Jeffrey D. Varner

A review of the mammalian unfolded protein response.

Proteins requiring post‐translational modifications such as N‐linked glycosylation are processed in the endoplasmic reticulum (ER). A diverse array of cellular stresses can lead to dysfunction of the ER and ultimately to an imbalance between protein‐folding capacity and protein‐folding load. Cells monitor protein folding by an inbuilt quality control system involving both the ER and the Golgi apparatus. Unfolded or misfolded proteins are tagged for degradation via ER‐associated degradation (ERAD) or sent back through the folding cycle. Continued accumulation of incorrectly folded proteins can also trigger the unfolded protein response (UPR). In mammalian cells, UPR is a complex signaling program mediated by three ER transmembrane receptors: activating transcription factor 6 (ATF6), inositol requiring kinase 1 (IRE1) and double‐stranded RNA‐activated protein kinase (PKR)‐like endoplasmic reticulum kinase (PERK). UPR performs three functions, adaptation, alarm, and apoptosis. During adaptation, the UPR tries to reestablish folding homeostasis by inducing the expression of chaperones that enhance protein folding. Simultaneously, global translation is attenuated to reduce the ER folding load while the degradation rate of unfolded proteins is increased. If these steps fail, the UPR induces a cellular alarm and mitochondrial mediated apoptosis program. UPR malfunctions have been associated with a wide range of disease states including tumor progression, diabetes, as well as immune and inflammatory disorders. This review describes recent advances in understanding the molecular structure of UPR in mammalian cells, its functional role in cellular stress, and its pathophysiology. Biotechnol. Bioeng. 2011;108: 2777–2793.

Metabolic Engineering from a Cybernetic Perspective. 1. Theoretical Preliminaries

The theoretical basis of a cybernetic metabolic network design and analysis framework, which has been subsequently successfully applied to predict system response to genetic alteration, is presented. This conceptual methodology consists of three main branches, namely, a model realization framework, a representation of genetic alteration, and lastly, a metabolic design component. These concepts are introduced as a series of postulates that describe the basic tenets of the approach. Each branch is discussed in turn, starting with the cybernetic representation of arbitrarily complex metabolic networks. A set of postulates is put forth that affords the modular construction of cybernetic models of metabolic networks using as a base a library of elementary pathways. This is followed by a discussion of the representation of genetic alterations within the cybernetic framework. It is postulated that the objective of the base network and the altered system are identical (at least on the time scale required for the organism to “learn” new objectives). This implies, with respect to resource allocation, that the base network and its genetically altered counterpart may still be treated as optimal systems; however, the set of competing physiological choices open to the altered network expands or contracts depending upon the nature of the genetic perturbation. Lastly, to add a predictive design aspect to the methodology, we present a set of postulates that outline the application of metabolic control analysis to cybernetic model systems. We postulate that sensitivity coefficients computed from a cybernetic model, although still local in scope, have the added benefit of a systematic representation of regulatory function as described by the cybernetic variables. Thus, information gained from sensitivity measurements stemming from a cybernetic model include the explicit input of metabolic regulation, a component that is lacking in a purely kinetic representation of metabolic function. The sensitivity results can then be employed to develop qualitative strategies for the rational alteration of metabolic function, which can be evaluated by simulation of an appropriately modified cybernetic model of the base network.

Multiscale Models of Breast Cancer Progression

Breast cancer initiation, invasion and metastasis span multiple length and time scales. Molecular events at short length scales lead to an initial tumorigenic population, which left unchecked by immune action, acts at increasingly longer length scales until eventually the cancer cells escape from the primary tumor site. This series of events is highly complex, involving multiple cell types interacting with (and shaping) the microenvironment. Multiscale mathematical models have emerged as a powerful tool to quantitatively integrate the convective-diffusion-reaction processes occurring on the systemic scale, with the molecular signaling processes occurring on the cellular and subcellular scales. In this study, we reviewed the current state of the art in cancer modeling across multiple length scales, with an emphasis on the integration of intracellular signal transduction models with pro-tumorigenic chemical and mechanical microenvironmental cues. First, we reviewed the underlying biomolecular origin of breast cancer, with a special emphasis on angiogenesis. Then, we summarized the development of tissue engineering platforms which could provide high-fidelity ex vivo experimental models to identify and validate multiscale simulations. Lastly, we reviewed top-down and bottom-up multiscale strategies that integrate subcellular networks with the microenvironment. We present models of a variety of cancers, in addition to breast cancer specific models. Taken together, we expect as the sophistication of the simulations increase, that multiscale modeling and bottom-up agent-based models in particular will become an increasingly important platform technology for basic scientific discovery, as well as the identification and validation of potentially novel therapeutic targets.

Physicochemical regulation of endothelial sprouting in a 3D microfluidic angiogenesis model

Both physiological and pathological tissue remodeling (e.g., during wound healing and cancer, respectively) require new blood vessel formation via angiogenesis, but the underlying microenvironmental mechanisms remain poorly defined due in part to the lack of biologically relevant in vitro models. Here, we present a biomaterials-based microfluidic 3D platform for analysis of endothelial sprouting in response to morphogen gradients. This system consists of three lithographically defined channels embedded in type I collagen hydrogels. A central channel is coated with endothelial cells, and two parallel side channels serve as a source and a sink for the steady-state generation of biochemical gradients. Gradients of vascular endothelial growth factor (VEGF) promoted sprouting, whereby endothelial cell responsiveness was markedly dependent on cell density and vessel geometry regardless of treatment conditions. These results point toward mechanical and/or autocrine mechanisms that may overwhelm pro-angiogenic paracrine signaling under certain conditions. To date, neither geometrical effects nor cell density have been considered critical determinants of angiogenesis in health and disease. This biomimetic vessel platform demonstrated utility for delineating hitherto underappreciated contributors of angiogenesis, and future studies may enable important new mechanistic insights that will inform anti-angiogenic cancer therapy.

Application of cybernetic models to metabolic engineering: Investigation of storage pathways

A cybernetic model is proposed to examine generic features of storage pathways. This model is capable of describing synthesis of carbon and non-carbon storage polymers. The effect of environmental conditions is evaluated using storage polymer level as a fraction of total biomass as a gauge of pathway performance. The base wild-type pathway is then analyzed to determine the effect of genetic alterations upon system performance. Proposed modifications are tested using the cybernetic model as a diagnostic tool to ascertain the ramifications of potential genetic alterations. A methodology is developed within the cybernetic framework to describe alterations of enzyme activity and over-expression of pathway enzymes. Copyright 1998 John Wiley & Sons, Inc.

Metabolic Engineering from a Cybernetic Perspective. 2. Qualitative Investigation of Nodal Architechtures and Their Response to Genetic Perturbation

A cybernetic representation of the branch point development of Stephanopoulos and Vallino is formulated. The model systems are employed to translate the qualitative properties of the nodal control architectures characterized by Stephanopoulos and Vallino into a mathematical context. It is shown that a cybernetic model in which the objective is the independent maximization of the levels of branch point products is consistent with the characterization of a flexible node. In contrast, the rigid control architecture is shown to be equivalent to the maximization of the mathematical product of the branch point products. It has been demonstrated subsequently that cybernetic metabolic network models are capable of predicting the system response to enzymatic amplification. However, given the complicated nature of the subsequent models, a clear illustration of the basic mechanism by which such predictions are manifested is not forthwith. Thus, a second objective of the present work is the examination of the response of the flexible and rigid control architectures to genetic perturbation, specifically enzymatic overexpression, with the expressed aim of elucidating the mechanism by which a cybernetic model predicts metabolic network responsiveness. It is shown that the ramifications of genetic perturbation are transmitted through the cybernetic representation of a metabolic network via the resource allocation structure which acts as the conduit by which regulatory signals are transmitted to seemingly unconnected portions of the network. It is postulated that enzymatic overexpression under an artificial promoter represents, from the perspective of the microorganism, an uncontrollable resource drain that forces the metabolic network control architecture to reevaluate the standing resource allocation policy as implemented via the cybernetic control variables. In biological terms, the reevaluation of allocation policy implies a shift in the level and activity of network enzymes yielding, in some cases, qualitatively different network function. It is our position that, conceptually, this is equivalent to the conventional wisdom that genetic manipulation of a metabolic network is the impetus for shifts in the network functionality, i.e., enzyme levels as well as activity. Thus, this development provides a necessary intellectual precursor for the formulation and analysis of the model systems that follow.

Analysis of the molecular networks in androgen dependent and independent prostate cancer revealed fragile and robust subsystems.

Androgen ablation therapy is currently the primary treatment for metastatic prostate cancer. Unfortunately, in nearly all cases, androgen ablation fails to permanently arrest cancer progression. As androgens like testosterone are withdrawn, prostate cancer cells lose their androgen sensitivity and begin to proliferate without hormone growth factors. In this study, we constructed and analyzed a mathematical model of the integration between hormone growth factor signaling, androgen receptor activation, and the expression of cyclin D and Prostate-Specific Antigen in human LNCaP prostate adenocarcinoma cells. The objective of the study was to investigate which signaling systems were important in the loss of androgen dependence. The model was formulated as a set of ordinary differential equations which described 212 species and 384 interactions, including both the mRNA and protein levels for key species. An ensemble approach was chosen to constrain model parameters and to estimate the impact of parametric uncertainty on model predictions. Model parameters were identified using 14 steady-state and dynamic LNCaP data sets taken from literature sources. Alterations in the rate of Prostatic Acid Phosphatase expression was sufficient to capture varying levels of androgen dependence. Analysis of the model provided insight into the importance of network components as a function of androgen dependence. The importance of androgen receptor availability and the MAPK/Akt signaling axes was independent of androgen status. Interestingly, androgen receptor availability was important even in androgen-independent LNCaP cells. Translation became progressively more important in androgen-independent LNCaP cells. Further analysis suggested a positive synergy between the MAPK and Akt signaling axes and the translation of key proliferative markers like cyclin D in androgen-independent cells. Taken together, the results support the targeting of both the Akt and MAPK pathways. Moreover, the analysis suggested that direct targeting of the translational machinery, specifically eIF4E, could be efficacious in androgen-independent prostate cancers.

A Test of Highly Optimized Tolerance Reveals Fragile Cell-Cycle Mechanisms Are Molecular Targets in Clinical Cancer Trials

Robustness, a long-recognized property of living systems, allows function in the face of uncertainty while fragility, i.e., extreme sensitivity, can potentially lead to catastrophic failure following seemingly innocuous perturbations. Carlson and Doyle hypothesized that highly-evolved networks, e.g., those involved in cell-cycle regulation, can be resistant to some perturbations while highly sensitive to others. The “robust yet fragile” duality of networks has been termed Highly Optimized Tolerance (HOT) and has been the basis of new lines of inquiry in computational and experimental biology. In this study, we tested the working hypothesis that cell-cycle control architectures obey the HOT paradigm. Three cell-cycle models were analyzed using monte-carlo sensitivity analysis. Overall state sensitivity coefficients, which quantify the robustness or fragility of a given mechanism, were calculated using a monte-carlo strategy with three different numerical techniques along with multiple parameter perturbation strategies to control for possible numerical and sampling artifacts. Approximately 65% of the mechanisms in the G1/S restriction point were responsible for 95% of the sensitivity, conversely, the G2-DNA damage checkpoint showed a much stronger dependence on a few mechanisms; ∼32% or 13 of 40 mechanisms accounted for 95% of the sensitivity. Our analysis predicted that CDC25 and cyclin E mechanisms were strongly implicated in G1/S malfunctions, while fragility in the G2/M checkpoint was predicted to be associated with the regulation of the cyclin B-CDK1 complex. Analysis of a third model containing both G1/S and G2/M checkpoint logic, predicted in addition to mechanisms already mentioned, that translation and programmed proteolysis were also key fragile subsystems. Comparison of the predicted fragile mechanisms with literature and current preclinical and clinical trials suggested a strong correlation between efficacy and fragility. Thus, when taken together, these results support the working hypothesis that cell-cycle control architectures are HOT networks and establish the mathematical estimation and subsequent therapeutic exploitation of fragile mechanisms as a novel strategy for anti-cancer lead generation.

Ubiquibodies: Synthetic E3 Ubiquitin Ligases Endowed with Unnatural Substrate Specificity for Targeted Protein Silencing

Background: Techniques that harness the power of the ubiquitin-proteasome pathway (UPP) for protein knock-out are limited to a narrow set of protein targets. Results: Engineered “ubiquibodies” specifically and systematically removed exogenous target proteins. Conclusion: Diverse protein targets can be redirected to the UPP using this new protein silencing method. Significance: Ubiquibodies offer a simple, reproducible, and customizable technique for selectively and controllably depleting cellular proteins. The ubiquitin-proteasome pathway (UPP) is the main route of protein degradation in eukaryotic cells and is a common mechanism through which numerous cellular pathways are regulated. To date, several reverse genetics techniques have been reported that harness the power of the UPP for selectively reducing the levels of otherwise stable proteins. However, each of these approaches has been narrowly developed for a single substrate and cannot be easily extended to other protein substrates of interest. To address this shortcoming, we created a generalizable protein knock-out method by engineering protein chimeras called “ubiquibodies” that combine the activity of E3 ubiquitin ligases with designer binding proteins to steer virtually any protein to the UPP for degradation. Specifically, we reprogrammed the substrate specificity of a modular human E3 ubiquitin ligase called CHIP (carboxyl terminus of Hsc70-interacting protein) by replacing its natural substrate-binding domain with a single-chain Fv (scFv) intrabody or a fibronectin type III domain monobody that target their respective antigens with high specificity and affinity. Engineered ubiquibodies reliably transferred ubiquitin to surface exposed lysines on target proteins and even catalyzed the formation of biologically relevant polyubiquitin chains. Following ectopic expression of ubiquibodies in mammalian cells, specific and systematic depletion of desired target proteins was achieved, whereas the levels of a natural substrate of CHIP were unaffected. Taken together, engineered ubiquibodies offer a simple, reproducible, and customizable means for directly removing specific cellular proteins through accelerated proteolysis.

Computational Modeling and Analysis of Insulin Induced Eukaryotic Translation Initiation

Insulin, the primary hormone regulating the level of glucose in the bloodstream, modulates a variety of cellular and enzymatic processes in normal and diseased cells. Insulin signals are processed by a complex network of biochemical interactions which ultimately induce gene expression programs or other processes such as translation initiation. Surprisingly, despite the wealth of literature on insulin signaling, the relative importance of the components linking insulin with translation initiation remains unclear. We addressed this question by developing and interrogating a family of mathematical models of insulin induced translation initiation. The insulin network was modeled using mass-action kinetics within an ordinary differential equation (ODE) framework. A family of model parameters was estimated, starting from an initial best fit parameter set, using 24 experimental data sets taken from literature. The residual between model simulations and each of the experimental constraints were simultaneously minimized using multiobjective optimization. Interrogation of the model population, using sensitivity and robustness analysis, identified an insulin-dependent switch that controlled translation initiation. Our analysis suggested that without insulin, a balance between the pro-initiation activity of the GTP-binding protein Rheb and anti-initiation activity of PTEN controlled basal initiation. On the other hand, in the presence of insulin a combination of PI3K and Rheb activity controlled inducible initiation, where PI3K was only critical in the presence of insulin. Other well known regulatory mechanisms governing insulin action, for example IRS-1 negative feedback, modulated the relative importance of PI3K and Rheb but did not fundamentally change the signal flow.