Epidermal growth factor-mediated T-cell factor/lymphoid enhancer factor transcriptional activity is essential but not sufficient for cell cycle progression in nontransformed mammary epithelial cells

Because beta-catenin target genes such as cyclin D1 are involved in cell cycle progression, we examined whether beta-catenin has a more pervasive role in normal cell proliferation, even upon stimulation by non-Wnt ligands. Here, we demonstrate that epidermal growth factor (EGF) stimulates T-cell factor/lymphoid enhancer factor (Tcf/Lef) transcriptional activity in nontransformed mammary epithelial cells (MCF-10A) and that its transcriptional activity is essential for EGF-mediated progression through G(1)/S phase. Thus, expression of dominant-negative Tcf4 blocks EGF-mediated Tcf/Lef transcriptional activity and bromodeoxyuridine uptake. In fact, the importance of EGF-mediated Tcf/Lef transcriptional activity for cell cycle progression may lie further upstream at the G(1)/S phase transition. We demonstrate that dominant-negative Tcf4 inhibits a reporter of cyclin D1 promoter activity in a dose-dependent manner. Importantly, dominant-negative Tcf4 suppresses EGF- mediated cell cycle activity specifically by thwarting EGF- mediated Tcf/Lef transcriptional activity, not by broader effects on EGF signaling. Thus, although expression of dominant-negative Tcf4 blocks EGF- mediated TOPFLASH activation, it has no effect on either EGF receptor or ERK phosphorylation, further underscoring the fact that Tcf/ Lef-mediated transcription is essential for cell cycle progression, even when other pro-mitogenic signals are at normal levels. Yet, despite its essential role, Tcf/Lef transcriptional activity alone is not sufficient for cell cycle progression. Serum also stimulates Tcf/ Lef transcriptional activation in MCF-10A cells but is unable to promote DNA synthesis. Taken together, our data support a model wherein EGF promotes Tcf/ Lef transcriptional activity, and this signal is essential but not sufficient for cell cycle activity

Signal Processing during Developmental Multicellular Patterning

Developing design strategies for tissue engineering and regenerative medicine is limited by our nascent understanding of how cell populations self-organize into multicellular structures on synthetic scaffolds. Mechanistic insights can be gleaned from the quantitative analysis of biomolecular signals that drive multicellular patterning during the natural processes of embryonic and adult development. This review describes three critical layers of signal processing that govern multicellular patterning: spatiotemporal presentation of extracellular cues, intracellular signaling networks that mediate crosstalk among extracellular cues, and finally, intranuclear signal integration at the level of transcriptional regulation. At every level in this hierarchy, the quantitative attributes of signals have a profound impact on patterning. We discuss how experiments and mathematical models are being used to uncover these quantitative features and their impact on multicellular phenotype

Bioengineering models of cell signaling

Strategies for rationally manipulating cell behavior in cell-based technologies and molecular therapeutics and understanding effects of environmental agents on physiological systems may be derived from a mechanistic understanding of underlying signaling mechanisms that regulate cell functions. Three crucial attributes of signal transduction necessitate modeling approaches for analyzing these systems: an ever-expanding plethora of signaling molecules and interactions, a highly interconnected biochemical scheme, and concurrent biophysical regulation. Because signal flow is tightly regulated with positive and negative feedbacks and is bidirectional with commands traveling both from outside-in and inside-out, dynamic models that couple biophysical and biochemical elements are required to consider information processing both during transient and steady-state conditions. Unique mathematical frameworks will be needed to obtain an integrated perspective on these complex systems, which operate over wide length and time scales. These may involve a two-level hierarchical approach wherein the overall signaling network is modeled in terms of effective "circuit" or "algorithm" modules, and then each module is correspondingly modeled with more detailed incorporation of its actual underlying biochemical/biophysical molecular interactions

Three-Dimensional Analysis of the Effect of Epidermal Growth Factor on Cell-Cell Adhesion in Epithelial Cell Clusters

The effect that growth factors such as epidermal growth factor (EGF) have on cell-cell adhesion is of interest in the study of cellular processes such as epithelial-mesenchymal transition. Because cell-cell adhesions cannot be measured directly, we use three-dimensional traction force microscopy to measure the tractions applied by clusters of MCF-10A cells to a compliant substrate beneath them before and after stimulating the cells with EGF. To better interpret the results, a finite element model, which simulates a cluster of individual cells adhered to one another and to the substrate with linear springs, is developed to better understand the mechanical interaction between the cells in the experiments. The experiments and simulations show that the cluster of cells acts collectively as a single unit, indicating that cell-cell adhesion remains strong before and after stimulation with EGF. In addition, the experiments and model emphasize the importance of three-dimensional measurements and analysis in these experiments

Hydration and mobility of HO-(aq)

The hydroxide anion plays an essential role in many chemical and biochemical reactions. But a molecular-scale description of its hydration state, and hence also its transport, in water is currently controversial. The statistical mechanical quasi-chemical theory of solutions suggests that HO[H2O]3- is the predominant species in the aqueous phase under standard conditions. This result is in close agreement with recent spectroscopic studies on hydroxide water clusters, and with the available thermodynamic hydration free energies. In contrast, a recent ab initio molecular dynamics simulation has suggested that HO[H_2O]4- is the only dominant aqueous solution species. We apply adiabatic ab initio molecular dynamics simulations, and find good agreement with both the quasi-chemical theoretical predictions and experimental results. The present results suggest a picture that is simpler, more traditional, but with additional subtlety. These coordination structures are labile but the tri-coordinate species is the prominent case. This conclusion is unaltered with changes in the electronic density functional. No evidence is found for rate-determining activated inter-conversion of a HO[H2O]4- trap structure to HO[H2O]3-, mediating hydroxide transport. The view of HO- diffusion as the hopping of a proton hole has substantial validity, the rate depending largely on the dynamic disorder of the water hydrogen-bond network.Comment: 7 pages, 5 figures, additional results include

An Information-Theoretic Characterization of the Optimal Gradient Sensing Response of Cells

Many cellular systems rely on the ability to interpret spatial heterogeneities in chemoattractant concentration to direct cell migration. The accuracy of this process is limited by stochastic fluctuations in the concentration of the external signal and in the internal signaling components. Here we use information theory to determine the optimal scheme to detect the location of an external chemoattractant source in the presence of noise. We compute the minimum amount of mutual information needed between the chemoattractant gradient and the internal signal to achieve a prespecified chemotactic accuracy. We show that more accurate chemotaxis requires greater mutual information. We also demonstrate that a priori information can improve chemotaxis efficiency. We compare the optimal signaling schemes with existing experimental measurements and models of eukaryotic gradient sensing. Remarkably, there is good quantitative agreement between the optimal response when no a priori assumption is made about the location of the existing source, and the observed experimental response of unpolarized Dictyostelium discoideum cells. In contrast, the measured response of polarized D. discoideum cells matches closely the optimal scheme, assuming prior knowledge of the external gradient—for example, through prolonged chemotaxis in a given direction. Our results demonstrate that different observed classes of responses in cells (polarized and unpolarized) are optimal under varying information assumptions

Co-Regulation of Metabolic Genes Is Better Explained by Flux Coupling Than by Network Distance

To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools

Quantitative effect of scaffold abundance on signal propagation

Protein scaffolds bring together multiple components of a signalling pathway, thereby promoting signal propagation along a common physical 'backbone'. Scaffolds play a prominent role in natural signalling pathways and provide a promising platform for synthetic circuits. To better understand how scaffolding quantitatively affects signal transmission, we conducted an in vivo sensitivity analysis of the yeast mating pathway to a broad range of perturbations in the abundance of the scaffold Ste5. Our measurements show that signal throughput exhibits a biphasic dependence on scaffold concentration and that altering the amount of scaffold binding partners reshapes this biphasic dependence. Unexpectedly, the wild-type level of Ste5 is ~ 10-fold below the optimum needed to maximize signal throughput. This sub-optimal configuration may be a tradeoff as increasing Ste5 expression promotes baseline activation of the mating pathway. Furthermore, operating at a sub-optimal level of Ste5 may provide regulatory flexibility as tuning Ste5 expression up or down directly modulates the downstream phenotypic response. Our quantitative analysis reveals performance tradeoffs in scaffold-based modules and defines engineering challenges for implementing molecular scaffolds in synthetic pathways