You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: How epigenome drives chromatin folding and dynamics, insights from efficient coarse-grained models of chromosomes}
Article:
Abstract:
The 3D organization of chromosomes is crucial for regulating gene expression and cell function. Many experimental and polymer modeling efforts are dedicated to deciphering the mechanistic principles behind chromosome folding. Chromosomes are long and densely packed—topologically constrained—polymers. The main challenges are therefore to develop adequate models and simulation methods to investigate properly the multi spatio-temporal scales of such macromolecules. Here, we proposed a generic strategy to develop efficient coarse-grained models for self-avoiding polymers on a lattice. Accounting accurately for the polymer entanglement length and the volumic density, we show that our simulation scheme not only captures the steady-state structural and dynamical properties of the system but also tracks the same dynamics at different coarse-graining. This strategy allows a strong power-law gain in numerical efficiency and offers a systematic way to define reliable coarse-grained null models for chromosomes and to go beyond the current limitations by studying long chromosomes during an extended time period with good statistics. We use our formalism to investigate in details the time evolution of the 3D organization of chromosome 3R( 20 Mbp) in drosophila during one cell cycle( 20 hours). We show that a combination of our coarse-graining strategy with a one-parameter block copolymer model integrating epigenomic-driven interactions quantitatively reproduce experimental data at the chromosome-scale and predict that chromatin motion is very dynamic during the cell cycle.
Introduction:
Though all cells of a multicellular organism contain the same genetic information, they vary widely in shapes, physiologies, and functions. These differences mainly reflect variations in gene expression between different tissues or cell types. Recent experiments have highlighted the important role of the physical organization of chromosomes inside the cell nucleus in regulating gene expression[1–3]: gene activities being modulated, not only by the local folding of the chromatin fiber but also by its higher order organization with 3D nuclear compartments favorable to gene activation or repression. During interphase, the longest phase of the cell cycle where genes are expressed and DNA is replicated, chromosomes are found to be organized hierarchically. Confocal and electron microscopy experiments have revealed that each chromosome occupies its own territory[4]. Also, the genes sharing the same transcriptional state tend to colocalize[5–7]: inactive genomic regions( the heterochromatin) being more peripheral while active regions( the euchromatin) being more central. At the sub-chromosomal level, advanced molecular biology tools, like chromosome conformation capture( Hi-C) techniques, have shown that chromosomes are partitioned into consecutive 3D interaction compartments, the so-called topologically-associated domains( TADs),[8–10]. Loci inside these domains experience enriched contact probabilities with other loci of the same domain while showing partial insulation from loci of nearest neighbor domains. These domains can be easily visualized as consecutive “squares” along the diagonal of a 2D contact frequency matrix( see section ‘Application to chromatin folding in drosophila’ for illustration). TADs formation has been associated with the local biochemical composition of chromatin, the so-called epigenome, which encodes for gene activity[7, 11–14]: genes inside the same TAD tends to have the same epigenomic state, and long-range contacts may be observed between TADs of the same state. However, how genome precisely organized in space is still not fully understood and addressing this question represents one of the most exciting challenges of modern biology[15]. Lots of experimental and modeling efforts are currently dedicated to understand the mechanisms implied in chromosome folding. In particular, polymer models have been instrumental in simulating and testing different molecular and physical mechanisms and in driving new experiments[5, 16–39]. An important challenge for such models is to simulate, with good precision, the behavior of long polymer chains( the typical size of a chromosome ranging from about a million base-pairs in yeast to hundreds of Mbp in human) during an extended time period( of the order of hours for a typical cell cycle). Therefore the standard strategy used in these approaches is to start from a coarse-grained “null” model for chromatin with few basic interactions[40]. Then eventually decorate it with more physical or chemical interactions driven by biological information such as the gene activity or the local epigenomic state. Chromosomes being very long polymers densely packed into the cell nucleus, topological constraints generated by polymer entanglement may play an essential role in controlling the dynamics and fluctuations of such polymers[16, 41]. However, when building their null models, very few approaches account adequately of such constrained situations. As neglecting topological constraints may lead to different structural and dynamical properties of the polymer, how can one interpret the outcomes correctly of such models concerning realistic mechanisms, if the considered null model is already biased? Here, we develop a generic coarse-graining strategy for self-avoiding polymers that allows, simultaneously, to drastically reduce the computation time while maintaining the polymer in the same topological regime and thus preserving the correct structural and dynamical properties. In the first part, we explain the strategy and demonstrate its efficiency, leading to a systematic approach to develop coarse-grained null models for chromosomes. In the second part, we apply it to investigate the role of epigenomic-driven interactions in the folding and dynamics of drosophila genome. Finally, we discuss our results and their implications in the general context of chromosome modeling and chromatin biology.
Discussion:
In the first part of this article, we introduced a new coarse-graining strategy for long and dense self-avoiding walks that conserves the entanglement regime and the volumic density. Using kinetic Monte Carlo simulations on a lattice, we demonstrated that such strategy not only leads to the accurate description of the steady-state but also the time-evolution of the expected structural and dynamical properties of the underlying fine-scale model. We showed that by introducing an effective rigidity to the coarse-grained model and by controlling the volume fraction, we could achieve very high gain in numerical( CPU-time) efficiency while maintaining a quantitative approximation and minimizing the loss in spatial and temporal resolution of the model. Using our efficient polymer model one can simulate chromosome dynamics during the whole cell cycle on a desktop computer within a day. While we illustrated our approach using chromosomes on a lattice model as toy examples, our strategy is generic and can be applied to any self-avoiding polymers and off-lattice systems. We emphasized that neglecting topological constraints may lead to an erroneous description of the fine-scale model. Therefore the effect of supplementary interactions added to the null model, to describe specific observations present in experimental data, may lead to misinterpretation. In the second part, we used our strategy to build a coarse-grained null model for chromatin and decorated it with a copolymer framework based solely on epigenomic data to investigate the folding and dynamics of a big fraction of chromosome 3R of drosophila. It is the first study trying to quantitatively describe the behavior and time evolution of such large genomic regions( 20 Mbp) during one cell cycle( 20h of real time) with high precision( 10 kbp resolution). Our heteropolymer model has one free parameter, the strength of short-range interaction Ei between genomic loci having the same epigenomic state. Our findings are in qualitative agreement with our previous works based on shorter pieces of chromosomes[20, 32], but significantly improve our description of chromosome folding in drosophila. By varying Ei, we showed that the system continuously switches from a dynamic homogeneous crumpled-like behavior to a crumpled heterogeneous micro-phased state. Interestingly, we observed that during this transition, the chromosome fluctuations characterized by the mean squared displacement conserve the same scaling behavior( g1( t) ≈ γt0. 4) with exponents compatible with a crumpled polymer. However, the prefactor γ depends on Ei and is sharply reduced above a given strength of interaction, characteristics of a glass-like transition[67, 68]. Another interesting observation was that the sequence-average contact probability Pc( s), a quantity directly comparable to experimental data, is independent of Ei( at least for weak, biologically relevant values) and is same as in the reference null model, as already observed by Gursoy et al[71]. This motivates, afterward, the validity of homopolymer models, extensively used by polymer physicists, to study the generic physical principles behind chromosome folding based on comparison with sequence-average experimental data[16, 41, 43, 58], even if such chromatin organization is strongly heterogeneous. This also suggests that, before adding specific interactions to the system, any quantitative approach should first aim to describe such sequence-average behaviors in a null homogeneous model. Comparing our model predictions for chromosome 3R to the corresponding Hi-C data, we observed an excellent agreement at all scales, strongly suggesting that epigenomics is a primary driver of chromosome folding in drosophila. The strength of interaction compatible with the data is weak( ∼ 0. 1 kT) and locate the in vivo situation in the transition zone between the homogeneous crumpled and the micro-phased states. TADs are only partially collapsed and interact dynamically with other TADs of the same main epigenomic state. This suggests a substantial stochasticity in chromosome organization, consistent with recent single-cell Hi-C or super-resolution experiments[72–77]. We also detected several discrepancies between the predicted and experimental contact maps. For example, the model predicts spurious contacts or misses some between some TADs. This could be due to a wrong annotation of the local epigenomic state or the existence of specific interactions driven by other biological processes not accounted in the model. For example, refining the model to account more precisely for the local epigenetic content( for example the relative levels of histone modifications or chromatin-binding proteins) or differences in interaction strengths between different states would certainly lead to a better correspondence. We also observed that TADs are more sharply defined in the experiments, particularly in the corners of large TADs. This might be the results of the presence of cis-interacting mechanisms, like supercoiling[23, 78] or the recently proposed loop extrusion model in mammals[26, 27], that enhance the contact frequency along the genome. To exploit the capacity of the model to simulate long trajectories( 20 hrs), we analyzed the time evolution of chromosome organization. TADs formed very quickly( within minutes), entirely consistent with Hi-C data made on synchronized mammalian cells showing that, in early G1, TADs are already observable in the data[76, 79]. Formation of long-range contacts require more time and eventually appear hours after the mitotic exit, also consistent with the time evolution of Hi-C data during the cell cycle in human[76, 79] and yeast[80]. To go deeper into this characterization and get insights into the dynamics of contact formation, we tracked pairs of loci. At the investigated resolution( 10 kbp), contacts are transient and their typical lifetime is ∼ 10 seconds, indicating a very dynamic situation, consistent with many experiments performed on living cells[81]. Probability distribution functions of the first encounter time, the contact time, or the search time are polynomial, suggesting a possible connection with fractional brownian motion physics, as for bacterial chromosomes[82]. Interestingly, we predicted that a significant proportion( 5 − 15%) of long-range contacts( > 1 Mbp) are not established within one cell cycle. This suggests that the genomic distance between regulatory elements should not exceed 1 Mbp to ensure that physical contact between the elements, prerequisite to an activation or repression event, would happen at least once in the cell cycle in order to maintain a stable regulation or gene expression. With the recent progress in genome editing[83], it would be interesting to experimentally test such predictions by simultaneously tracking the distance between a promoter and its enhancer and the current gene activity[84], for various genomic distances between the two elements. All this suggests that the 3D chromosome organization in higher eucaryotes is out-of-equilibrium and the chromatin is very dynamic. This emphasizes the necessity to adequately account for the time evolution of such organization in quantitative models of chromosomes, especially for higher eucaryotes where chromosomes are strongly topologically constrained. As a proof of concept, we demonstrated the utility of our coarse-graining approach to study chromatin organization in drosophila. However, the numerical efficiency of the method opens new perspectives to investigate the physical and mechanistic principles behind chromosome folding more deeply with many aspects remain to be understood. For example, extrapolating to the whole human genome, it would require ∼120h of CPU time with our strategy at 10kbp resolution to simulate one cell cycle while it remains elusive to do it with the fine-scale model( > 100 years of CPU time). The possibility to easily simulate the dynamics of chromosomes or full genomes during long biologically relevant time period would allow to quantitatively investigate in the future the role of other types of interactions, like those associated with the nuclear membrane, another important player in organizing chromosomes inside the nucleus[25, 85] and the crosstalk with epigenomic-driven interaction as presented here. This situation seems particularly attractive to describe the reorganization of chromatin during senescence where constitutive heterochromatin detaches from the membrane to form large foci at the interior of the nucleus[86].
The chromosome architecture inside cell nuclei plays important roles in regulating cell functions . Many experimental and modeling efforts are dedicated to deciphering the mechanisms controlling such organization . There are proliferations of experimental studies which report the hierarchical structure of chromosomes but how exactly they physically organize in 3D is not fully understood . In modeling , the main challenges are to develop adequate models and simulation methods to investigate correctly these highly dense long polymer chains . Taken into consideration the fundamental physical characteristics of chromosomes , we developed robust and numerically efficient polymer models that enabled us to explore the dynamics of long chromosomes over long time periods with good statistics . We applied this framework to investigate the dynamical folding of chromosome in drosophila . Accounting for the local biochemical information , we were able to reproduce the experimentally-measured contact frequencies between any pairs of genomic loci quantitatively and to track the hierarchical chromosome structure throughout the cell cycle . Our results further support the picture of a very dynamic chromosome organization driven by weak short-range interactions .