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

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

A Characterization of Scale Invariant Responses in Enzymatic Networks

An ubiquitous property of biological sensory systems is adaptation: a step increase in stimulus triggers an initial change in a biochemical or physiological response, followed by a more gradual relaxation toward a basal, pre-stimulus level. Adaptation helps maintain essential variables within acceptable bounds and allows organisms to readjust themselves to an optimum and non-saturating sensitivity range when faced with a prolonged change in their environment. Recently, it was shown theoretically and experimentally that many adapting systems, both at the organism and single-cell level, enjoy a remarkable additional feature: scale invariance, meaning that the initial, transient behavior remains (approximately) the same even when the background signal level is scaled. In this work, we set out to investigate under what conditions a broadly used model of biochemical enzymatic networks will exhibit scale-invariant behavior. An exhaustive computational study led us to discover a new property of surprising simplicity and generality, uniform linearizations with fast output (ULFO), whose validity we show is both necessary and sufficient for scale invariance of enzymatic networks. Based on this study, we go on to develop a mathematical explanation of how ULFO results in scale invariance. Our work provides a surprisingly consistent, simple, and general framework for understanding this phenomenon, and results in concrete experimental predictions

Predicting Phenotypic Diversity and the Underlying Quantitative Molecular Transitions

During development, signaling networks control the formation of multicellular patterns. To what extent quantitative fluctuations in these complex networks may affect multicellular phenotype remains unclear. Here, we describe a computational approach to predict and analyze the phenotypic diversity that is accessible to a developmental signaling network. Applying this framework to vulval development in C. elegans, we demonstrate that quantitative changes in the regulatory network can render ~500 multicellular phenotypes. This phenotypic capacity is an order-of-magnitude below the theoretical upper limit for this system but yet is large enough to demonstrate that the system is not restricted to a select few outcomes. Using metrics to gauge the robustness of these phenotypes to parameter perturbations, we identify a select subset of novel phenotypes that are the most promising for experimental validation. In addition, our model calculations provide a layout of these phenotypes in network parameter space. Analyzing this landscape of multicellular phenotypes yielded two significant insights. First, we show that experimentally well-established mutant phenotypes may be rendered using non-canonical network perturbations. Second, we show that the predicted multicellular patterns include not only those observed in C. elegans, but also those occurring exclusively in other species of the Caenorhabditis genus. This result demonstrates that quantitative diversification of a common regulatory network is indeed demonstrably sufficient to generate the phenotypic differences observed across three major species within the Caenorhabditis genus. Using our computational framework, we systematically identify the quantitative changes that may have occurred in the regulatory network during the evolution of these species. Our model predictions show that significant phenotypic diversity may be sampled through quantitative variations in the regulatory network without overhauling the core network architecture. Furthermore, by comparing the predicted landscape of phenotypes to multicellular patterns that have been experimentally observed across multiple species, we systematically trace the quantitative regulatory changes that may have occurred during the evolution of the Caenorhabditis genus