A computational model to understand mouse iron physiology and disease.

It is well known that iron is an essential element for life but is toxic when in excess or in certain forms. Accordingly there are many diseases that result directly from either lack or excess of iron. Yet many molecular and physiological aspects of iron regulation have only been discovered recently and others are still elusive. There is still no good quantitative and dynamic description of iron absorption, distribution, storage and mobilization that agrees with the wide array of phenotypes presented in several iron-related diseases. The present work addresses this issue by developing a mathematical model of iron distribution in mice calibrated with ferrokinetic data and subsequently validated against data from mouse models of iron disorders, such as hemochromatosis, ß-thalassemia, atransferrinemia and anemia of inflammation. To adequately fit the ferrokinetic data required inclusion of the following mechanisms: a) transferrin-mediated iron delivery to tissues, b) induction of hepcidin by transferrin-bound iron, c) ferroportin-dependent iron export regulated by hepcidin, d) erythropoietin regulation of erythropoiesis, and e) liver uptake of NTBI. The utility of the model to simulate disease interventions was demonstrated by using it to investigate the outcome of different schedules of transferrin treatment in ß-thalassemia.

Gastrointestinal iron excretion and reversal of iron excess in a mouse model of inherited iron excess.

The current paradigm in the field of mammalian iron biology states that body iron levels are determined by dietary iron absorption, not by iron excretion. Iron absorption is a highly regulated process influenced by iron levels and other factors. Iron excretion is believed to occur at a basal rate irrespective of iron levels and is associated with processes such as turnover of intestinal epithelium, blood loss, and exfoliation of dead skin. Here we explore iron excretion in a mouse model of iron excess due to inherited transferrin deficiency. Iron excess in this model is attributed to impaired regulation of iron absorption leading to excessive dietary iron uptake. Pharmacological correction of transferrin deficiency not only normalized iron absorption rates and halted progression of iron excess but also reversed body iron excess. Transferrin treatment did not alter the half-life of 59Fe in mutant mice. 59Fe-based studies indicated that most iron was excreted via the gastrointestinal tract and suggested that iron-loaded mutant mice had increased rates of iron excretion. Direct measurement of urinary iron levels agreed with 59Fe-based predictions that urinary iron levels were increased in untreated mutant mice. Fecal ferritin levels were also increased in mutant mice relative to wild-type mice. Overall, these data suggest that mice have a significant capacity for iron excretion. We propose that further investigation into iron excretion is warranted in this and other models of perturbed iron homeostasis, as pharmacological targeting of iron excretion may represent a novel means of treatment for diseases of iron excess.

An important role for periplasmic storage in Pseudomonas aeruginosa copper homeostasis revealed by a combined experimental and computational modeling study.

Biological systems require precise copper homeostasis enabling metallation of cuproproteins while preventing metal toxicity. In bacteria, sensing, transport, and storage molecules act in coordination to fulfill these roles. However, there is not yet a kinetic schema explaining the system integration. Here, we report a model emerging from experimental and computational approaches that describes the dynamics of copper distribution in Pseudomonas aeruginosa. Based on copper uptake experiments, a minimal kinetic model describes well the copper distribution in the wild-type bacteria but is unable to explain the behavior of the mutant strain lacking CopA1, a key Cu+ efflux ATPase. The model was expanded through an iterative hypothesis-driven approach, arriving to a mechanism that considers the induction of compartmental pools and the parallel function of CopA and Cus efflux systems. Model simulations support the presence of a periplasmic copper storage with a crucial role under dyshomeostasis conditions in P. aeruginosa. Importantly, the model predicts not only the interplay of periplasmic and cytoplasmic pools but also the existence of a threshold in the concentration of external copper beyond which cells lose their ability to control copper levels.

Modeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient.

BACKGROUND: Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data. RESULTS: A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments. CONCLUSIONS: These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important.

Primary peritoneal serous carcinoma: a rare case and palliative approach.

Primary peritoneal serous carcinoma (PPSC) is a rare primary malignancy that diffusely involves the peritoneum, indistinguishable clinically and histopathologically from primary serous ovarian carcinoma. The origin of PPSC has not been well characterized. Here we present a case of PPSC diagnosed in ultrasonography-guided fine needle aspiration cytology (FNAC) in a 76- old female presenting with ascites, abdominal pain, distension and constipation. PPSC is an unusual tumour but cytomorphology is distinctive enough to diagnose preoperatively. In the case report hereby described PPSC is an inoperable malignancy, hence chemotherapy and palliative care are the only offered treatment.

Knockdown of estrogen receptor-α induces autophagy and inhibits antiestrogen-mediated unfolded protein response activation, promoting ROS-induced breast cancer cell death.

Approximately 70% of all newly diagnosed breast cancers express estrogen receptor (ER)-α. Although inhibiting ER action using targeted therapies such as fulvestrant (ICI) is often effective, later emergence of antiestrogen resistance limits clinical use. We used antiestrogen-sensitive and -resistant cells to determine the effect of antiestrogens/ERα on regulating autophagy and unfolded protein response (UPR) signaling. Knockdown of ERα significantly increased the sensitivity of LCC1 cells (sensitive) and also resensitized LCC9 cells (resistant) to antiestrogen drugs. Interestingly, ERα knockdown, but not ICI, reduced nuclear factor (erythroid-derived 2)-like (NRF)-2 (UPR-induced antioxidant protein) and increased cytosolic kelch-like ECH-associated protein (KEAP)-1 (NRF2 inhibitor), consistent with the observed increase in ROS production. Furthermore, autophagy induction by antiestrogens was prosurvival but did not prevent ERα knockdown-mediated death. We built a novel mathematical model to elucidate the interactions among UPR, autophagy, ER signaling, and ROS regulation of breast cancer cell survival. The experimentally validated mathematical model explains the counterintuitive result that knocking down the main target of ICI (ERα) increased the effectiveness of ICI. Specifically, the model indicated that ERα is no longer present in excess and that the effect on proliferation from further reductions in its level by ICI cannot be compensated for by increased autophagy. The stimulation of signaling that can confer resistance suggests that combining autophagy or UPR inhibitors with antiestrogens would reduce the development of resistance in some breast cancers.

Modelling the effect of GRP78 on anti-oestrogen sensitivity and resistance in breast cancer.

Understanding the origins of resistance to anti-oestrogen drugs is of critical importance to many breast cancer patients. Recent experiments show that knockdown of GRP78, a key gene in the unfolded protein response (UPR), can re-sensitize resistant cells to anti-oestrogens, and overexpression of GRP78 in sensitive cells can cause them to become resistant. These results appear to arise from the operation and interaction of three cellular systems: the UPR, autophagy and apoptosis. To determine whether our current mechanistic understanding of these systems is sufficient to explain the experimental results, we built a mathematical model of the three systems and their interactions. We show that the model is capable of reproducing previously published experimental results and some new data gathered specifically for this paper. The model provides us with a tool to better understand the interactions that bring about anti-oestrogen resistance and the effects of GRP78 on both sensitive and resistant breast cancer cells.

Quantification of metabolism in Saccharomyces cerevisiae under hyperosmotic conditions using elementary mode analysis.

Yeast metabolism under hyperosmotic stress conditions was quantified using elementary mode analysis to obtain insights into the metabolic status of the cell. The fluxes of elementary modes were determined as solutions to a linear program that used the stoichiometry of the elementary modes as constraints. The analysis demonstrated that distinctly different sets of elementary modes operate under normal and hyperosmotic conditions. During the adaptation phase, elementary modes that only produce glycerol are active, while elementary modes that yield biomass, ethanol, and glycerol become active after the adaptive phase. The flux distribution in the metabolic network, calculated using the fluxes in the elementary modes, was employed to obtain the flux ratio at key nodes. At the glucose 6-phosphate (G6P) node, 25% of the carbon influx was diverted towards the pentose phosphate pathway under normal growth conditions, while only 0.3% of the carbon flux was diverted towards the pentose phosphate pathway during growth at 1 M NaCl, indicating that cell growth is arrested under hyperosmotic conditions. Further, objective functions were used in the linear program to obtain optimal solution spaces corresponding to the different accumulation rates. The analysis demonstrated that while biomass formation was optimal under normal growth conditions, glycerol synthesis was closer to optimal during adaptation to osmotic shock.

Characterization of the adaptive response and growth upon hyperosmotic shock in Saccharomyces cerevisiae.

Molecular and physiological details of osmoadaptation in yeast Saccharomyces cerevisiae are well characterized. It is well known that a cell, upon osmotic shock, delays its growth, produces a compatible solute like glycerol in yeast to maintain the osmotic equilibrium. Many genes are regulated by the hyperosmolarity glycerol (HOG) singling pathway, some of which in turn control the carbon flux in the glycolytic pathway for glycerol synthesis and reduced growth. The whole process of survival of cells under hyperosmotic stress is controlled at multiple levels in signaling and metabolic pathways. To better understand the multi-level regulations in yeast to osmotic shock, a mathematical model is formulated which integrates the growth and the osmoadaptation process. The model included the HOG pathway which consists of Sho1 and Sln1 signaling branches, gene regulation, metabolism and cell growth on glucose and ethanol. Experiments were performed to characterize the effect of various concentrations of salt on the wild-type and mutant strains. The model was able to successfully predict the experimental observations for both the wild-type and mutant strains. Further, the model was used to analyze the effects of various regulatory mechanisms prevalent in the signaling and metabolic pathways which are essential in achieving optimum growth in a saline medium. The analysis demonstrated the relevance of the combined effects of regulation at several points in the signaling and metabolic pathways including activation of GPD1 and GPD2, inhibition of PYK and PDC1, closure of the Fps1 channel, volume effect on the glucose uptake rate, downregulation of ethanol synthesis and upregulation of ALD6 for acetate synthesis. The analysis demonstrated that these combined effects orchestrated the phenomena of adaptation to osmotic stress in yeast.