Challenges and promises in modeling dermatologic disorders with bioengineered skin.

The tremendous cost of drug development is often attributed to the long time interval between identifying lead compounds in preclinical studies to assessing clinical efficacy in randomized clinical trials. Many candidate molecules show promise in cell culture or animal models, only to fail in late stage in human investigations. There is a need for novel technologies that allow investigators to quickly and reliably predict drug safety and efficacy. The advent of microtechnology has made it possible to integrate multiple microphysiologic organ systems into a single microfabricated chip. This review focuses on three-dimensional engineered skin, which has enjoyed a long history of uses both in clinical treatments of refractory ulcers and as a laboratory model. We discuss current biological and engineering challenges in construction of a robust bioengineered skin and provide a blueprint for its potential utility to model dermatologic disorders such as psoriasis or cutaneous drug reactions.

TREM-1 as a potential therapeutic target in psoriasis.

Our group recently described a population of antigen-presenting cells that appear to be critical in psoriasis pathogenesis, termed inflammatory myeloid dendritic cells (CD11c(+)/blood dendritic cell (DC) antigen 1(-)). Triggering receptor expressed on myeloid cells type-1 (TREM-1) signaling was a major canonical pathway in the published transcriptome of these cells. TREM-1 is a member of the Ig superfamily, active through the DAP12 signaling pathway, with an unknown ligand. Activation through TREM-1 induces inflammatory cytokines, including IL-8, MCP/CCL2, and tumor necrosis factor. We now show that TREM-1 was expressed in the skin of healthy and psoriatic patients, and there was increased soluble TREM-1 in the circulation of psoriasis patients. In psoriasis lesions, TREM-1 was colocalized with DCs, as well as CD31(+) endothelial cells. TREM-1 expression was reduced with successful narrow band UVB (NB-UVB), etanercept, and anti-IL-17 treatments. An in vitro model of peptidoglycan-activated monocytes as inflammatory myeloid DCs was developed to study TREM-1 blockade, and treatment with a TREM-1 blocking chimera decreased allogeneic T-helper type 17 cell activation, as well as IL-17 production. Furthermore, TREM-1 blockade of ex vivo psoriatic DCs in an allogeneic mixed leukocyte reaction also showed a decrease in IL-17. Together, these data suggest that the TREM-1 signaling pathway may be a previously unidentified therapeutic target to prevent the effects of inflammatory myeloid DCs in psoriasis.

Compartment-specific feedback loop and regulated trafficking can result in sustained activation of Ras at the Golgi.

Imaging experiments have shown that cell signaling components such as Ras can be activated by growth factors at distinct subcellular locations. Trafficking between these subcellular locations is a regulated dynamic process. The effects of trafficking and the molecular mechanisms underlying compartment-specific Ras activation were studied using numerical simulations of an ordinary differential equation-based multi-compartment model. The simulations show that interplay between two distinct mechanisms, a palmitoylation cycle that controls Ras trafficking and a phospholipase C-epsilon (PLC-epsilon) driven feedback loop, can convert a transient calcium signal into prolonged Ras activation at the Golgi. Detailed analysis of the network identified PLC-epsilon as a key determinant of "compartment switching". Modulation of PLC-epsilon activity switches the location of activated Ras between the plasma membrane and Golgi through a new mechanism termed "kinetic scaffolding". These simulations indicate that multiple biochemical mechanisms, when appropriately coupled, can give rise to an intracellular compartment-specific sustained Ras activation in response to stimulation of growth factor receptors at the plasma membrane.

Formation of regulatory patterns during signal propagation in a Mammalian cellular network.

We developed a model of 545 components (nodes) and 1259 interactions representing signaling pathways and cellular machines in the hippocampal CA1 neuron. Using graph theory methods, we analyzed ligand-induced signal flow through the system. Specification of input and output nodes allowed us to identify functional modules. Networking resulted in the emergence of regulatory motifs, such as positive and negative feedback and feedforward loops, that process information. Key regulators of plasticity were highly connected nodes required for the formation of regulatory motifs, indicating the potential importance of such motifs in determining cellular choices between homeostasis and plasticity.

Cannabinoid receptor-induced neurite outgrowth is mediated by Rap1 activation through G(alpha)o/i-triggered proteasomal degradation of Rap1GAPII.

The G(alpha)o/i-coupled CB1 cannabionoid receptor induces neurite outgrowth in Neuro-2A cells. The mechanisms of signaling through G(alpha)o/i to induce neurite outgrowth were studied. The expression of G(alpha)o/i reduces the stability of its direct interactor protein, Rap1GAPII, by targeting it for ubiquitination and proteasomal degradation. This results in the activation of Rap1. G(alpha)o/i-induced activation of endogenous Rap1 in Neuro-2A cells is blocked by the proteasomal inhibitor lactacystin. G(alpha)o/i stimulates neurite outgrowth that is blocked by the expression of dominant negative Rap1. Expression of Rap1GAPII also blocks the G(alpha)o/i-induced neurite outgrowth and treatment with proteasomal inhibitors potentiates this inhibition. The endogenous G(alpha)o/i-coupled cannabinoid (CB1) receptor in Neuro-2A cells stimulates the degradation of Rap1GAPII; activation of Rap1 and treatment with pertussis toxin or lactacystin blocks these effects. The CB1 receptor-stimulated neurite outgrowth is blocked by treatment with pertussis toxin, small interfering RNA for Rap, lactacystin, and expression of Rap1GAPII. Thus, the G(alpha)o/i-coupled cannabinoid receptor, by regulating the proteasomal degradation of Rap1GAPII, activates Rap1 to induce neurite outgrowth.

Computational approaches for modeling regulatory cellular networks.

Cellular components interact with each other to form networks that process information and evoke biological responses. A deep understanding of the behavior of these networks requires the development and analysis of mathematical models. In this article, different types of mathematical representations for modeling signaling networks are described, and the advantages and disadvantages of each type are discussed. Two experimentally well-studied signaling networks are then used as examples to illustrate the insight that could be gained through modeling. Finally, the modeling approach is expanded to describe how signaling networks might regulate cellular machines and evoke phenotypic behaviors.

Modeling cell signaling networks.

Cell signaling pathways interact with one another to form networks in mammalian systems. Such networks are complex in their organization and exhibit emergent properties such as bistability and ultrasensitivity. Analysis of signaling networks requires a combination of experimental and theoretical approaches including the development and analysis of models. This review focuses on theoretical approaches to understanding cell signaling networks. Using heterotrimeric G protein pathways an example, we demonstrate how interactions between two pathways can result in a network that contains a positive feedback loop and function as a switch. Different mathematical approaches that are currently used to model signaling networks are described, and future challenges including the need for databases as well as enhanced computing environments are discussed.

The force-velocity relationship for the actin-based motility of Listeria monocytogenes.

The intracellular movement of the bacterial pathogen Listeria monocytogenes has helped identify key molecular constituents of actin-based motility (recent reviews ). However, biophysical as well as biochemical data are required to understand how these molecules generate the forces that extrude eukaryotic membranes. For molecular motors and for muscle, force-velocity curves have provided key biophysical data to distinguish between mechanistic theories. Here we manipulate and measure the viscoelastic properties of tissue extracts to provide the first force-velocity curve for Listeria monocytogenes. We find that the force-velocity relationship is highly curved, almost biphasic, suggesting a high cooperativity between biochemical catalysis and force generation. Using high-resolution motion tracking in low-noise extracts, we find long trajectories composed exclusively of molecular-sized steps. Robust statistics from these trajectories show a correlation between the duration of steps and macroscopic Listeria speed, but not between average step size and speed. Collectively, our data indicate how the molecular properties of the Listeria polymerization engine regulate speed, and that regulation occurs during molecular-scale pauses.