Calcium is a noncompetitive inhibitor of DMT1 on the intestinal iron absorption process: empirical evidence and mathematical modeling analysis.

Iron absorption is a complex and highly controlled process where DMT1 transports nonheme iron through the brush-border membrane of enterocytes to the cytoplasm but does not transport alkaline-earth metals such as calcium. However, it has been proposed that high concentrations of calcium in the diet could reduce iron bioavailability. In this work, we investigate the effect of intracellular and extracellular calcium on iron uptake by Caco-2 cells, as determined by calcein fluorescence quenching. We found that extracellular calcium inhibits iron uptake by Caco-2 cells in a concentration-dependent manner. Chelation of intracellular calcium with BAPTA did not affect iron uptake, which indicates that the inhibitory effect of calcium is not exerted through intracellular calcium signaling. Kinetic studies performed, provided evidence that calcium acts as a reversible noncompetitive inhibitor of the iron transport activity of DMT1. Based on these experimental results, a mathematical model was developed that considers the dynamics of noncompetitive inhibition using a four-state mechanism to describe the inhibitory effect of calcium on the DMT1 iron transport process in intestinal cells. The model accurately predicts the calcein fluorescence quenching dynamics observed experimentally after an iron challenge. Therefore, the proposed model structure is capable of representing the inhibitory effect of extracellular calcium on DMT1-mediated iron entry into the cLIP of Caco-2 cells. Considering the range of calcium concentrations that can inhibit iron uptake, the possible inhibition of dietary calcium on intestinal iron uptake is discussed.

Novel Symbiotic Genome-Scale Model Reveals Wolbachia's Arboviral Pathogen Blocking Mechanism in Aedes aegypti.

Wolbachia are endosymbiont bacteria known to infect arthropods causing different effects, such as cytoplasmic incompatibility and pathogen blocking in Aedes aegypti. Although several Wolbachia strains have been studied, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their obligate endosymbiont nature and its pathogen blocking ability. Motivated by the potential applications on disease control, we developed a genome-scale model of two Wolbachia strains: wMel and the strongest Dengue blocking strain known to date: wMelPop. The obtained metabolic reconstructions exhibit an energy metabolism relying mainly on amino acids and lipid transport to support cell growth that is consistent with altered lipid and cholesterol metabolism in Wolbachia-infected mosquitoes. The obtained metabolic reconstruction was then coupled with a reconstructed mosquito model to retrieve a symbiotic genome-scale model accounting for 1,636 genes and 6,408 reactions of the Aedes aegypti-Wolbachia interaction system. Simulation of an arboviral infection in the obtained novel symbiotic model represents a metabolic scenario characterized by pathogen blocking in higher titer Wolbachia strains, showing that pathogen blocking by Wolbachia infection is consistent with competition for lipid and amino acid resources between arbovirus and this endosymbiotic bacteria. IMPORTANCE Arboviral diseases such as Zika and Dengue have been on the rise mainly due to climate change, and the development of new treatments and strategies to limit their spreading is needed. The use of Wolbachia as an approach for disease control has motivated new research related to the characterization of the mechanisms that underlie its pathogen-blocking properties. In this work, we propose a new approach for studying the metabolic interactions between Aedes aegypti and Wolbachia using genome-scale models, finding that pathogen blocking is mainly influenced by competition for the resources required for Wolbachia and viral replication.

A systems biology approach for studying Wolbachia metabolism reveals points of interaction with its host in the context of arboviral infection.

Wolbachia are alpha-proteobacteria known to infect arthropods, which are of interest for disease control since they have been associated with improved resistance to viral infection. Although several genomes for different strains have been sequenced, there is little knowledge regarding the relationship between this bacterium and their hosts, particularly on their dependency for survival. Motivated by the potential applications on disease control, we developed genome-scale models of four Wolbachia strains known to infect arthropods: wAlbB (Aedes albopictus), wVitA (Nasonia vitripennis), wMel and wMelPop (Drosophila melanogaster). The obtained metabolic reconstructions exhibit a metabolism relying mainly on amino acids for energy production and biomass synthesis. A gap analysis was performed to detect metabolic candidates which could explain the endosymbiotic nature of this bacterium, finding that amino acids, requirements for ubiquinone precursors and provisioning of metabolites such as riboflavin could play a crucial role in this relationship. This work provides a systems biology perspective for studying the relationship of Wolbachia with its host and the development of new approaches for control of the spread of arboviral diseases. This approach, where metabolic gaps are key objects of study instead of just additions to complete a model, could be applied to other endosymbiotic bacteria of interest.

Mathematical modeling of the relocation of the divalent metal transporter DMT1 in the intestinal iron absorption process.

Iron is essential for the normal development of cellular processes. This metal has a high redox potential that can damage cells and its overload or deficiency is related to several diseases, therefore it is crucial for its absorption to be highly regulated. A fast-response regulatory mechanism has been reported known as mucosal block, which allows to regulate iron absorption after an initial iron challenge. In this mechanism, the internalization of the DMT1 transporters in enterocytes would be a key factor. Two phenomenological models are proposed for the iron absorption process: DMT1's binary switching mechanism model and DMT1's swinging-mechanism model, which represent the absorption mechanism for iron uptake in intestinal cells. The first model considers mutually excluding processes for endocytosis and exocytosis of DMT1. The second model considers a Ball's oscillator to represent the oscillatory behavior of DMT1's internalization. Both models are capable of capturing the kinetics of iron absorption and represent empirical observations, but the DMT1's swinging-mechanism model exhibits a better correlation with experimental data and is able to capture the regulatory phenomenon of mucosal block. The DMT1 swinging-mechanism model is the first phenomenological model reported to effectively represent the complexity of the iron absorption process, as it can predict the behavior of iron absorption fluxes after challenging cells with an initial dose of iron, and the reduction in iron uptake observed as a result of mucosal block after a second iron dose.

Parameter estimation and mathematical modeling for the quantitative description of therapy failure due to drug resistance in gastrointestinal stromal tumor metastasis to the liver.

In this work we develop a general mathematical model and devise a practical identifiability approach for gastrointestinal stromal tumor (GIST) metastasis to the liver, with the aim of quantitatively describing therapy failure due to drug resistance. To this end, we have modeled metastatic growth and therapy failure produced by resistance to two standard treatments based on tyrosine kinase inhibitors (Imatinib and Sunitinib) that have been observed clinically in patients with GIST metastasis to the liver. The parameter identification problem is difficult to solve, since there are no general results on this issue for models based on ordinary differential equations (ODE) like the ones studied here. We propose a general modeling framework based on ODE for GIST metastatic growth and therapy failure due to drug resistance and analyzed five different model variants, using medical image observations (CT scans) from patients that exhibit drug resistance. The associated parameter estimation problem was solved using the Nelder-Mead simplex algorithm, by adding a regularization term to the objective function to address model instability, and assessing the agreement of either an absolute or proportional error in the objective function. We compared the goodness of fit to data for the proposed model variants, as well as evaluated both error forms in order to improve parameter estimation results. From the model variants analyzed, we identified the one that provides the best fit to all the available patient data sets, as well as the best assumption in computing the objective function (absolute or proportional error). This is the first work that reports mathematical models capable of capturing and quantitatively describing therapy failure due to drug resistance based on clinical images in a patient-specific manner.

Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming.

Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modeling framework. This is the first report of short-term studies for this system. A non-linear behavior in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jackknife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems.

A Consensus Genome-scale Reconstruction of Chinese Hamster Ovary Cell Metabolism.

Chinese hamster ovary (CHO) cells dominate biotherapeutic protein production and are widely used in mammalian cell line engineering research. To elucidate metabolic bottlenecks in protein production and to guide cell engineering and bioprocess optimization, we reconstructed the metabolic pathways in CHO and associated them with >1,700 genes in the Cricetulus griseus genome. The genome-scale metabolic model based on this reconstruction, iCHO1766, and cell-line-specific models for CHO-K1, CHO-S, and CHO-DG44 cells provide the biochemical basis of growth and recombinant protein production. The models accurately predict growth phenotypes and known auxotrophies in CHO cells. With the models, we quantify the protein synthesis capacity of CHO cells and demonstrate that common bioprocess treatments, such as histone deacetylase inhibitors, inefficiently increase product yield. However, our simulations show that the metabolic resources in CHO are more than three times more efficiently utilized for growth or recombinant protein synthesis following targeted efforts to engineer the CHO secretory pathway. This model will further accelerate CHO cell engineering and help optimize bioprocesses.

Iron-induced reactive oxygen species mediate transporter DMT1 endocytosis and iron uptake in intestinal epithelial cells.

Recent evidence shows that iron induces the endocytosis of the iron transporter dimetal transporter 1 (DMT1) during intestinal absorption. We, and others, have proposed that iron-induced DMT1 internalization underlies the mucosal block phenomena, a regulatory response that downregulates intestinal iron uptake after a large oral dose of iron. In this work, we investigated the participation of reactive oxygen species (ROS) in the establishment of this response. By means of selective surface protein biotinylation of polarized Caco-2 cells, we determined the kinetics of DMT1 internalization from the apical membrane after an iron challenge. The initial decrease in DMT1 levels in the apical membrane induced by iron was followed at later times by increased levels of DMT1. Addition of Fe(2+), but not of Cd(2+), Zn(2+), Cu(2+), or Cu(1+), induced the production of intracellular ROS, as detected by 2',7'-dichlorofluorescein (DCF) fluorescence. Preincubation with the antioxidant N-acetyl-l-cysteine (NAC) resulted in increased DMT1 at the apical membrane before and after addition of iron. Similarly, preincubation with the hydroxyl radical scavenger dimethyl sulfoxide (DMSO) resulted in the enhanced presence of DMT1 at the apical membrane. The decrease of DMT1 levels at the apical membrane induced by iron was associated with decreased iron uptake rates. A kinetic mathematical model based on operational rate constants of DMT1 endocytosis and exocytosis is proposed. The model qualitatively captures the experimental observations and accurately describes the effect of iron, NAC, and DMSO on the apical distribution of DMT1. Taken together, our data suggest that iron uptake induces the production of ROS, which modify DMT1 endocytic cycling, thus changing the iron transport activity at the apical membrane.

Comparative metabolic analysis of CHO cell clones obtained through cell engineering, for IgG productivity, growth and cell longevity.

Cell engineering has been used to improve animal cells' central carbon metabolism. Due to the central carbon metabolism's inefficiency and limiting input of carbons into the TCA cycle, key reactions belonging to these pathways have been targeted to improve cultures' performance. Previous works have shown the positive effects of overexpressing PYC2, MDH II and fructose transporter. Since each of these modifications was performed in different cell lines and culture conditions, no comparisons between these modifications can be made. In this work we aim at contrasting the effect of each of the modifications by comparing pools of transfected IgG producing CHO cells cultivated in batch cultures. Results of the culture performance of engineered clones indicate that even though all studied clones had a more efficient metabolism, not all of them showed the expected improvement on cell proliferation and/or specific productivity. CHO cells overexpressing PYC2 were able to improve their exponential growth rate but IgG synthesis was decreased, MDH II overexpression lead to a reduction in cell growth and protein production, and cells transfected with the fructose transporter gene were able to increase cell density and reach the same volumetric protein production as parental CHO cells in glucose. We propose that a redox unbalance caused by the new metabolic flux distribution could affect IgG assembly and protein secretion. In addition to reaction dynamics, thermodynamic aspects of metabolism are also discussed to further understand the effect of these modifications over central carbon metabolism.

Modeling metabolic networks for mammalian cell systems: general considerations, modeling strategies, and available tools.

Over the past decades, the availability of large amounts of information regarding cellular processes and reaction rates, along with increasing knowledge about the complex mechanisms involved in these processes, has changed the way we approach the understanding of cellular processes. We can no longer rely only on our intuition for interpreting experimental data and evaluating new hypotheses, as the information to analyze is becoming increasingly complex. The paradigm for the analysis of cellular systems has shifted from a focus on individual processes to comprehensive global mathematical descriptions that consider the interactions of metabolic, genomic, and signaling networks. Analysis and simulations are used to test our knowledge by refuting or validating new hypotheses regarding a complex system, which can result in predictive capabilities that lead to better experimental design. Different types of models can be used for this purpose, depending on the type and amount of information available for the specific system. Stoichiometric models are based on the metabolic structure of the system and allow explorations of steady state distributions in the network. Detailed kinetic models provide a description of the dynamics of the system, they involve a large number of reactions with varied kinetic characteristics and require a large number of parameters. Models based on statistical information provide a description of the system without information regarding structure and interactions of the networks involved. The development of detailed models for mammalian cell metabolism has only recently started to grow more strongly, due to the intrinsic complexities of mammalian systems, and the limited availability of experimental information and adequate modeling tools. In this work we review the strategies, tools, current advances, and recent models of mammalian cells, focusing mainly on metabolism, but discussing the methodology applied to other types of networks as well.

Mathematical modeling of the dynamic storage of iron in ferritin.

BACKGROUND: Iron is essential for the maintenance of basic cellular processes. In the regulation of its cellular levels, ferritin acts as the main intracellular iron storage protein. In this work we present a mathematical model for the dynamics of iron storage in ferritin during the process of intestinal iron absorption. A set of differential equations were established considering kinetic expressions for the main reactions and mass balances for ferritin, iron and a discrete population of ferritin species defined by their respective iron content. RESULTS: Simulation results showing the evolution of ferritin iron content following a pulse of iron were compared with experimental data for ferritin iron distribution obtained with purified ferritin incubated in vitro with different iron levels. Distinctive features observed experimentally were successfully captured by the model, namely the distribution pattern of iron into ferritin protein nanocages with different iron content and the role of ferritin as a controller of the cytosolic labile iron pool (cLIP). Ferritin stabilizes the cLIP for a wide range of total intracellular iron concentrations, but the model predicts an exponential increment of the cLIP at an iron content > 2,500 Fe/ferritin protein cage, when the storage capacity of ferritin is exceeded. CONCLUSIONS: The results presented support the role of ferritin as an iron buffer in a cellular system. Moreover, the model predicts desirable characteristics for a buffer protein such as effective removal of excess iron, which keeps intracellular cLIP levels approximately constant even when large perturbations are introduced, and a freely available source of iron under iron starvation. In addition, the simulated dynamics of the iron removal process are extremely fast, with ferritin acting as a first defense against dangerous iron fluctuations and providing the time required by the cell to activate slower transcriptional regulation mechanisms and adapt to iron stress conditions. In summary, the model captures the complexity of the iron-ferritin equilibrium, and can be used for further theoretical exploration of the role of ferritin in the regulation of intracellular labile iron levels and, in particular, as a relevant regulator of transepithelial iron transport during the process of intestinal iron absorption.

Modeling heterocyst pattern formation in cyanobacteria.

BACKGROUND: To allow the survival of the population in the absence of nitrogen, some cyanobacteria strains have developed the capability of differentiating into nitrogen fixing cells, forming a characteristic pattern. In this paper, the process by which cyanobacteria differentiates from vegetative cells into heterocysts in the absence of nitrogen and the elements of the gene network involved that allow the formation of such a pattern are investigated. METHODS: A simple gene network model, which represents the complexity of the differentiation process, and the role of all variables involved in this cellular process is proposed. Specific characteristics and details of the system's behavior such as transcript profiles for ntcA, hetR and patS between consecutive heterocysts were studied. RESULTS: The proposed model is able to capture one of the most distinctive features of this system: a characteristic distance of 10 cells between two heterocysts, with a small standard deviation according to experimental variability. The system's response to knock-out and over-expression of patS and hetR was simulated in order to validate the proposed model against experimental observations. In all cases, simulations show good agreement with reported experimental results. CONCLUSION: A simple evolution mathematical model based on the gene network involved in heterocyst differentiation was proposed. The behavior of the biological system naturally emerges from the network and the model is able to capture the spacing pattern observed in heterocyst differentiation, as well as the effect of external perturbations such as nitrogen deprivation, gene knock-out and over-expression without specific parameter fitting.

Comparative transcriptome analysis to unveil genes affecting recombinant protein productivity in mammalian cells.

Low temperature culture (33 degrees C) has been shown to enhance the specific productivity of recombinant antibodies in Chinese hamster ovary (CHO) cells but did not affect antibody productivity in hybridoma (MAK) cells. We probed the transcriptional response of both cells undergoing temperature shift using cDNA microarrays. Among the orthologous gene probes, common trends in the expression changes between CHO and MAK are not prominent. Instead, many transcriptional changes were specific to only one cell line. Notably, oxidative phosphorylation and ribosomal genes were downregulated in MAK but not in CHO. Conversely, several protein trafficking genes and cytoskeleton elements were upregulated in CHO but remained unchanged in MAK. Interestingly, at 33 degrees C, immunoglobulin heavy and light chain showed no significant changes in CHO, but the immunoglobulin light chain was downregulated in MAK. Overall, a clear distinction in the transcriptional response to low temperature was seen in the two cell lines. To further elucidate the set of genes responsible for increased antibody productivity, the expression data of low temperature cultures was compared to that of butyrate treatment which increased specific antibody productivity in both cell lines. Genes which are commonly differentially expressed under conditions that increased productivity are likely to reflect functional classes that are important in the productivity changes. This comparative transcriptome analysis suggests that vesicle trafficking, endocytosis and cytoskeletal elements are involved in increased specific antibody productivity.

Non-linear reduction for kinetic models of metabolic reaction networks.

Kinetic models of metabolic networks are essential for predicting and optimizing the transient behavior of cells in culture. However, such models are inherently high dimensional and stiff due to the large number of species and reactions involved and to kinetic rate constants of widely different orders of magnitude. In this paper we address the problem of deriving non-stiff, reduced-order non-linear models of the dominant dynamics of metabolic networks with fast and slow reactions. We present a method, based on singular perturbation analysis, which allows the systematic identification of quasi-steady-state conditions for the fast reactions, and the derivation of explicit non-linear models of the slow dynamics independent of the fast reaction rate expressions. The method is successfully applied to detailed models of metabolism in human erythrocytes and Saccharomyces cerevisiae.