Search

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., Highlights • Asparagine-linked (N -linked) glycosylation is critical to many therapeutic proteins. • Protein glycosylation pathways have been transfer into prokaryotes such as Escherichia coli. • Prokaryotic glycoprotein production suffers from insufficient glycan precursor availability. • Constraint based modeling identified gene deletions that improved glycan biosynthesis. • Predicted gene knockouts and knockout combinations improved glycan biosynthesis by 3-fold.

Cells monitor protein folding by an inbuilt quality-control system in which incorrectly or misfolded folded proteins are tagged for degradation or sent back through a refolding cycle. However, continued accumulation of incorrectly folded proteins triggers the Unfolded Protein Response (UPR), which attempts to re-establish folding homeostasis or commits the cell to apoptosis. In this study, we developed a family of mechanistic models of the mammalian UPR system. An ensemble of models parameters was estimated by minimizing the difference between simulations and experimental measurements using multiobjective optimization. The ensemble of model parameters was validated using cross-validation. Analysis of the model ensemble suggested the three branches of UPR fired simultaneously. However, the importance of each brach was ranked ordered in time; PERK and IRE1 were more important early, while ATF6 was important later in the response. The activity of all three branches as coordinated by the molecular chaperone BiP. Model analysis suggested that BiP feedback was critical to the overall robustness of the system. Removal of any one branch of BiP feedback, destabilized the other branches. On the other hand, removal of all nodes of BiP feedback increased the overall robustness of the system. Thus, while BiP feedback is crucial to allowing the cell to adapt to small perturbations, it also makes the system fragile and susceptible to manipulation.

Prostate cancer is the most common cancer in men and the second leading cause of cancer related death in the United States. Androgens, such as testosterone, are required for androgen dependent prostate cancer (ADPC) growth. Androgen ablation in combination with radiation or chemotherapy remains the primary non-surgical treatment for ADPC. However, androgen ablation typically fails to permanently arrest cancer progression, often resulting in castration resistant prostate cancer (CRPC). In this study, we analyzed a population of mathematical models that described the integration of androgen and mitogenic signaling in androgen dependent and independent prostate cancer. An ensemble of model parameters was estimated from 43 studies of signaling in androgen dependent and resistant LNCaP cell lines. The model population was then validated by comparing simulations with an additional 33 data sets from LNCaP cell lines and clinical trials. Analysis of the model population suggested that simultaneously targeting the PI3K and MAPK pathways in addition to anti-androgen therapies could be an effective treatment for CRPC. We tested this hypothesis in both ADPC LNCaP cell lines and LNCaP derived CRPC C4-2 cells using three inhibitors: the androgen receptor inhibitor MDV3100 (enzalutamide), the Raf kinase inhibitor sorafenib, and the PI3K inhibitor LY294002. Consistent with model predictions, cell viability decreased at 72 hrs in the dual and triple inhibition cases in both the LNCaP and C4-2 cell lines, compared to treatment with any single inhibitor. Taken together, this study suggested that crosstalk between the androgen and mitogenic signaling axes led to robustness of CRPC to any single inhibitor. Model analysis predicted potentially efficacious target combinations which were confirmed by experimental studies in multiple cell lines, thereby illustrating the potentially important role that mathematical modeling can play in cancer.

Flux balance analysis (FBA) in combination with the decomposition of metabolic networks into elementary modes has provided a route to modeling cellular metabolism. It is dependent, however, on the availability of external fluxes such as substrate uptake or growth rate before estimates can become available of intracellular fluxes. The framework classically does not allow modeling of metabolic regulation or the formulation of dynamic models except through dynamic measurement of external fluxes. The cybernetic modeling approach of Ramkrishna and coworkers provides a dynamic framework for modeling metabolic systems because of its focus on describing regulatory processes based on cybernetic arguments and hence has the capacity to describe both external and internal fluxes. In this article, we explore the alternative of developing hybrid models combining cybernetic models for the external fluxes with the flux balance approach for estimation of the internal fluxes. The approach has the merit of the simplicity of the early cybernetic models and hence computationally facile while also providing detailed information on intracellular fluxes. The hybrid model of this article is based on elementary mode decomposition of the metabolic network. The uptake rates for the various elementary modes are combined using global cybernetic variables based on maximizing substrate uptake rates. Estimation of intracellular metabolism is based on its stoichiometric coupling with the external fluxes under the assumption of (pseudo-) steady state conditions. The set of parameters of the hybrid model was estimated with the aid of nonlinear optimization routine, by fitting simulations with dynamic experimental data on concentrations of biomass, substrate, and fermentation products. The hybrid model estimations were tested with FBA (based on measured substrate uptake rate) for two different metabolic networks (one is a reduced network which fixes ATP contribution to the biomass and maintenance requirement of ATP, and the other network is a more complex network which has a separate reaction for maintenance.) for the same experiment involving anaerobic growth of E. coli GJT001. The hybrid model estimated glucose consumption and all fermentation byproducts to better than 10%. The FBA makes similar estimations of fermentation products, however, with the exception of succinate. The simulation results show that the global cybernetic variables alone can regulate the metabolic reactions obtaining a very satisfactory fit to the measured fermentation byproducts. In view of the hybrid model's ability to predict biomass growth and fermentation byproducts of anaerobic E. coli GJT001, this reduced order model offers a computationally efficient alternative to more detailed models of metabolism and hence useful for the simulation of bioreactors.

The role of JAK signaling in cell cycle transit and maintenance of genomic stability was determined in HL-60 human myeloblastic leukemia cells. We have previously reported that a pan-JAK inhibitor caused ERK-dependent endoreduplication. In the current study we find that JAK inhibition caused nuclear re-localization of RAF-1, which could be inhibited by RAF inhibitor GW5074. GW5074 also inhibited JAK inhibitor-induced appearance of nuclear phosphorylated RAF-1(pS621RAF) and MEK, and it inhibited the JAK inhibitor-induced co-immunoprecipitation of nuclear RAF-1 and MEK. JAK inhibition also increased nuclear BubR1 phosphorylation, which was diminished by RAF inhibitor GW5074. RAF-1 and BubR1 in the nucleus co-immunoprecipitated; and GW5074 eliminated this. Furthermore, inhibiting RAF with GW5074 blocked the pan-JAK inhibitor-induced endoreduplication. These data thus show that JAK inhibition causes nuclear re-localization and phosphorylation of RAF and MEK where RAF binds BubR1 with ensuing nuclear RAF-dependent BubR1 phosphorylation. Inhibiting RAF inhibited this and endoreduplication. The results suggest that there is a JAK/RAF/MEK/BubR1 axis that can regulate genomic stability. In this hypothetical model JAK suppresses RAF/MEK phosphorylation and nuclear re-localization, but JAK inhibition induces the phosphorylations and re-localization with association of RAF and phosphorylated BubR1 in the nucleus leading to endoreduplication.

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., Author Summary To date, mechanistic mathematical modeling, in general, has not played a significant role in the development of new therapies for cancer, cardiovascular diseases, or the treatment of acute events like thrombosis during surgery. One critical issue often cited for the lack of interest has been uncertainty; the conventional wisdom is that the data requirement to fully determine and validate large mechanistic models is just too high. We show, using tools from systems biology and sensitivity analysis, that it may be possible to extract qualitative information about the critical elements of human relevant cascades despite model uncertainty. Using a mechanistic model of the human coagulation cascade, we were able to identify the critical mechanisms controlling the formation of thrombin, a key protein active in the formation of blood clots. We were further able to support the hypothesis that the critical mechanisms identified by our analysis could serve as drug targets by comparing our findings with the thrombosis treatment literature and with current clinical trials. The results support the notion that mechanistic models could be used, despite model uncertainty, to pinpoint key mechanisms in complex networks, and that these mechanisms could potentially be therapeutically exploited.