Design principles of nuclear receptor signaling: how complex networking improves signal transduction

ROS networks: designs, aging, Parkinson's disease and precision therapies

How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.

Thyroid hormone receptor localization in target tissues

The thyroid hormone receptors, TRα1, TRβ1 and other subtypes, are members of the nuclear receptor superfamily that mediate the action of thyroid hormone signaling in numerous tissues to regulate important physiological and developmental processes. Their most well-characterized role is as ligand-dependent transcription factors; TRs bind thyroid hormone response elements in the presence or absence of thyroid hormone to facilitate the expression of target genes. Although primarily residing in the nucleus, TRα1 and TRβ1 shuttle rapidly between the nucleus and cytoplasm. We have identified multiple nuclear localization signals and nuclear export signals within TRα1 and TRβ1 that interact with importins and exportins, respectively, to mediate translocation across the nuclear envelope. More recently, enigmatic cytoplasmic functions have been ascribed to other TR subtypes, expanding the diversity of the cellular response to thyroid hormone. By integrating data on localization signal motifs, this review provides an overview of the complex interplay between TR's dynamic transport pathways and thyroid hormone signaling activities. We examine the variation in TR subtype response to thyroid hormone signaling, and what is currently known about regulation of the variety of tissue-specific localization patterns, including targeting to the nucleus, the mitochondria and the inner surface of the plasma membrane.

Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology

Background

Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. In addition, endogenous (e.g. genetic variation) and external (e.g. exposure to estrogen-like chemicals) factors are known to impact estrogen biology. To understand how these multiple factors interact to determine an individual’s response to therapy is complex, and may be best approached through a systems approach.

Methods

We present a physiologically-based pharmacokinetic model (PBPK) of estradiol, and validate it against plasma kinetics in humans following intravenous and oral exposure. We extend this model by replacing the intrinsic clearance term with: a detailed kinetic model of estrogen metabolism in the liver; or, a genome-scale model of liver metabolism. Both models were validated by their ability to reproduce clinical data on estradiol exposure. We hypothesise that the enhanced mechanistic information contained within these models will lead to more robust predictions of the biological phenotype that emerges from the complex interactions between estrogens and the body.

Results

To demonstrate the utility of these models we examine the known drug-drug interactions between phenytoin and oral estradiol. We are able to reproduce the approximate 50% reduction in area under the concentration-time curve for estradiol associated with this interaction. Importantly, the inclusion of a genome-scale metabolic model allows the prediction of this interaction without directly specifying it within the model. In addition, we predict that PXR activation by drugs results in an enhanced ability of the liver to excrete glucose. This has important implications for the relationship between drug treatment and metabolic syndrome.

Conclusions

We demonstrate how the novel coupling of PBPK models with genome-scale metabolic networks has the potential to aid prediction of drug action, including both drug-drug interactions and changes to the metabolic landscape that may predispose an individual to disease development.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0520-3) contains supplementary material, which is available to authorized users.

The Peculiar Glycolytic Pathway in Hyperthermophylic Archaea: Understanding Its Whims by Experimentation In Silico

Mathematical models are key to systems biology where they typically describe the topology and dynamics of biological networks, listing biochemical entities and their relationships with one another. Some (hyper)thermophilic Archaea contain an enzyme, called non-phosphorylating glyceraldehyde-3-phosphate dehydrogenase (GAPN), which catalyzes the direct oxidation of glyceraldehyde-3-phosphate to 3-phosphoglycerate omitting adenosine 5′-triphosphate (ATP) formation by substrate-level-phosphorylation via phosphoglycerate kinase. In this study we formulate three hypotheses that could explain functionally why GAPN exists in these Archaea, and then construct and use mathematical models to test these three hypotheses. We used kinetic parameters of enzymes of Sulfolobus solfataricus (S. solfataricus) which is a thermo-acidophilic archaeon that grows optimally between 60 and 90 °C and between pH 2 and 4. For comparison, we used a model of Saccharomyces cerevisiae (S. cerevisiae), an organism that can live at moderate temperatures. We find that both the first hypothesis, i.e., that the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plus phosphoglycerate kinase (PGK) route (the alternative to GAPN) is thermodynamically too much uphill and the third hypothesis, i.e., that GAPDH plus PGK are required to carry the flux in the gluconeogenic direction, are correct. The second hypothesis, i.e., that the GAPDH plus PGK route delivers less than the 1 ATP per pyruvate that is delivered by the GAPN route, is only correct when GAPDH reaction has a high rate and 1,3-bis-phosphoglycerate (BPG) spontaneously degrades to 3PG at a high rate.

MUFINS: multi-formalism interaction network simulator

Systems Biology has established numerous approaches for mechanistic modeling of molecular networks in the cell and a legacy of models. The current frontier is the integration of models expressed in different formalisms to address the multi-scale biological system organization challenge. We present MUFINS (MUlti-Formalism Interaction Network Simulator) software, implementing a unique set of approaches for multi-formalism simulation of interaction networks. We extend the constraint-based modeling (CBM) framework by incorporation of linear inhibition constraints, enabling for the first time linear modeling of networks simultaneously describing gene regulation, signaling and whole-cell metabolism at steady state. We present a use case where a logical hypergraph model of a regulatory network is expressed by linear constraints and integrated with a Genome-Scale Metabolic Network (GSMN) of mouse macrophage. We experimentally validate predictions, demonstrating application of our software in an iterative cycle of hypothesis generation, validation and model refinement. MUFINS incorporates an extended version of our Quasi-Steady State Petri Net approach to integrate dynamic models with CBM, which we demonstrate through a dynamic model of cortisol signaling integrated with the human Recon2 GSMN and a model of nutrient dynamics in physiological compartments. Finally, we implement a number of methods for deriving metabolic states from ~omics data, including our new variant of the iMAT congruency approach. We compare our approach with iMAT through the analysis of 262 individual tumor transcriptomes, recovering features of metabolic reprogramming in cancer. The software provides graphics user interface with network visualization, which facilitates use by researchers who are not experienced in coding and mathematical modeling environments.

Can a systems approach produce a better understanding of mood disorders?

BACKGROUND

One in twenty-five people suffer from a mood disorder. Current treatments are sub-optimal with poor patient response and uncertain modes-of-action. There is thus a need to better understand underlying mechanisms that determine mood, and how these go wrong in affective disorders. Systems biology approaches have yielded important biological discoveries for other complex diseases such as cancer, and their potential in affective disorders will be reviewed.

SCOPE OF REVIEW

This review will provide a general background to affective disorders, plus an outline of experimental and computational systems biology. The current application of these approaches in understanding affective disorders will be considered, and future recommendations made.

MAJOR CONCLUSIONS

Experimental systems biology has been applied to the study of affective disorders, especially at the genome and transcriptomic levels. However, data generation has been slowed by a lack of human tissue or suitable animal models. At present, computational systems biology has only be applied to understanding affective disorders on a few occasions. These studies provide sufficient novel biological insight to motivate further use of computational biology in this field.

GENERAL SIGNIFICANCE

In common with many complex diseases much time and money has been spent on the generation of large-scale experimental datasets. The next step is to use the emerging computational approaches, predominantly developed in the field of oncology, to leverage the most biological insight from these datasets. This will lead to the critical breakthroughs required for more effective diagnosis, stratification and treatment of affective disorders.

Glucocorticoid receptor isoforms direct distinct mitochondrial programs to regulate ATP production

The glucocorticoid receptor (GR), a nuclear receptor and major drug target, has a highly conserved minor splice variant, GRγ, which differs by a single arginine within the DNA binding domain. GRγ, which comprises 10% of all GR transcripts, is constitutively expressed and tightly conserved through mammalian evolution, suggesting an important non-redundant role. However, to date no specific role for GRγ has been reported. We discovered significant differences in subcellular localisation, and nuclear-cytoplasmic shuttling in response to ligand. In addition the GRγ transcriptome and protein interactome was distinct, and with a gene ontology signal for mitochondrial regulation which was confirmed using Seahorse technology. We propose that evolutionary conservation of the single additional arginine in GRγ is driven by a distinct, non-redundant functional profile, including regulation of mitochondrial function.

Inhibition of prenyltransferase activity by statins in both liver and muscle cell lines is not causative of cytotoxicity

As inhibitors of 3-hydroxy-3-methylglutaryl-CoA reductase, statins are an important first-line treatment for hypercholesterolemia. However, a recognized side-effect of statin therapy is myopathy, which in severe cases can present as potentially fatal rhabdomyolysis. This represents an important impediment to successful statin therapy, and despite decades of research the molecular mechanisms underlying this side-effect remain unclear. Current evidence supports a role for reduced levels of mevalonate pathway intermediates, with the most accepted hypothesis being a reduction in isoprenoids formation, leading to faulty post-translational modifications of membrane-associated proteins. We have undertaken a comprehensive analysis of the impact of nine statins on two human cell lines; Huh7 hepatoma and RD rhabdomyosarcoma. In both cell lines, concentration-dependent inhibition of prenylation was observed for cerivastatin and simvastatin, which could be rescued with the pathway intermediate mevalonate; in general, muscle cells were more sensitive to this effect, as measured by the levels of unprenylated Rap1A, a marker for prenylation by geranylgeranyl transferase I. Concentration-dependent toxicity was observed in both cell lines, with muscle cells again being more sensitive. Importantly, there was no correlation between inhibition of prenylation and cell toxicity, suggesting they are not causally linked. The lack of a causal relationship was confirmed by the absence of cytotoxicity in all cell lines following exposure to specific inhibitors of geranylgeranyl transferases I and II, and farnesyl transferase. As such, we provide strong evidence against the commonly accepted hypothesis linking inhibition of prenylation and statin-mediated toxicity, with the two processes likely to be simultaneous but independent.

An introduction to systems toxicology

Systems toxicology combines novel and historical experimental data to generate increasingly complex models of the biological response to chemical exposure.

A simple kinetic model with explicit predictions for nuclear transport

Molecular exchange between the cell nucleus and cytoplasm is one of the most fundamental features of eukaryotic cell biology. The nuclear pores act as a conduit of this transport, both for cargo that crosses the pore autonomously as well as that whose translocation requires an intermediary receptor. The major class of such receptors is regulated by the small GTPase Ran, via whose interaction the nucleo-cytoplasmic transport system functions as a selective molecular pump. We propose a simple analytical model for transport that includes both translocation and receptor binding kinetics. The model is suitable for steady-state kinetics such as fluorescence recovery after photobleaching. Time constants appear as a combination of parameters whose effects on measured kinetics are not separable. Competitive cargo binding to receptors and large cytoplasmic volume buffer the transport properties of any particular cargo. Specific limits to the solutions provide a qualitative insight and interpretation of nuclear transport in the cellular context. Most significantly, we find that under realistic conditions receptor binding, rather than permeability of the nuclear pores, may be rate-limiting for nucleo-cytoplasmic exchange.

Optimization of stress response through the nuclear receptor-mediated cortisol signalling network

It is an accepted paradigm that extended stress predisposes an individual to pathophysiology. However, the biological adaptations to minimize this risk are poorly understood. Using a computational model based upon realistic kinetic parameters we are able to reproduce the interaction of the stress hormone cortisol with its two nuclear receptors, the high-affinity glucocorticoid receptor and the low-affinity pregnane X-receptor. We demonstrate that regulatory signals between these two nuclear receptors are necessary to optimize the body's response to stress episodes, attenuating both the magnitude and duration of the biological response. In addition, we predict that the activation of pregnane X-receptor by multiple, low-affinity endobiotic ligands is necessary for the significant pregnane X-receptor-mediated transcriptional response observed following stress episodes. This integration allows responses mediated through both the high and low-affinity nuclear receptors, which we predict is an important strategy to minimize the risk of disease from chronic stress.

Higher nucleoporin-Importinβ affinity at the nuclear basket increases nucleocytoplasmic import

Several in vitro studies have shown the presence of an affinity gradient in nuclear pore complex proteins for the import receptor Importinβ, at least partially contributing to nucleocytoplasmic transport, while others have historically argued against the presence of such a gradient. Nonetheless, the existence of an affinity gradient has remained an uncharacterized contributing factor. To shed light on the affinity gradient theory and better characterize how the existence of such an affinity gradient between the nuclear pore and the import receptor may influence the nucleocytoplasmic traffic, we have developed a general-purpose agent based modeling (ABM) framework that features a new method for relating rate constants to molecular binding and unbinding probabilities, and used our ABM approach to quantify the effects of a wide range of forward and reverse nucleoporin-Importinβ affinity gradients. Our results indicate that transport through the nuclear pore complex is maximized with an effective macroscopic affinity gradient of 2000 µM, 200 µM and 10 µM in the cytoplasmic, central channel and nuclear basket respectively. The transport rate at this gradient is approximately 10% higher than the transport rate for a comparable pore lacking any affinity gradient, which has a peak transport rate when all nucleoporins have an affinity of 200 µM for Importinβ. Furthermore, this optimal ratio of affinity gradients is representative of the ratio of affinities reported for the yeast nuclear pore complex – suggesting that the affinity gradient seen in vitro is highly optimized.

Understanding complexity in neurodegenerative diseases: in silico reconstruction of emergence

Healthy functioning is an emergent property of the network of interacting biomolecules that comprise an organism. It follows that disease (a network shift that causes malfunction) is also an emergent property, emerging from a perturbation of the network. On the one hand, the biomolecular network of every individual is unique and this is evident when similar disease-producing agents cause different individual pathologies. Consequently, a personalized model and approach for every patient may be required for therapies to become effective across mankind. On the other hand, diverse combinations of internal and external perturbation factors may cause a similar shift in network functioning. We offer this as an explanation for the multi-factorial nature of most diseases: they are "systems biology diseases," or "network diseases." Here we use neurodegenerative diseases, like Parkinson's disease (PD), as an example to show that due to the inherent complexity of these networks, it is difficult to understand multi-factorial diseases with simply our "naked brain." When describing interactions between biomolecules through mathematical equations and integrating those equations into a mathematical model, we try to reconstruct the emergent properties of the system in silico. The reconstruction of emergence from interactions between huge numbers of macromolecules is one of the aims of systems biology. Systems biology approaches enable us to break through the limitation of the human brain to perceive the extraordinarily large number of interactions, but this also means that we delegate the understanding of reality to the computer. We no longer recognize all those essences in the system's design crucial for important physiological behavior (the so-called "design principles" of the system). In this paper we review evidence that by using more abstract approaches and by experimenting in silico, one may still be able to discover and understand the design principles that govern behavioral emergence.

Characterization of rhodamine-123 as a tracer dye for use in in vitro drug transport assays

Fluorescent tracer dyes represent an important class of sub-cellular probes and allow the examination of cellular processes in real-time with minimal impact upon these processes. Such tracer dyes are becoming increasingly used for the examination of membrane transport processes, as they are easy-to-use, cost effective probe substrates for a number of membrane protein transporters. Rhodamine 123, a member of the rhodamine family of flurone dyes, has been used to examine membrane transport by the ABCB1 gene product, MDR1. MDR1 is viewed as the archetypal drug transport protein, and is able to efflux a large number of clinically relevant drugs. In addition, ectopic activity of MDR1 has been associated with the development of multiple drug resistance phenotype, which results in a poor patient response to therapeutic intervention. It is thus important to be able to examine the potential for novel compounds to be MDR1 substrates. Given the increasing use rhodamine 123 as a tracer dye for MDR1, a full characterisation of its spectral properties in a range of in vitro assay-relevant media is warranted. Herein, we determine λmax for excitation and emission or rhodamine 123 and its metabolite rhodamine 110 in commonly used solvents and extraction buffers, demonstrating that fluorescence is highly dependent on the chemical environment: Optimal parameters are 1% (v/v) methanol in HBSS, with λex = 505 nm, λem = 525 nm. We characterise the uptake of rhodamine 123 into cells, via both passive and active processes, and demonstrate that this occurs primarily through OATP1A2-mediated facilitated transport at concentrations below 2 µM, and via micelle-mediated passive diffusion above this. Finally, we quantify the intracellular sequestration and metabolism of rhodamine 123, demonstrating that these are both cell line-dependent factors that may influence the interpretation of transport assays.

The virtual liver: a multidisciplinary, multilevel challenge for systems biology

The liver is the central metabolic organ in human physiology, with functions that are fundamentally important to the detoxification of xenobiotics (drugs), the maintenance of homeostasis of numerous blood metabolites, and the production of mediators of the acute phase response. Liver toxicity, whether actual or implied is the reason for the failure of a significant proportion of many promising novel medicines that consequently never reach the market, and diseases such as atherosclerosis, diabetes, and fatty liver diseases, that are a major burden on current health resources, are directly linked to functional and structural disorders of the liver. This article presents the concepts and approaches underpinning one of the most exciting and ambitious modeling projects in the field of systems biology and systems medicine. This major multidisciplinary research program is aimed at developing a whole-organ model of the human liver, representing its central physiological functions under normal and pathological conditions The model will be composed of a larger battery of interconnected submodels representing liver anatomy and physiology, integrating processes across hierarchical levels in space, time, and structural organization. In this article, we outline the general architecture of the liver model and present first step taken to reach this ambitious goal.

Pharmacology and clinical use of sex steroid hormone receptor modulators

Sex steroid receptors are ligand-triggered transcription factors. Oestrogen, progesterone and androgen receptors form, together with the glucocorticoid and mineralocorticoid receptors, a subgroup of the superfamily of nuclear receptors. They share a common mode of action, namely translating a hormone-i.e. a small-molecule signal-from outside to changes in gene expression and cell fate, and thereby represent "natural" pharmacological targets.For pharmacological therapy, these receptors have originally been addressed by hormones and synthetic hormone analogues in order to overcome pathologies related to deficiencies in the natural ligands. Another major use for female sex hormone receptor modulators is oral contraception, i.e. birth control.On the other side, blocking the activity of sex steroid receptors has become an established way to treat hormone-dependent malignancies, such as breast and prostate cancer.In this review, we will discuss how the experience gained from the classical pharmacology of these receptors and their molecular similarities led to new options for the treatment of gender-specific diseases and highlight recent progress in medicinal chemistry of sex hormone-modulating drugs.

Emergence of the silicon human and network targeting drugs

The development of disease may be characterized as a pathological shift of homeostasis; the main goal of contemporary drug treatment is, therefore, to return the pathological homeostasis back to the normal physiological range. From the view point of systems biology, homeostasis emerges from the interactions within the network of biomolecules (e.g. DNA, mRNA, proteins), and, hence, understanding how drugs impact upon the entire network should improve their efficacy at returning the network (body) to physiological homeostasis. Large, mechanism-based computer models, such as the anticipated human whole body models (silicon or virtual human), may help in the development of such network-targeting drugs. Using the philosophical concept of weak and strong emergence, we shall here take a more general look at the paradigm of network-targeting drugs, and propose our approaches to scale the strength of strong emergence. We apply these approaches to several biological examples and demonstrate their utility to reveal principles of bio-modeling. We discuss this in the perspective of building the silicon human.