     
115          1572  3667     
116          1572  3668     
117          2726  4172     
118          2727  4172     
119          2728  3945     
120          2728  4039     
121          2728  4171     
122          2729  3945     
123          2729  4038     
124          2730  3943     
125          2730  4038     
126          2731  3942     
127          2731  4036     
128          2732  3942     
129          3424  4172     
130          3426  3945     
131          3426  4156     
132          3427  3944     
133          3427  4038     
134          3428  3943     
135          3428  3944     
136          3429  3942     
137          3471  4177     
138          3472  4176     
139          3472  4177     
140          3619  3763     
141          3620  3763     
142          3620  3764     
143          3621  3764     
144          3621  3765     
145          3622  3765     
146          3622  3766     
147          3623  3766     
148          3623  3768     
149          3942  4167     
150          3942  4209     
151          3943  4038     
152          3944  4038     
153          3944  4169     
154          3944  4210     
155          4036  4167     
156          4036  4209     
157          4037  4443     
158          4041  4214     
159          4172  4214     
Appendix B Radial location of CLs and segment’s CM of E.Coli.
In the main manuscript, we show the radial organization of different CLs and segmentCMs in Figs.6 and Fig.7, respectively for BC2 and compare it with the DNApolymer with CLs corresponding to RC2, which has the same number of effective CLs as in BC2. In the following, we give analogous plots with BC1 and RC1.