Parameter adaptations during phenotype transitions in progressive diseases

<p>Abstract</p> <p>Background</p> <p>The study of phenotype transitions is important to understand progressive diseases, e.g., diabetes mellitus, metabolic syndrome, and cardiovascular diseases. A challenge remains to explain phenotype transitions in terms of adaptations in molecular components and interactions in underlying biological systems.</p> <p>Results</p> <p>Here, mathematical modeling is used to describe the different phenotypes by integrating experimental data on metabolic pools and fluxes. Subsequently, trajectories of parameter adaptations are identified that are essential for the phenotypical changes. These changes in parameters reflect progressive adaptations at the transcriptome and proteome level, which occur at larger timescales. The approach was employed to study the metabolic processes underlying liver X receptor induced hepatic steatosis. Model analysis predicts which molecular processes adapt in time after pharmacological activation of the liver X receptor. Our results show that hepatic triglyceride fluxes are increased and triglycerides are especially stored in cytosolic fractions, rather than in endoplasmic reticulum fractions. Furthermore, the model reveals several possible scenarios for adaptations in cholesterol metabolism. According to the analysis, the additional quantification of one cholesterol flux is sufficient to exclude many of these hypotheses.</p> <p>Conclusions</p> <p>We propose a generic computational approach to analyze biological systems evolving through various phenotypes and to predict which molecular processes are responsible for the transition. For the case of liver X receptor induced hepatic steatosis the novel approach yields information about the redistribution of fluxes and pools of triglycerides and cholesterols that was not directly apparent from the experimental data. Model analysis provides guidance which specific molecular processes to study in more detail to obtain further understanding of the underlying biological system.</p

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib.

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines

Applications of analysis of dynamic adaptations in parameter trajectories

Metabolic profiling in combination with pathway-based analyses and computational modelling are becoming increasingly important in clinical and preclinical research. Modelling multi-factorial, progressive diseases requires the integration of molecular data at the metabolome, proteome and transcriptome level. Also the dynamic interaction of organs and tissues needs to be considered. The processes involved cover time scales that are several orders of magnitude different. We report applications of a computational approach to bridge the scales and different levels of biological detail. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) aims to investigate phenotype transitions during disease development and after a therapeutic intervention. ADAPT is based on a time-dependent evolution of model parameters to describe the dynamics of metabolic adaptations. The progression of metabolic adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages. To get a better understanding of the concept, the ADAPT approach is illustrated in a theoretical study. Its application in research on progressive changes in lipoprotein metabolism is also discussed

A computational model of hepatic lipid metabolism : identifying persistent behavior

Abstract onl

A novel modelling approach to analyze complex metabolic pathway dynamics; application to LXR activated lipoprotein metabolism

Lipoprotein metabolism is governed by a highly complex network of metabolic pathways involved in lipid and protein synthesis and degradation. Understanding the dynamics of network regulation is impossible without the help of computational modeling. We present a new modeling approach to analyze the long term effects of a dietary or pharmacological intervention in lipoprotein metabolism. A concept of time-dependent evolution of model parameters is introduced to study the dynamics of metabolic network adaptations. The progression of these adaptations is predicted by identifying necessary dynamic changes in the model parameters to describe the transition of steady states during different stages of the dietary or pharmacological treatment. The trajectories provide insight into the affected underlying biological systems and provide targeted direction to the molecular events that should be studied in more detail to unravel the mechanistic basis of treatment outcome. Modulating effects on pathways caused by interactions with the proteome and transcriptome levels can be captured by the time-dependent descriptions of the parameters. The approach was employed to identify metabolic adaptations induced upon a 3-week activation of the liver X receptor (LXR) in C57BL/6J wild-type mice. The metabolic trajectories were modeled to analyze the metabolic adaptations in time. This provided a number of sometimes counterintuitive insights into the underlying mechanisms inducing hepatic steatosis, dynamic changes in plasma triglycerides, and plasma HDL content. The model predicted for instance decreased activity of the scavenger receptor class B1 (SR-B1) despite an increased flux mediated via the receptor. This prediction was validated experimentally by immunoblotting measurements of SR-B1 in hepatic membranes. We also show that this procedure can be used quite simply to select optimal therapeutic targets successfully

Application of ADAPT to identify adaptations upon pharmacological treatment of mice by LXR agonist T0901317.

<p>The intervention starts at the proteome level and subsequently induces adaptations at the other levels (left part, vertical arrows). Mathematical modeling was focused on integrating biological pathways from which the topology is well known and a substantial amount of components were measured quantitatively, i.e. the metabolome level (right part). A detailed description of the mathematical model is presented in Supporting Information <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003166#pcbi.1003166.s003" target="_blank">Text S3</a>. The modulating effects on metabolic pathways via interactions with the proteome and transcriptome levels are less well understood. At present it is not yet feasible to include a full mechanistic description of these interactions in the model. ADAPT overcomes this problem by introducing time-dependent parameters that incorporate missing modulating effects.</p

Estimation of time-dependent parameters.

<p>The progression of adaptations induced by a treatment intervention is predicted by identifying necessary dynamic changes in the model parameters to describe the transition between experimental data obtained during different stages of the treatment. The time-dependency of the parameters is introduced by dividing a simulation in steps of time period. Initially () the system is in steady-state and corresponding parameters are estimated to describe the experimental data of the untreated phenotype. Subsequently, for each step the system is simulated for a time period of using the final values of the model states of the previous step as initial conditions (B). Simultaneously, parameters are estimated (A) by minimizing the difference between the data interpolants and corresponding model outputs (C). Here, the previously estimated parameter set was provided as initial set for the optimization algorithm.</p

The hepatic HDL-C uptake capacity is reduced upon LXR activation.

<p> histograms were calculated from the acceptable sets to determine the density of trajectories during the treatment period. A darker color represents a higher density of trajectories in that specific region and time point. The white lines enclose the central of the densities. A) HDL-C concentration. The white dots represent the experimental data obtained via FPLC measurements from pooled mice plasma. B) Peripheral cholesterol efflux to HDL particles. C) Hepatic uptake of HDL-C. D) Difference between peripheral cholesterol efflux to HDL and HDL-C uptake by the liver. E) Normalized hepatic uptake capacity of HDL-C, which is assumed to be proportional the SR-B1 protein level. This prediction was recently confirmed experimentally by immunoblotting measurements of SR-B1 in hepatic membranes <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003166#pcbi.1003166-Grefhorst3" target="_blank">[27]</a> (data represent means standard deviations). Note that this data serves as an independent validation and was not included in the optimization procedure.</p

Rise and fall periods of metabolic concentrations, parameters, and fluxes.

<p>The rise and fall periods are represented by light-gray and dark-gray bars (median median absolute deviation), respectively. The rise period is defined as the time period during which a trajectory rises from to of its maximal value. Similarly, the fall period is defined as the time period during which a trajectory falls from to of its maximal value.</p