Summary A new level of chromosome organization, Topologically Associating Domains (TADs), was recently uncovered by chromosome-confirmation-capture (3C) techniques. To explore TAD structure and function, we developed a polymer model that can extract the full repertoire of chromatin conformations within TADs from population-based 3C data. This model predicts actual physical distances and to what extent chromosomal contacts vary between cells. It also identifies interactions within single TADs that stabilize boundaries between TADs and allows us to identify and genetically validate key structural elements within TADs. Combining the model’s predictions with high-resolution DNA FISH and quantitative RNA FISH for TADs within the X-inactivation center (Xic), we dissect the relationship between transcription and spatial proximity to cis-regulatory elements. We demonstrate that contacts between potential regulatory elements occur in the context of fluctuating structures rather than stable loops and propose that such fluctuations may contribute to asymmetric expression in the Xic during X inactivation.
Understanding how regulatory sequences interact in the context of chromosomal architecture is a central challenge in biology. Chromosome conformation capture revealed that mammalian chromosomes possess a rich hierarchy of structural layers, from multi-megabase compartments to sub-megabase topologically associating domains (TADs) and sub-TAD contact domains. TADs appear to act as regulatory microenvironments by constraining and segregating regulatory interactions across discrete chromosomal regions. However, it is unclear whether other (or all) folding layers share similar properties, or rather TADs constitute a privileged folding scale with maximal impact on the organization of regulatory interactions. Here, we present a novel algorithm named CaTCH that identifies hierarchical trees of chromosomal domains in Hi-C maps, stratified through their reciprocal physical insulation, which is a single and biologically relevant parameter. By applying CaTCH to published Hi-C data sets, we show that previously reported folding layers appear at different insulation levels. We demonstrate that although no structurally privileged folding level exists, TADs emerge as a functionally privileged scale defined by maximal boundary enrichment in CTCF and maximal cell-type conservation. By measuring transcriptional output in embryonic stem cells and neural precursor cells, we show that the likelihood that genes in a domain are coregulated during differentiation is also maximized at the scale of TADs. Finally, we observe that regulatory sequences occur at genomic locations corresponding to optimized mutual interactions at the same scale. Our analysis suggests that the architectural functionality of TADs arises from the interplay between their ability to partition interactions and the specific genomic position of regulatory sequences.
In performing protein-denaturation experiments, it is common to employ different kinds of denaturants interchangeably. We make use of molecular dynamics simulations of Protein L in water, in urea, and in guanidinium chloride (GdmCl) to ascertain if there are any structural differences in the associated unfolding processes. The simulation of proteins in solutions of GdmCl is complicated by the large number of charges involved, making it difficult to set up a realistic force field. Furthermore, at high concentrations of this denaturant, the motion of the solvent slows considerably. The simulations show that the unfolding mechanism depends on the denaturing agent: in urea the beta-sheet is destabilized first, whereas in GdmCl, it is the alpha-helix. Moreover, whereas urea interacts with the protein accumulating in the first solvation shell, GdmCl displays a longer-range electrostatic effect that does not perturb the structure of the solvent close to the protein.
A paradigm in transcriptional regulation is that graded increases in transcription factor (TF) concentration are translated into on/off transcriptional responses by cooperative TF binding to adjacent sites. Digital transcriptional responses underlie the definition of anatomical boundaries during development. Here we show that NF-kappaB, a TF controlling inflammation and immunity, is conversely an analog transcriptional regulator that uses clustered binding sites noncooperatively. We observed that increasing concentrations of NF-kappaB are translated into gradual increments in gene transcription. We provide a thermodynamic interpretation of the experimental observations by combining quantitative measurements and a minimal physical model of an NF-kappaB-dependent promoter. We demonstrate that NF-kappaB binds independently to adjacent sites to promote additive RNA Pol II recruitment and graded transcriptional outputs. These findings reveal an alternative mode of operation of clustered TF binding sites, which might function in biological conditions where the transcriptional output is proportional to the strength of an environmental input.
The results of minimal model calculations indicate that the stability and the kinetic accessibility of the native state of small globular proteins are controlled by few "hot" sites. By means of molecular dynamics simulations around the native conformation, which describe the protein and the surrounding solvent at the all-atom level, an accurate and compact energetic map of the native state of the protein is generated. This map is further simplified by means of an eigenvalue decomposition. The components of the eigenvector associated with the lowest eigenvalue indicate which hot sites are likely to be responsible for the stability and for the rapid folding of the protein. The comparison of the results of the model with the findings of mutagenesis experiments performed for four small proteins show that the eigenvalue decomposition method is able to identify between 60% and 80% of these (hot) sites.Keywords: protein folding; protein stability; molecular dynamics; local elementary structures The study of how the structure and stability of a protein is connected with its amino acid sequence has been, during recent years, a major focus of research. In particular, the problem of protein folding and its relation to stability has been studied through a variety of experimental and theoretical techniques. These studies have highlighted the central role the free energy landscape plays in determining the properties displayed by proteins. The discussion has then been turned to the problem of determining this landscape for specific proteins (Shea and Brooks 2001).Experimental techniques have yielded detailed information on macroscopic features of protein dynamical behavior, such as folding times and stability, and on some specific issues at the level of amino acids, such as the sensitivity to mutations (cf. Fersht 1999). However, there is still no experimental procedure capable of providing insight at the amino acid level into either the folding process in its completeness or the stabilization determinants of proteins. To gain insight into these questions, one must turn to theoretical and computational methods.Because of the high computational costs involved, realistic models that provide an exhaustive description of the free energy landscape have been used in the study of only short peptides. Ferrara and Caflish (2000), for instance, could reconstruct the whole free energy landscape of a small designed -sheet peptide with an all-atom representation of the solute and an implicit model of the solvent. Daura et al. (1998) were able to demonstrate the reversible folding of a small (seven residues) helix forming -peptide in methanol solvent using long molecular dynamics (MD) simulations.On the other hand, minimal models (Chan and Dill 1991;Sali et al. 1994;Mirny and Shakhnovich 2001;Micheletti 2003) have provided important results about the general features of the free energy landscape of proteins. They give an approximate description of both the interaction energy among amino acids (usually through a contact potential en-
The development of new techniques to quantitatively measure gene expression in cells has shed light on a number of systems that display oscillations in protein concentration. Here we review the different mechanisms which can produce oscillations in gene expression or protein concentration, using a framework of simple mathematical models. We focus on three eukaryotic genetic regulatory networks which show "ultradian" oscillations, with time period of the order of hours, and involve, respectively, proteins important for development (Hes1), apoptosis (p53) and immune response (NF-κB). We argue that underlying all three is a common design consisting of a negative feedback loop with time delay which is responsible for the oscillatory behaviour.
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