Mathematical modeling of signaling and promoter crosstalk.

<p>(<b>A</b>) Thermodynamic modeling of promoter states. Depending on the transcription factor concentrations, the hepcidin promoter may be occupied by pSMAD (bound to BRE1 or BRE2), pSTAT (bound to STATBS) and RNAP (bound to the transcription start site), alone or in combination, giving rise to 16 different promoter states. A central presumption of thermodynamic modeling is that all RNAP-bound states are capable of transcription initiation, while RNAP-less states are considered silent. (<b>B</b>) A model selection approach allows for the identification of protein-protein interactions on the promoter. Various model variants were tested for their ability to fit the data in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi-1003421-g001" target="_blank">Fig. 1C</a>. The minimal model (model 1) assumes that each transcription factor independently activates RNAP (grey arrows), while more complex variants additionally take cooperativity among transcription factors into account (red arrows). Statistical criteria (Akaike information criterion, likelihood ratio test) indicate that model topology 4 is best suited to describe all data (see Methods, Supplemental <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi.1003421.s010" target="_blank">Protocol S2</a>). (<b>C</b>) and (<b>D</b>) Integrative crosstalk model simultaneously fits luciferase data and dose-response curves of transcription factor phosphorylation. The thermodynamic promoter model (topology 4 in panel B) was coupled to a simple signaling model describing inhibitory crosstalk between phospho-SMAD and phospo-STAT transcription factors (Supplemental <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi.1003421.s010" target="_blank">Protocol S2</a>). Solid lines in C represent model trajectories in comparison to experimentally measured data points (shown as mean +/− std). The simulated luciferase activities in D agree well with the corresponding data in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi-1003421-g001" target="_blank">Fig. 1C</a>.</p

Verification of model predictions using double-mutant promoters.

<p>(<b>A</b>) Schematic representation of double-mutant promoters which lack two transcription factor binding sites (cf. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi-1003421-g001" target="_blank">Fig. 1B</a>). (<b>B</b>) Systematic analysis of transcription factor binding site deletion effects confirms cooperativity of BRE1 and STATBS. The impact of binding site deletions was calculated by taking the luciferase activity ratios of different promoters (indicated in the legend) and expressed as a log10-fold change (y axis). As expected for a system where both sites cooperatively enhance transcription, the fold-change upon a combined deletion of BRE1 and STATBS (red) is less than the product of the single deletion fold-changes (green and blue; see text). Data points are mean and standard deviation, and model predictions represent the range of measurement-compliant parameter sets, as derived from a parameter identifiability analysis (see Methods, Supplemental <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi.1003421.s010" target="_blank">Protocol S2</a>). Only BRE1 and STATBS (but not BRE2) contribute to expression upon IL6 stimulation. (<b>C</b>) and (<b>D</b>) Co-stimulation heatmaps of double mutant promoters reveal that BRE1 and BRE2 are functionally similar in the absence of STATBS. (C) Heatmaps of luciferase activity under co-stimulation conditions. (D) Two-dimensional projection of the BRE1mSTATdel and BRE2mSTATdel data in C (averaged over all IL6 concentrations). Data points are mean (panel C, bottom row) or mean +/− std (panel D) (n = 6). Model predictions were formulated as ranges based on a parameter identifiability analysis (see Methods, Supplemental <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi.1003421.s010" target="_blank">Protocol S2</a>), and show measurement-compliant parameter sets with highest and lowest predicted luciferase expression (top and middle rows in panel C; edges of shaded corridors in panel D). Data and model in D were normalized to basal luciferase expression in the BRE2mSTATdel construct. (<b>E</b>) Systematic analysis of transcription factor binding site deletion effects confirms buffering of BRE1 and BRE2 single deletions. Concepts similar to panel B. The fold-change upon a combined deletion of BRE1 and BRE2 (red bars) is higher than the product of the single deletion fold-changes (green and blue bars; see text). BMP stimulation conditions were considered to ensure that BRE1 and BRE2 both contribute to expression.</p

Signal integration at the hepcidin promoter.

<p>(<b>A</b>) Schematic representation of two critical signaling pathways controlling hepcidin expression. SMAD and STAT transcription factors are phosphorylated upon BMP and IL6 stimulation, and bind BMP-responsive elements (BRE) and a STAT-binding site (STATBS) in the hepcidin promoter, respectively. The importance of signaling crosstalk is not clear. (TSS: transcription start site; RNAP: RNA polymerase) (<b>B</b>) and (<b>C</b>) Analysis of transcription factor crosstalk at the promoter level by reporter gene assays. Luciferase expression is driven by the wildtype (WT) hepcidin promoter (3 kb upstream of TSS) or promoter mutants lacking one of the transcription factor binding sites (panel B; BRE1m = BRE1 mutated; STATdel = deleted for STATBS). Luciferase activity of each reporter construct (shown on a log10-scale) was measured for increasing doses of IL6 and/or BMP (n = 6). All heatmaps represent the mean of at least four biological replicates (see Methods), and are given in the same arbitrary concentration units (<b>D</b>) Moderate inhibitory signaling crosstalk at the signaling level. Immunoblots against phosphorylated SMAD1/5/8 and STAT3 (Supplemental <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi.1003421.s002" target="_blank">Fig. S2</a>) were quantified by densitometric analysis. The data points and error bars represent mean and standard deviation of biological replicates (N = 2), respectively (see Methods). Lines are fits of the sigmoidal Hill equation (y = y_basal + y_max * Sⁿ/(Sⁿ + EC₅₀ⁿ), S…stimulus, y_basal…basal signaling activity, y_max…maximal pathway activation, EC50….half-maximal-stimulus, n…Hill coefficient). The fits with and without non-canonical stimulation (blue and green lines, respectively) solely differ in the y_max values.</p

Systems properties of hepcidin expression.

<p>(<b>A</b>) The presence of two BREs enhances promoter sensitivity towards BMP stimulation. Hepcidin expression (fold over basal) is shown as a function phospho-SMAD levels for the WT, BRE1m, and BRE2m promoter (phospho-STAT was assumed zero). The dashed lines indicate the maximal steepness of the WT dose-response. The grey corridor indicates range of phospho-SMAD levels in HuH7 cells. (<b>B</b>) and (<b>C</b>) Hepcidin expression is highly sensitive to BMP stimulation, and less sensitive to IL6. The luciferase activity (fold over basal) is plotted as function of the IL6 (blue) or BMP (red) concentration. Panel B shows simulations of the best-fit model, while panel C contains experimental data (n = 3–6) and fits of the Hill equation (solid lines). Dashed lines in C indicate the maximal steepness of the BMP response. (<b>D</b>) Extended mathematical model describing negative feedback regulation of iron blood levels by hepcidin <i>in vivo</i>. Iron blood levels (Fe_b) are controlled by influx and efflux reactions, and the iron influx rate is proportional to the intestinal iron concentration (species Fe_i). Iron blood levels control the BMP signaling pathway, and thus the expression of hepcidin, which in turn lowers the iron influx. Hepcidin expression regulation by IL6 and BMP was modeled using the best-fit crosstalk model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi-1003421-g002" target="_blank">Fig. 2C and D</a>; Supplemental <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi.1003421.s015" target="_blank">Text S4</a>). (<b>E</b>) Iron homeostasis requires two BMP-responsive elements and is abolished by inflammatory stimulation. Simulations of the extended model (panel D) show how iron blood levels respond to changes in the intestinal iron concentration. The model with a WT hepcidin promoter (blue solid line) shows relatively constant iron blood levels over a broad range of intestinal iron concentrations (‘homeostasis range’). Homeostasis is less efficient and the homeostasis range is narrower in model variants with BRE1m and BRE2m promoters, or if strong IL6 stimulation is assumed (see legend) The mutants are characterized by altered iron blood levels (reflecting iron overload and deficiency, respectively). (<b>F</b>) IL6 stimulation reduces the BMP sensitivity of the promoter. The best-fit model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003421#pcbi-1003421-g002" target="_blank">Fig. 2C and D</a>) was employed to simulate how increasing IL6 stimulation affects the BMP dose-response curve of the promoter. Dashed lines indicate the maximal slope in the absence of IL6. Grey corridor same as in A.</p

Modelling Systemic Iron Regulation during Dietary Iron Overload and Acute Inflammation: Role of Hepcidin-Independent Mechanisms

<div><p>Systemic iron levels must be maintained in physiological concentrations to prevent diseases associated with iron deficiency or iron overload. A key role in this process plays ferroportin, the only known mammalian transmembrane iron exporter, which releases iron from duodenal enterocytes, hepatocytes, or iron-recycling macrophages into the blood stream. Ferroportin expression is tightly controlled by transcriptional and post-transcriptional mechanisms in response to hypoxia, iron deficiency, heme iron and inflammatory cues by cell-autonomous and systemic mechanisms. At the systemic level, the iron-regulatory hormone hepcidin is released from the liver in response to these cues, binds to ferroportin and triggers its degradation. The relative importance of individual ferroportin control mechanisms and their interplay at the systemic level is incompletely understood. Here, we built a mathematical model of systemic iron regulation. It incorporates the dynamics of organ iron pools as well as regulation by the hepcidin/ferroportin system. We calibrated and validated the model with time-resolved measurements of iron responses in mice challenged with dietary iron overload and/or inflammation. The model demonstrates that inflammation mainly reduces the amount of iron in the blood stream by reducing intracellular ferroportin transcription, and not by hepcidin-dependent ferroportin protein destabilization. In contrast, ferroportin regulation by hepcidin is the predominant mechanism of iron homeostasis in response to changing iron diets for a big range of dietary iron contents. The model further reveals that additional homeostasis mechanisms must be taken into account at very high dietary iron levels, including the saturation of intestinal uptake of nutritional iron and the uptake of circulating, non-transferrin-bound iron, into liver. Taken together, our model quantitatively describes systemic iron metabolism and generated experimentally testable predictions for additional ferroportin-independent homeostasis mechanisms.</p></div

Model correctly predicts responses to perturbations in the SMAD4-hepcidin-pathway as well as development of anemia under chronic inflammation.

<p>A-Model quantitatively predicts experimentally measured responses for 2 months old C326S knock-in mice expressing a hepcidin-resistant FPN mutant or or SMAD4-knockout mice. The model simulations are shown as blue bars and the corresponding data from [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005322#pcbi.1005322.ref037" target="_blank">37</a>] and [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005322#pcbi.1005322.ref053" target="_blank">53</a>] as red bars, respectively. Fold changes are referred to the wildtype levels. The model error bars are calculated from the predictions of the 30 best fitting parameter sets (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005322#pcbi.1005322.s001" target="_blank">S1 Text</a>). B-Model prediction for body iron pools when ferroportin regulation by hepcidin is out of action in one of the indicated organs. Shown are model simulations whithout experimental validation. C-Model qualitatively reproduces the development of anemia of inflammation upon chronic elevation of body LPS. Simulation of plasma, RBC and liver iron evolution when the inflammatory Il6/STAT pathway is permanently activated by a persistent LPS stimulus (0.17 <i>μg</i>/g body weight). Shown are model simulations without a quantitative comparison to experiments.</p

LPS-induced dynamics of iron-related parameters under normal/enriched iron diet is well reproduced/predicted by the model.

<p>A-Serum iron, B-Liver Iron, C-Liver hepcidin, D-Spleen iron, E-Liver BMP6, F-Liver Fpn mRNA, G-Spleen Fpn protein, H-Liver pSTAT, I-Liver pSMAD, J-Duodenum iron, K-Red blood cells iron, L-Liver Fpn protein. 4–6 weeks old male C57BL/6-mice were administrated a normal diet, containing 200 ppm iron (blue), or a high iron diet, supplemented by 2% carbonyl iron containing about 20000 ppm iron (red). After 4 weeks, mice were injected with 1 <i>μg</i> LPS/g body weight and sacrificed 6/18/48 hours after the injection. Experimental data are given as means with standard deviation of 4–6 replicates and the model simulation for the best fitting parameter set is represented by curves (solid lines: fitted time courses, dashed lines: predicted time courses). Data represented be empty circles were used in fitting as a part of the calibration data set (LPS response for normal diet and the iron parameters after 4 weeks of high iron diet before injection of LPS). The LPS response for high iron diet data (filled circles) was used to test the model predictions. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005322#sec014" target="_blank">Materials and Methods</a> for the description of the experiment.</p

Iron overload as a consequence of an iron enriched diet leads to the preferential iron accumulation in the liver, which is quantitatively reproduced by the model considering NTBI uptake and liver ferritin storage.

<p>A-The measured distribution of iron between the organs is reproduced by the model fits for both normal and an iron enriched diet. The iron content of all compartments increases in mice maintained for 4 weeks on an iron rich diet, with most iron accumulating in the liver (arrows). B-Measured liver iron content under conditions of dietary iron overload and in HAMP-KO mice as well as best fit for the full model and models lacking NTBI uptake or liver ferritin storage, or both.</p

Experimental data used for model calibration.

<p>The table summarizes the experimental perturbations, time scales and measured quantities for the different datasets used.</p

Experimental data used for model validation.

<p>The table summarizes the experimental perturbations, time scales and measured quantities for the different datasets used.</p