Gamze Gürsoy

Spatial confinement is a major determinant of the folding landscape of human chromosomes

The global architecture of the cell nucleus and the spatial organization of chromatin play important roles in gene expression and nuclear function. Single-cell imaging and chromosome conformation capture-based techniques provide a wealth of information on the spatial organization of chromosomes. However, a mechanistic model that can account for all observed scaling behaviors governing long-range chromatin interactions is missing. Here we describe a model called constrained self-avoiding chromatin (C-SAC) for studying spatial structures of chromosomes, as the available space is a key determinant of chromosome folding. We studied large ensembles of model chromatin chains with appropriate fiber diameter, persistence length and excluded volume under spatial confinement. We show that the equilibrium ensemble of randomly folded chromosomes in the confined nuclear volume gives rise to the experimentally observed higher-order architecture of human chromosomes, including average scaling properties of mean-square spatial distance, end-to-end distance, contact probability and their chromosome-to-chromosome variabilities. Our results indicate that the overall structure of a human chromosome is dictated by the spatial confinement of the nuclear space, which may undergo significant tissue- and developmental stage-specific size changes.

Unique Toll-Like Receptor 4 Activation by NAMPT/PBEF Induces NFκ B Signaling and Inflammatory Lung Injury

Ventilator-induced inflammatory lung injury (VILI) is mechanistically linked to increased NAMPT transcription and circulating levels of nicotinamide phosphoribosyl-transferase (NAMPT/PBEF). Although VILI severity is attenuated by reduced NAMPT/PBEF bioavailability, the precise contribution of NAMPT/PBEF and excessive mechanical stress to VILI pathobiology is unknown. We now report that NAMPT/PBEF induces lung NFκB transcriptional activities and inflammatory injury via direct ligation of Toll–like receptor 4 (TLR4). Computational analysis demonstrated that NAMPT/PBEF and MD-2, a TLR4-binding protein essential for LPS-induced TLR4 activation, share ~30% sequence identity and exhibit striking structural similarity in loop regions critical for MD-2-TLR4 binding. Unlike MD-2, whose TLR4 binding alone is insufficient to initiate TLR4 signaling, NAMPT/PBEF alone produces robust TLR4 activation, likely via a protruding region of NAMPT/PBEF (S402-N412) with structural similarity to LPS. The identification of this unique mode of TLR4 activation by NAMPT/PBEF advances the understanding of innate immunity responses as well as the untoward events associated with mechanical stress-induced lung inflammation.

Mechanical Signaling on the Single Protein Level Studied Using Steered Molecular Dynamics

Efficient communication between the cell and its external environment is of the utmost importance to the function of multicellular organisms. While signaling events can be generally characterized as information exchange by means of controlled energy conversion, research efforts have hitherto mainly been concerned with mechanisms involving chemical and electrical energy transfer. Here, we review recent computational efforts addressing the function of mechanical force in signal transduction. Specifically, we focus on the role of steered molecular dynamics (SMD) simulations in providing details at the atomic level on a group of protein domains, which play a fundamental role in signal exchange by responding properly to mechanical strain. We start by giving a brief introduction to the SMD technique and general properties of mechanically stable protein folds, followed by specific examples illustrating three general regimes of signal transfer utilizing mechanical energy: purely mechanical, mechanical to chemical, and chemical to mechanical. Whenever possible the physiological importance of the example at hand is stressed to highlight the diversity of the processes in which mechanical signaling plays a key role. We also provide an overview of future challenges and perspectives for this rapidly developing field.

Computationally efficient measure of topological redundancy of biological and social networks.

It is well known that biological and social interaction networks have a varying degree of redundancy, though a consensus of the precise cause of this is so far lacking. In this paper, we introduce a topological redundancy measure for labeled directed networks that is formal, computationally efficient, and applicable to a variety of directed networks such as cellular signaling, and metabolic and social interaction networks. We demonstrate the computational efficiency of our measure by computing its value and statistical significance on a number of biological and social networks with up to several thousands of nodes and edges. Our results suggest a number of interesting observations: (1) Social networks are more redundant that their biological counterparts, (2) transcriptional networks are less redundant than signaling networks, (3) the topological redundancy of the C. elegans metabolic network is largely due to its inclusion of currency metabolites, and (4) the redundancy of signaling networks is highly (negatively) correlated with the monotonicity of their dynamics.

Computational predictions of structures of multichromosomes of budding yeast

Knowledge of the global architecture of the cell nucleus and the spatial organization of genome is critical for understanding gene expression and nuclear function. Single-cell imaging techniques provide a wealth of information on the spatial organization of chromosomes. Computational tools for modelling chromosome structure have broad implications in studying the effect of cell nucleus on higher-order genome organization. Here we describe a multichromosome constrained self-avoiding chromatin model for studying ensembles of genome structural models of budding yeast nucleus. We successfully generated a large number of model genomes of yeast with appropriate chromatin fiber diameter, persistence length, and excluded volume under spatial confinement. By incorporating details of the constraints from single-cell imaging studies, our method can model the budding yeast genome realistically. The model developed here provides a general computational framework for studying the overall architecture of budding yeast genome.

Computational construction of 3D chromatin ensembles and prediction of functional interactions of alpha-globin locus from 5C data

Abstract Conformation capture technologies measure frequencies of interactions between chromatin regions. However, understanding gene-regulation require knowledge of detailed spatial structures of heterogeneous chromatin in cells. Here we describe the nC-SAC (n-Constrained-Self Avoiding Chromatin) method that transforms experimental interaction frequencies into 3D ensembles of chromatin chains. nC-SAC first distinguishes specific from non-specific interaction frequencies, then generates 3D chromatin ensembles using identified specific interactions as spatial constraints. Application to α-globin locus shows that these constraints (∼20%) drive the formation of ∼99% all experimentally captured interactions, in which ∼30% additional to the imposed constraints is found to be specific. Many novel specific spatial contacts not captured by experiments are also predicted. A subset, of which independent ChIA-PET data are available, is validated to be RNAPII-, CTCF-, and RAD21-mediated. Their positioning in the architectural context of imposed specific interactions from nC-SAC is highly important. Our results also suggest the presence of a many-body structural unit involving α-globin gene, its enhancers, and POL3RK gene for regulating the expression of α-globin in silent cells.

Multiscale Modeling of Cellular Epigenetic States: Stochasticity in Molecular Networks, Chromatin Folding in Cell Nuclei, and Tissue Pattern Formation of Cells

Genome sequences provide the overall genetic blueprint of cells, but cells possessing the same genome can exhibit diverse phenotypes. There is a multitude of mechanisms controlling cellular epigenetic states and that dictate the behavior of cells. Among these, networks of interacting molecules, often under stochastic control, depending on the specific wirings of molecular components and the physiological conditions, can have a different landscape of cellular states. In addition, chromosome folding in three-dimensional space provides another important control mechanism for selective activation and repression of gene expression. Fully differentiated cells with different properties grow, divide, and interact through mechanical forces and communicate through signal transduction, resulting in the formation of complex tissue patterns. Developing quantitative models to study these multi-scale phenomena and to identify opportunities for improving human health requires development of theoretical models, algorithms, and computational tools. Here we review recent progress made in these important directions.

Three-dimensional chromosome structures from energy landscape

The human genome contains about 2-m length of DNA and is packed into a small cell nucleus of approximately cubic-micrometer size. A central question in genome biology is to understand how chromatins are organized in such a compact volume, while biological functions such as gene expression, DNA replication, and DNA repair are robustly orchestrated. Over the past two decades, experimental studies based on chromatin fragmentation and proximity cross-linking have given us quantitative information on the frequencies of long-range interactions among genomic elements (1). With recent development of the Hi-C methodology (2), frequencies of such interactions are now known at 1-kB resolution (3). These studies lead to discoveries of finer organizational structures of compartments, subcompartments, and topologically associated domains (TADs) (3, 4). Although these structures have been inferred from analysis of heat maps of frequencies of genomic interactions (3, 5, 6), a grand challenge in studying the 3D genome is to gain mechanistic understanding of the general principles governing chromatin folding and their spatial organization. In PNAS, Di Pierro et al. (7) introduce an energy landscape theory and a predictive model of chromosome architecture. Di Pierro et al. start by considering the roles of specific biochemical interactions. Although generic polymer models of chromatin have generated important insight into the overall behavior of chromatin, growing evidence suggests that biochemical interactions are critical for 3D genome organization (8). Di Pierro et al. assume that chromosomes fold under the influence of a cloud of proteins, which bind to different sections of chromatin with different affinities and specificities. To recapture the energy landscape governed by these interactions, Di Pierro et al. develop the minimal chromatin model. The first ingredient of Di Pierro et al.’s model is the partitioning of the genome into intervals of a handful of types. Each interval type is … [↵][1]1To whom correspondence should be addressed. Email: jliang{at}uic.edu. [1]: #xref-corresp-1-1

Stochastic Focusing and Defocusing in Biological Reaction Networks: Lessons Learned from Accurate Chemical Master Equation (ACME) Solutions

Biological reaction networks are stochastic due to random thermal fluctuations. Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small and often results in unexpected outcomes. For example, it has been shown that a basic enzymatic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the increasing signal noise [1]. Although stochastic simulation algorithm has been widely used to study such systems, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF. Here we use the ACME method for the exact solution of the discrete Chemical Master Equation and study the probability landscape of product molecules in the basic enzymatic reaction system used in the original SF study [1]. Examinations of the effects of signal molecules under different stochastic processes show that SF is at play as stochastic changes enhance the system sensitivity. However, we also observed that the noise in signaling under certain stochastic processes in the same reaction network lead to a decrease in the system sensitivities, thus the network experiences stochastic defocusing. We further show that signal molecules following certain stochastic processes in the same reaction network can give rise to noise-induced bistability in the distribution of product molecules. These results highlight the fundamental role of stochasticity in biological reaction networks and the need for exact computation of probability landscape of the molecules in the system. It also points to possible importance of positive and negative feedback loops in such networks for control of the intrinsic noise.[1] Paulsson et. al, 2000, PNAS.

Effects of Electric Field on Channel Proteins Through Dipole Perturbation and Network of Signal Transmission

Kv1.2 voltage-gated and MlotiK1 cyclic nucleotide-gated K+ channels belong to the family of tetrameric cation channels and share a similar protein fold in the transmembrane region. Kv1.2 channel is activated by the changes in the transmembrane potential, while MlotiK1 channel is activated upon the binding of cyclic nucleotides to its intracellular domain. We use a perturbation-based markovian transmission model [Lu and Liang,PLoS Comp. Biol. 2009] to study allosteric activation pathways in both channels. The initial perturbation on residues, e.g., ligand binding or conformational change, is converted to flow of probability, which allows studying of the time-course of signal transmission and propagation of probability flow through the protein molecule. As dipoles in channel proteins respond to the external electric field, change in energy and introduction of torque arise for individual residues. We postulate residues that experience large energy change and torque are those responding first to the membrane depolarization in ion channels. To identify regions of initial perturbation, we build structural models by embedding channel proteins in the POPC lipid bilayer, with surrounding slabs of water molecules on both sides of the membrane. Our calculations identified S1 helix of voltage sensing domain, linker, and filter regions in Kv1.2 channel, as well as helix S1 and linker in MlotiK1 channel as the regions of initial response, as they contain the majority of strongly polarizable dipoles. Our results show that dipole perturbation results in a strong signal transmittion to the charged arginine residues of S4 in Kv1.2, whereas no significant signal transmission is observed under the same perturbation for MlotiK1 channel. This suggests dipole perturbation is a mechanism how voltage gated channel proteins respond to external electric field. This mechanism, however, is not employed by ligand-gated channels.