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

Toll-like receptor 4 deficiency: Smaller infarcts, but nogain in function

<p>Abstract</p> <p>Backgound</p> <p>It has been reported that Toll-like receptor 4 (TLR4) deficiency reduces infarct size after myocardial ischemia/reperfusion (MI/R). However, measurement of MI/R injury was limited and did not include cardiac <b>function</b>. In a chronic closed-chest model we assessed whether cardiac <b>function </b>is preserved in TLR4-deficient mice (C3H/HeJ) following MI/R, and whether myocardial and systemic cytokine expression differed compared to wild type (WT).</p> <p>Results</p> <p>Infarct size (IS) in C3H/HeJ assessed by TTC staining after 60 min ischemia and 24h reperfusion was significantly smaller than in WT. Despite a smaller infarct size, echocardiography showed no functional difference between C3H/HeJ and WT. Left-ventricular developed pressure measured with a left-ventricular catheter was lower in C3H/HeJ (63.0 ± 4.2 mmHg vs. 77.9 ± 1.7 mmHg in WT, p < 0.05). Serum cytokine levels and myocardial IL-6 were higher in WT than in C3H/HeJ (p < 0.05). C3H/HeJ MI/R showed increased myocardial IL-1β and IL-6 expression compared to their respective shams (p < 0.05), indicating TLR4-independent cytokine activation due to MI/R.</p> <p>Conclusion</p> <p>These results demonstrate that, although a mutant TLR4 signaling cascade reduces myocardial IS and serum cytokine levels, it <b>does not preserve myocardial function</b>. The change in inflammatory response, secondary to a non-functional TLR-4 receptor, may contribute to the observed dichotomy between infarct size and function in the TLR-4 mutant mouse.</p

Photoassociation spectroscopy of cold calcium atoms

Photoassociation spectroscopy experiments on 40Ca atoms close to the dissociation limit 4s4s 1S0 - 4s4p 1P1 are presented. The vibronic spectrum was measured for detunings of the photoassociation laser ranging from 0.6 GHz to 68 GHz with respect to the atomic resonance. In contrast to previous measurements the rotational splitting of the vibrational lines was fully resolved. Full quantum mechanical numerical simulations of the photoassociation spectrum were performed which allowed us to put constraints on the possible range of the calcium scattering length to between 50 a_0 and 300 a_0

Sharing data from molecular simulations

Given the need for modern researchers to produce open, reproducible scientific output, the lack of standards and best practices for sharing data and workflows used to produce and analyze molecular dynamics (MD) simulations has become an important issue in the field. There are now multiple well-established packages to perform molecular dynamics simulations, often highly tuned for exploiting specific classes of hardware, each with strong communities surrounding them, but with very limited interoperability/transferability options. Thus, the choice of the software package often dictates the workflow for both simulation production and analysis. The level of detail in documenting the workflows and analysis code varies greatly in published work, hindering reproducibility of the reported results and the ability for other researchers to build on these studies. An increasing number of researchers are motivated to make their data available, but many challenges remain in order to effectively share and reuse simulation data. To discuss these and other issues related to best practices in the field in general, we organized a workshop in November 2018 (https://bioexcel.eu/events/workshop-on-sharing-data-from-molecular-simulations/). Here, we present a brief overview of this workshop and topics discussed. We hope this effort will spark further conversation in the MD community to pave the way toward more open, interoperable, and reproducible outputs coming from research studies using MD simulations

Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis

Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.</p

Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis

Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists

Toll-like receptor 4 deficiency: smaller infarcts, but no gain in function.

BackgroundIt has been reported that Toll-like receptor 4 (TLR4) deficiency reduces infarct size after myocardial ischemia/reperfusion (MI/R). However, measurement of MI/R injury was limited and did not include cardiac function. In a chronic closed-chest model we assessed whether cardiac function is preserved in TLR4-deficient mice (C3H/HeJ) following MI/R, and whether myocardial and systemic cytokine expression differed compared to wild type (WT).ResultsInfarct size (IS) in C3H/HeJ assessed by TTC staining after 60 min ischemia and 24h reperfusion was significantly smaller than in WT. Despite a smaller infarct size, echocardiography showed no functional difference between C3H/HeJ and WT. Left-ventricular developed pressure measured with a left-ventricular catheter was lower in C3H/HeJ (63.0 +/- 4.2 mmHg vs. 77.9 +/- 1.7 mmHg in WT, p &lt; 0.05). Serum cytokine levels and myocardial IL-6 were higher in WT than in C3H/HeJ (p &lt; 0.05). C3H/HeJ MI/R showed increased myocardial IL-1beta and IL-6 expression compared to their respective shams (p &lt; 0.05), indicating TLR4-independent cytokine activation due to MI/R.ConclusionThese results demonstrate that, although a mutant TLR4 signaling cascade reduces myocardial IS and serum cytokine levels, it does not preserve myocardial function. The change in inflammatory response, secondary to a non-functional TLR-4 receptor, may contribute to the observed dichotomy between infarct size and function in the TLR-4 mutant mouse

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