Engineering orthogonal human O-linked glycoprotein biosynthesis in bacteria.

A major objective of synthetic glycobiology is to re-engineer existing cellular glycosylation pathways from the top down or construct non-natural ones from the bottom up for new and useful purposes. Here, we have developed a set of orthogonal pathways for eukaryotic O-linked protein glycosylation in Escherichia coli that installed the cancer-associated mucin-type glycans Tn, T, sialyl-Tn and sialyl-T onto serine residues in acceptor motifs derived from different human O-glycoproteins. These same glycoengineered bacteria were used to supply crude cell extracts enriched with glycosylation machinery that permitted cell-free construction of O-glycoproteins in a one-pot reaction. In addition, O-glycosylation-competent bacteria were able to generate an antigenically authentic Tn-MUC1 glycoform that exhibited reactivity with antibody 5E5, which specifically recognizes cancer-associated glycoforms of MUC1. We anticipate that the orthogonal glycoprotein biosynthesis pathways developed here will provide facile access to structurally diverse O-glycoforms for a range of important scientific and therapeutic applications.

Multi-scale computational study of the Warburg effect, reverse Warburg effect and glutamine addiction in solid tumors.

Cancer metabolism has received renewed interest as a potential target for cancer therapy. In this study, we use a multi-scale modeling approach to interrogate the implications of three metabolic scenarios of potential clinical relevance: the Warburg effect, the reverse Warburg effect and glutamine addiction. At the intracellular level, we construct a network of central metabolism and perform flux balance analysis (FBA) to estimate metabolic fluxes; at the cellular level, we exploit this metabolic network to calculate parameters for a coarse-grained description of cellular growth kinetics; and at the multicellular level, we incorporate these kinetic schemes into the cellular automata of an agent-based model (ABM), iDynoMiCS. This ABM evaluates the reaction-diffusion of the metabolites, cellular division and motion over a simulation domain. Our multi-scale simulations suggest that the Warburg effect provides a growth advantage to the tumor cells under resource limitation. However, we identify a non-monotonic dependence of growth rate on the strength of glycolytic pathway. On the other hand, the reverse Warburg scenario provides an initial growth advantage in tumors that originate deeper in the tissue. The metabolic profile of stromal cells considered in this scenario allows more oxygen to reach the tumor cells in the deeper tissue and thus promotes tumor growth at earlier stages. Lastly, we suggest that glutamine addiction does not confer a selective advantage to tumor growth with glutamine acting as a carbon source in the tricarboxylic acid (TCA) cycle, any advantage of glutamine uptake must come through other pathways not included in our model (e.g., as a nitrogen donor). Our analysis illustrates the importance of accounting explicitly for spatial and temporal evolution of tumor microenvironment in the interpretation of metabolic scenarios and hence provides a basis for further studies, including evaluation of specific therapeutic strategies that target metabolism.

Analysis of the role of thrombomodulin in all-trans retinoic acid treatment of coagulation disorders in cancer patients.

BACKGROUND: Clinical studies have shown that all-trans retinoic acid (RA), which is often used in treatment of cancer patients, improves hemostatic parameters and bleeding complications such as disseminated intravascular coagulation (DIC). However, the mechanisms underlying this improvement have yet to be elucidated. In vitro studies have reported that RA upregulates thrombomodulin (TM) expression on the endothelial cell surface. The objective of this study was to investigate how and to what extent the TM concentration changes after RA treatment in cancer patients, and how this variation influences the blood coagulation cascade. RESULTS: In this study, we introduced an ordinary differential equation (ODE) model of gene expression for the RA-induced upregulation of TM concentration. Coupling the gene expression model with a two-compartment pharmacokinetic model of RA, we obtained the time-dependent changes in TM and thrombomodulin-mRNA (TMR) concentrations following oral administration of RA. Our results indicated that the TM concentration reached its peak level almost 14 h after taking a single oral dose (110 [Formula: see text]) of RA. Continuous treatment with RA resulted in oscillatory expression of TM on the endothelial cell surface. We then coupled the gene expression model with a mechanistic model of the coagulation cascade, and showed that the elevated levels of TM over the course of RA therapy with a single daily oral dose (110 [Formula: see text]) of RA, reduced the peak thrombin levels and endogenous thrombin potential (ETP) up to 50 and 49%, respectively. We showed that progressive reductions in plasma levels of RA, observed in continuous RA therapy with a once-daily oral dose (110 [Formula: see text]) of RA, did not affect TM-mediated reduction of thrombin generation significantly. This finding prompts the hypothesis that continuous RA treatment has more consistent therapeutic effects on coagulation disorders than on cancer. CONCLUSIONS: Our results indicate that the oscillatory upregulation of TM expression on the endothelial cells over the course of RA therapy could potentially contribute to the treatment of coagulation abnormalities in cancer patients. Further studies on the impacts of RA therapy on the procoagulant activity of cancer cells are needed to better elucidate the mechanisms by which RA therapy improves hemostatic abnormalities in cancer.

Population Heterogeneity in the Epithelial to Mesenchymal Transition Is Controlled by NFAT and Phosphorylated Sp1.

Epithelial to mesenchymal transition (EMT) is an essential differentiation program during tissue morphogenesis and remodeling. EMT is induced by soluble transforming growth factor ß (TGF-ß) family members, and restricted by vascular endothelial growth factor family members. While many downstream molecular regulators of EMT have been identified, these have been largely evaluated individually without considering potential crosstalk. In this study, we created an ensemble of dynamic mathematical models describing TGF-ß induced EMT to better understand the operational hierarchy of this complex molecular program. We used ordinary differential equations (ODEs) to describe the transcriptional and post-translational regulatory events driving EMT. Model parameters were estimated from multiple data sets using multiobjective optimization, in combination with cross-validation. TGF-ß exposure drove the model population toward a mesenchymal phenotype, while an epithelial phenotype was enhanced following vascular endothelial growth factor A (VEGF-A) exposure. Simulations predicted that the transcription factors phosphorylated SP1 and NFAT were master regulators promoting or inhibiting EMT, respectively. Surprisingly, simulations also predicted that a cellular population could exhibit phenotypic heterogeneity (characterized by a significant fraction of the population with both high epithelial and mesenchymal marker expression) if treated simultaneously with TGF-ß and VEGF-A. We tested this prediction experimentally in both MCF10A and DLD1 cells and found that upwards of 45% of the cellular population acquired this hybrid state in the presence of both TGF-ß and VEGF-A. We experimentally validated the predicted NFAT/Sp1 signaling axis for each phenotype response. Lastly, we found that cells in the hybrid state had significantly different functional behavior when compared to VEGF-A or TGF-ß treatment alone. Together, these results establish a predictive mechanistic model of EMT susceptibility, and potentially reveal a novel signaling axis which regulates carcinoma progression through an EMT versus tubulogenesis response.

Ubiquibodies, synthetic E3 ubiquitin ligases endowed with unnatural substrate specificity for targeted protein silencing.

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.

Integrated Constraint-Based Modeling of E. coli Cell-Free Protein Synthesis.

Cell-free protein expression has become a widely used research tool in systems and synthetic biology and a promising technology for protein biomanufacturing. Cell-free protein synthesis relies on in-vitro transcription and translation processes to produce a protein of interest. However, transcription and translation depend upon the operation of complex metabolic pathways for precursor and energy regeneration. Toward understanding the role of metabolism in a cell-free system, we developed a dynamic constraint-based simulation of protein production in the myTXTL E. coli cell-free system with and without electron transport chain inhibitors. Time-resolved absolute metabolite measurements for â"³ = 63 metabolites, along with absolute concentration measurements of the mRNA and protein abundance and measurements of enzyme activity, were integrated with kinetic and enzyme abundance information to simulate the time evolution of metabolic flux and protein production with and without inhibitors. The metabolic flux distribution estimated by the model, along with the experimental metabolite and enzyme activity data, suggested that the myTXTL cell-free system has an active central carbon metabolism with glutamate powering the TCA cycle. Further, the electron transport chain inhibitor studies suggested the presence of oxidative phosphorylation activity in the myTXTL cell-free system; the oxidative phosphorylation inhibitors provided biochemical evidence that myTXTL relied, at least partially, on oxidative phosphorylation to generate the energy required to sustain transcription and translation for a 16-hour batch reaction.

Convergent Approaches to Delineate the Metabolic Regulation of Tumor Invasion by Hyaluronic Acid Biosynthesis.

Metastasis is the leading cause of breast cancer-related deaths and is often driven by invasion and cancer-stem like cells (CSCs). Both the CSC phenotype and invasion are associated with increased hyaluronic acid (HA) production. How these independent observations are connected, and which role metabolism plays in this process, remains unclear due to the lack of convergent approaches integrating engineered model systems, computational tools, and cancer biology. Using microfluidic invasion models, metabolomics, computational flux balance analysis, and bioinformatic analysis of patient data, the functional links between the stem-like, invasive, and metabolic phenotype of breast cancer cells as a function of HA biosynthesis are investigated. These results suggest that CSCs are more invasive than non-CSCs and that broad metabolic changes caused by overproduction of HA play a role in this process. Accordingly, overexpression of hyaluronic acid synthases (HAS) 2 or 3 induces a metabolic phenotype that promotes cancer cell stemness and invasion in vitro and upregulates a transcriptomic signature predictive of increased invasion and worse patient survival. This study suggests that HA overproduction leads to metabolic adaptations to satisfy the energy demands for 3D invasion of breast CSCs highlighting the importance of engineered model systems and multidisciplinary approaches in cancer research.

Endothelial cells metabolically regulate breast cancer invasion toward a microvessel.

Breast cancer metastasis is initiated by invasion of tumor cells into the collagen type I-rich stroma to reach adjacent blood vessels. Prior work has identified that metabolic plasticity is a key requirement of tumor cell invasion into collagen. However, it remains largely unclear how blood vessels affect this relationship. Here, we developed a microfluidic platform to analyze how tumor cells invade collagen in the presence and absence of a microvascular channel. We demonstrate that endothelial cells secrete pro-migratory factors that direct tumor cell invasion toward the microvessel. Analysis of tumor cell metabolism using metabolic imaging, metabolomics, and computational flux balance analysis revealed that these changes are accompanied by increased rates of glycolysis and oxygen consumption caused by broad alterations of glucose metabolism. Indeed, restricting glucose availability decreased endothelial cell-induced tumor cell invasion. Our results suggest that endothelial cells promote tumor invasion into the stroma due, in part, to reprogramming tumor cell metabolism.

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.

Constructing Cell-Free Expression Systems for Low-Cost Access.

Cell-free systems for gene expression have gained attention as platforms for the facile study of genetic circuits and as highly effective tools for teaching. Despite recent progress, the technology remains inaccessible for many in low- and middle-income countries due to the expensive reagents required for its manufacturing, as well as specialized equipment required for distribution and storage. To address these challenges, we deconstructed processes required for cell-free mixture preparation and developed a set of alternative low-cost strategies for easy production and sharing of extracts. First, we explored the stability of cell-free reactions dried through a low-cost device based on silica beads, as an alternative to commercial automated freeze dryers. Second, we report the positive effect of lactose as an additive for increasing protein synthesis in maltodextrin-based cell-free reactions using either circular or linear DNA templates. The modifications were used to produce active amounts of two high-value reagents: the isothermal polymerase Bst and the restriction enzyme BsaI. Third, we demonstrated the endogenous regeneration of nucleoside triphosphates and synthesis of pyruvate in cell-free systems (CFSs) based on phosphoenol pyruvate (PEP) and maltodextrin (MDX). We exploited this novel finding to demonstrate the use of a cell-free mixture completely free of any exogenous nucleotide triphosphates (NTPs) to generate high yields of sfGFP expression. Together, these modifications can produce desiccated extracts that are 203-424-fold cheaper than commercial versions. These improvements will facilitate wider use of CFS for research and education purposes.

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.

Effective Biophysical Modeling of Cell Free Transcription and Translation Processes.

Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.

Toward a genome scale sequence specific dynamic model of cell-free protein synthesis in Escherichia coli.

In this study, we developed a dynamic mathematical model of E. coli cell-free protein synthesis (CFPS). Model parameters were estimated from a dataset consisting of glucose, organic acids, energy species, amino acids, and protein product, chloramphenicol acetyltransferase (CAT) measurements. The model was successfully trained to simulate these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the performance, e.g., the yield and rates of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity was most sensitive to the oxidative phosphorylation and glycolysis/gluconeogenesis pathways. Amino acid biosynthesis was also important for productivity, while overflow metabolism and TCA cycle affected the overall system state. In addition, translation was more important to productivity than transcription. Finally, CAT production was robust to allosteric control, as were most of the predicted metabolite concentrations; the exceptions to this were the concentrations of succinate and malate, and to a lesser extent pyruvate and acetate, which varied from the measured values when allosteric control was removed. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free E. coli system, and could provide a foundation for genome scale, dynamic modeling of cell-free E. coli protein synthesis.

Engineering the spatial organization of metabolic enzymes: mimicking nature's synergy.

A growing body of evidence indicates that many cellular reactions within metabolic pathways are catalyzed not by free-floating 'soluble' enzymes, but via one or more membrane-associated multienzyme complexes. This type of macromolecular organization has important implications for the overall efficiency, specificity, and regulation of metabolic pathways. An ever-increasing number of biochemical and genetic studies on primary and secondary metabolism have laid a solid foundation for this model, providing compelling evidence in favor of the so-called channeling of intermediates between enzyme active sites and colocalization of enzymes inside a cell. In this review, we discuss several of nature's most notable multifunctional enzyme systems including the AROM complex and tryptophan synthase, each of which provides new fundamental insights into the structural organization of metabolic machinery within living cells. We then focus on the growing body of literature related to engineering strategies using protein chimeras and post-translational assembly mechanisms. Common among these techniques is the desire to mimic natural enzyme organization for optimizing the production of valuable metabolites with industrial and medical importance.

Absolute Quantification of Cell-Free Protein Synthesis Metabolism by Reversed-Phase Liquid Chromatography-Mass Spectrometry.

Cell-free protein synthesis (CFPS) is an emerging technology in systems and synthetic biology for the in vitro production of proteins. However, if CFPS is going to move beyond the laboratory and become a widespread and standard just in time manufacturing technology, we must understand the performance limits of these systems. Toward this question, we developed a robust protocol to quantify 40 compounds involved in glycolysis, the pentose phosphate pathway, the tricarboxylic acid cycle, energy metabolism and cofactor regeneration in CFPS reactions. The method uses internal standards tagged with 13C-aniline, while compounds in the sample are derivatized with 12C-aniline. The internal standards and sample were mixed and analyzed by reversed-phase liquid chromatography-mass spectrometry (LC/MS). The co-elution of compounds eliminated ion suppression, allowing the accurate quantification of metabolite concentrations over 2-3 orders of magnitude where the average correlation coefficient was 0.988. Five of the forty compounds were untagged with aniline, however, they were still detected in the CFPS sample and quantified with a standard curve method. The chromatographic run takes approximately 10 min to complete. Taken together, we developed a fast, robust method to separate and accurately quantify 40 compounds involved in CFPS in a single LC/MS run. The method is a comprehensive and accurate approach to characterize cell-free metabolism, so that ultimately, we can understand and improve the yield, productivity and energy efficiency of cell-free systems.

Improving designer glycan production in Escherichia coli through model-guided metabolic engineering.

Asparagine-linked (N-linked) glycosylation is the most common protein modification in eukaryotes, affecting over two-thirds of the proteome. Glycosylation is also critical to the pharmacokinetic activity and immunogenicity of many therapeutic proteins currently produced in complex eukaryotic hosts. The discovery of a protein glycosylation pathway in the pathogen Campylobacter jejuni and its subsequent transfer into laboratory strains of Escherichia coli has spurred great interest in glycoprotein production in prokaryotes. However, prokaryotic glycoprotein production has several drawbacks, including insufficient availability of non-native glycan precursors. To address this limitation, we used a constraint-based model of E. coli metabolism in combination with heuristic optimization to design gene knockout strains that overproduced glycan precursors. First, we incorporated reactions associated with C. jejuni glycan assembly into a genome-scale model of E. coli metabolism. We then identified gene knockout strains that coupled optimal growth to glycan synthesis. Simulations suggested that these growth-coupled glycan overproducing strains had metabolic imbalances that rerouted flux toward glycan precursor synthesis. We then validated the model-identified knockout strains experimentally by measuring glycan expression using a flow cytometric-based assay involving fluorescent labeling of cell surface-displayed glycans. Overall, this study demonstrates the promising role that metabolic modeling can play in optimizing the performance of a next-generation microbial glycosylation platform.

Retinoic acid and 6-formylindolo(3,2-b)carbazole (FICZ) combination therapy reveals putative targets for enhancing response in non-APL AML.

In non-acute promyelotic leukemia (APL)- non myelocytic leukemia (AML), identification of a signaling signature would predict potentially actionable targets to enhance differentiation effects of all-trans-retinoic acid (RA) and make combination differentiation therapy realizable. Components of such a signaling machine/signalsome found to drive RA-induced differentiation discerned in a FAB M2 cell line/model (HL-60) were further characterized and then compared against AML patient expression profiles. FICZ, known to enhance RA-induced differentiation, was used to experimentally augment signaling for analysis. FRET revealed novel signalsome protein associations: CD38 with pS376SLP76 and caveolin-1 with CD38 and AhR. The signaling molecules driving differentiation in HL-60 cluster in non-APL AML de novo samples, too. Pearson correlation coefficients for this molecular ensemble are nearer 1 in the FAB M2 subtype than in non-APL AML. SLP76 correlation to RXRα and p47phox were conserved in FAB M2 model and patient subtype but not in general non-APL AML. The signalsome ergo identifies potential actionable targets in AML.

Computationally derived points of fragility of a human cascade are consistent with current therapeutic strategies.

The role that mechanistic mathematical modeling and systems biology will play in molecular medicine and clinical development remains uncertain. In this study, mathematical modeling and sensitivity analysis were used to explore the working hypothesis that mechanistic models of human cascades, despite model uncertainty, can be computationally screened for points of fragility, and that these sensitive mechanisms could serve as therapeutic targets. We tested our working hypothesis by screening a model of the well-studied coagulation cascade, developed and validated from literature. The predicted sensitive mechanisms were then compared with the treatment literature. The model, composed of 92 proteins and 148 protein-protein interactions, was validated using 21 published datasets generated from two different quiescent in vitro coagulation models. Simulated platelet activation and thrombin generation profiles in the presence and absence of natural anticoagulants were consistent with measured values, with a mean correlation of 0.87 across all trials. Overall state sensitivity coefficients, which measure the robustness or fragility of a given mechanism, were calculated using a Monte Carlo strategy. In the absence of anticoagulants, fluid and surface phase factor X/activated factor X (fX/FXa) activity and thrombin-mediated platelet activation were found to be fragile, while fIX/FIXa and fVIII/FVIIIa activation and activity were robust. Both anti-fX/FXa and direct thrombin inhibitors are important classes of anticoagulants; for example, anti-fX/FXa inhibitors have FDA approval for the prevention of venous thromboembolism following surgical intervention and as an initial treatment for deep venous thrombosis and pulmonary embolism. Both in vitro and in vivo experimental evidence is reviewed supporting the prediction that fIX/FIXa activity is robust. When taken together, these results support our working hypothesis that computationally derived points of fragility of human relevant cascades could be used as a rational basis for target selection despite model uncertainty.

Src family kinase inhibitor bosutinib enhances retinoic acid-induced differentiation of HL-60 leukemia cells.

The acute promyelocytic leukemia (APL) has been treated with all-trans retinoic acid (RA) for decades. While RA has largely been ineffective in non-APL AML subtypes, co-treatments combining RA and other agents are currently in clinical trials. Using the RA-responsive non-APL AML cell line HL-60, we tested the efficacy of the Src family kinase (SFK) inhibitor bosutinib on RA-induced differentiation. HL-60 has been recently shown to bear fidelity to a subtype of AML that respond to RA. We found that co-treatment with RA and bosutinib enhanced differentiation evidenced by increased CD11b expression, G1/G0 cell cycle arrest, and respiratory burst. Expression of the SFK members Fgr and Lyn was enhanced, while SFK activation was inhibited. Phosphorylation of several sites of c-Raf was increased and expression of AhR and p85 PI3K was enhanced. Expression of c-Cbl and mTOR was decreased. Our study suggests that SFK inhibition enhances RA-induced differentiation and may have therapeutic value in non-APL AML.

Sequence Specific Modeling of E. coli Cell-Free Protein Synthesis.

Cell-free protein synthesis (CFPS) is a widely used research tool in systems and synthetic biology. However, if CFPS is to become a mainstream technology for applications such as point of care manufacturing, we must understand the performance limits and costs of these systems. Toward this question, we used sequence specific constraint based modeling to evaluate the performance of E. coli cell-free protein synthesis. A core E. coli metabolic network, describing glycolysis, the pentose phosphate pathway, energy metabolism, amino acid biosynthesis, and degradation was augmented with sequence specific descriptions of transcription and translation and effective models of promoter function. Model parameters were largely taken from literature; thus the constraint based approach coupled the transcription and translation of the protein product, and the regulation of gene expression, with the availability of metabolic resources using only a limited number of adjustable model parameters. We tested this approach by simulating the expression of two model proteins: chloramphenicol acetyltransferase and dual emission green fluorescent protein, for which we have data sets; we then expanded the simulations to a range of additional proteins. Protein expression simulations were consistent with measurements for a variety of cases. The constraint based simulations confirmed that oxidative phosphorylation was active in the CAT cell-free extract, as without it there was no feasible solution within the experimental constraints of the system. We then compared the metabolism of theoretically optimal and experimentally constrained CFPS reactions, and developed parameter free correlations which could be used to estimate productivity as a function of carbon number and promoter type. Lastly, global sensitivity analysis identified the key metabolic processes that controlled CFPS productivity and energy efficiency. In summary, sequence specific constraint based modeling of CFPS offered a novel means to a priori estimate the performance of a cell-free system, using only a limited number of adjustable parameters. While we modeled the production of a single protein in this study, the approach could easily be extended to multiprotein synthetic circuits, RNA circuits, or the cell-free production of small molecule products.