Plasticity of TGF-β signaling

Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer

Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Multiscale modeling of collective cell migration elucidates the mechanism underlying tumor-stromal interactions in different spatiotemporal scales

Metastasis is the pathogenic spread of cancer cells from a primary tumor to a secondary site which happens at the late stages of cancer. It is caused by a variety of biological, chemical, and physical processes, such as molecular interactions, intercellular communications, and tissue-level activities. Complex interactions of cancer cells with their microenvironment components such as cancer associated fibroblasts (CAFs) and extracellular matrix (ECM) cause them to adopt an invasive phenotype that promotes tumor growth and migration. This paper presents a multiscale model for integrating a wide range of time and space interactions at the molecular, cellular, and tissue levels in a three-dimensional domain. The modeling procedure starts with presenting nonlinear dynamics of cancer cells and CAFs using ordinary differential equations based on TGFβ, CXCL12, and LIF signaling pathways. Unknown kinetic parameters in these models are estimated using hybrid unscented Kalman filter and the models are validated using experimental data. Then, the principal role of CAFs on metastasis is revealed by spatial-temporal modeling of circulating signals throughout the TME. At this stage, the model has evolved into a coupled ODE-PDE system that is capable of determining cancer cells' status in one of the quiescent, proliferating or migratory conditions due to certain metastasis factors and ECM characteristics. At the tissue level, we consider a force-based framework to model the cancer cell proliferation and migration as the final step towards cancer cell metastasis. The ability of the multiscale model to depict cancer cells' behavior in different levels of modeling is confirmed by comparing its outputs with the results of RT PCR and wound scratch assay techniques. Performance evaluation of the model indicates that the proposed multiscale model can pave the way for improving the efficiency of therapeutic methods in metastasis prevention.

Data-based stochastic modeling reveals sources of activity bursts in single-cell TGF-β signaling

Cells sense their surrounding by employing intracellular signaling pathways that transmit hormonal signals from the cell membrane to the nucleus. TGF-β/SMAD signaling encodes various cell fates, controls tissue homeostasis and is deregulated in diseases such as cancer. The pathway shows strong heterogeneity at the single-cell level, but quantitative insights into mechanisms underlying fluctuations at various time scales are still missing, partly due to inefficiency in the calibration of stochastic models that mechanistically describe signaling processes. In this work we analyze single-cell TGF-β/SMAD signaling and show that it exhibits temporal stochastic bursts which are dose-dependent and whose number and magnitude correlate with cell migration. We propose a stochastic modeling approach to mechanistically describe these pathway fluctuations with high computational efficiency. Employing high-order numerical integration and fitting to burst statistics we enable efficient quantitative parameter estimation and discriminate models that assume noise in different reactions at the receptor level. This modeling approach suggests that stochasticity in the internalization of TGF-β receptors into endosomes plays a key role in the observed temporal bursting. Further, the model predicts the single-cell dynamics of TGF-β/SMAD signaling in untested conditions, e.g., successfully reflects memory effects of signaling noise and cellular sensitivity towards repeated stimulation. Taken together, our computational framework based on burst analysis, noise modeling and path computation scheme is a suitable tool for the data-based modeling of complex signaling pathways, capable of identifying the source of temporal noise.

Fluctuations in molecular networks give rise to heterogeneity in cellular behavior and therefore promote the diversification of tissues. For a better understanding of cellular decision making, it is important to identify sources of molecular fluctuations and to quantitatively describe them by predictive mathematical models. In this work, we focused on temporal fluctuations of the TGF-β signaling pathway that is important for controlling cell division and migration.

We characterized a single-cell dataset comprising hundreds of cells using time series analysis and a large-scale stochastic model. By fitting several model variants to the data, we identified the stochastic internalization of cell surface receptors into endosomes as a main source of temporal fluctuations (’bursts’) in the signaling pathway. The corresponding model accurately predicted novel experimental data, and provided insights into the long-term memory of signaling fluctuations. In summary, we propose a modeling approach to quantitatively describe heterogeneous behavior in large-scale single-cell datasets and to identify the underlying biological mechanisms.

Synthesis of the Dynamical Properties of Feedback Loops in Bio-Pathways

Feedback loops regulate various biological functions such as oscillations, bistability, and robustness. They play a significant role in developmental signalling and failure of feedback can lead to disease. Systematic analysis of feedback loops could be useful in understanding their properties and biological effects. We propose here a method to automatically analyze feedback loops in bio-pathways and synthesize temporal logic properties which describe their dynamics. Starting with an ordinary differential equations (ODEs) based model of a bio-pathway, for a chosen feedback loop present in the pathway, we use a convolutional neural network to classify the behaviours of the key components of the feedback according to templates specified in bounded linear temporal logic (BLTL). Once a template has been identified, we instantiate the symbolic variables appearing in the template and synthesize properties using a parameter estimation procedure based on sequential hypothesis testing. We have applied this framework to a number of bio-pathway models and validated that the synthesized properties faithfully describe the behaviours of the feedback loops.

The double-edged sword role of fibroblasts in the interaction with cancer cells; an agent-based modeling approach

Fibroblasts as key components of tumor microenvironment show different features in the interaction with cancer cells. Although, Normal fibroblasts demonstrate anti-tumor effects, cancer associated fibroblasts are principal participant in tumor growth and invasion. The ambiguity of fibroblasts function can be regarded as two heads of its behavioral spectrum and can be subjected for mathematical modeling to identify their switching behavior. In this research, an agent-based model of mutual interactions between fibroblast and cancer cell was created. The proposed model is based on nonlinear differential equations which describes biochemical reactions of the main factors involved in fibroblasts and cancer cells communication. Also, most of the model parameters are estimated using hybrid unscented Kalman filter. The interactions between two cell types are illustrated by the dynamic modeling of TGFβ and LIF pathways as well as their crosstalk. Using analytical and computational approaches, reciprocal effects of cancer cells and fibroblasts are constructed and the role of signaling molecules in tumor progression or prevention are determined. Finally, the model is validated using a set of experimental data. The proposed dynamic modeling might be useful for designing more efficient therapies in cancer metastasis treatment and prevention.

GP73 promotes invasion and metastasis of bladder cancer by regulating the epithelial-mesenchymal transition through the TGF-β1/Smad2 signalling pathway

This study investigated the effects of Golgi membrane protein 73 (GP73) on the epithelial-mesenchymal transition (EMT) and on bladder cancer cell invasion and metastasis through the TGF-β1/Smad2 signalling pathway. Paired bladder cancer and adjacent tissue samples (102) and normal bladder tissue samples (106) were obtained. Bladder cancer cell lines (T24, 5637, RT4, 253J and J82) were selected and assigned to blank, negative control (NC), TGF-β, thrombospondin-1 (TSP-1), TGF-β1+ TSP-1, GP73-siRNA-1, GP73-siRNA-2, GP73-siRNA-1+ TSP-1, GP73-siRNA-1+ pcDNA-GP73, WT1-siRNA and WT1-siRNA + GP73-siRNA-1 groups. Expressions of GP73, TGF-β1, Smad2, p-Smad2, E-cadherin and vimentin were detected using RT-qPCR and Western blotting. Cell proliferation, migration and invasion were determined using MTT assay, scratch testing and Transwell assay, respectively. Compared with the blank and NC groups, levels of GP73, TGF-β1, Smad2, p-Smad2, N-cadherin and vimentin decreased, and levels of WT1 and E-cadherin increased in the GP73-siRNA-1 and GP73-siRNA-2 groups, while the opposite results were observed in the WT1 siRNA, TGF-β, TSP-1 and TGF-β + TSP-1 groups. Cell proliferation, migration and invasion notably decreased in the GP73-siRNA-1 and GP73-siRNA-2 groups in comparison with the blank and NC groups, while in the WT1 siRNA, TGF-β, TSP-1 and TGF-β + TSP-1 groups, cell migration, invasion and proliferation showed the reduction after the EMT. These results suggest that GP73 promotes bladder cancer invasion and metastasis by inducing the EMT through down-regulating WT1 levels and activating the TGF-β1/Smad2 signalling pathway.

Demystifying the cytokine network: Mathematical models point the way

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

A systems biology approach to defining regulatory mechanisms for cartilage and tendon cell phenotypes

Phenotypic plasticity of adult somatic cells has provided emerging avenues for the development of regenerative therapeutics. In musculoskeletal biology the mechanistic regulatory networks of genes governing the phenotypic plasticity of cartilage and tendon cells has not been considered systematically. Additionally, a lack of strategies to effectively reproduce in vitro functional models of cartilage and tendon is retarding progress in this field. De- and redifferentiation represent phenotypic transitions that may contribute to loss of function in ageing musculoskeletal tissues. Applying a systems biology network analysis approach to global gene expression profiles derived from common in vitro culture systems (monolayer and three-dimensional cultures) this study demonstrates common regulatory mechanisms governing de- and redifferentiation transitions in cartilage and tendon cells. Furthermore, evidence of convergence of gene expression profiles during monolayer expansion of cartilage and tendon cells, and the expression of key developmental markers, challenges the physiological relevance of this culture system. The study also suggests that oxidative stress and PI3K signalling pathways are key modulators of in vitro phenotypes for cells of musculoskeletal origin.

Cancer stem cell-vascular endothelial cell interactions in glioblastoma

Glioblastoma (GBM), a higher grade glial tumor, is highly aggressive, therapy resistant and often shows poor patient prognosis due to frequent recurrence. These features of GBM are attributed to presence of a significantly smaller proportion of glioma stem cells (GSCs) that are endowed with self-renewal ability, multi-potent nature and show resistance to therapy in patients. GSCs preferably take shelter close to tumor vasculature due to paracrine need of soluble factors secreted by endothelial cells (ECs) of vasculature. The physical proximity of GSCs to ECs creates a localized perivascular niche where mutual GSC-EC interactions regulate GSC stemness, migration, therapy resistance, and cellular kinetics during tumor growth. Together, perivascular niche presents a therapeutically targetable tumor structure for clinical management of GBM. Thus, understanding cellular and non-cellular components in perivascular niche is vital for designing in vitro and in vivo GBM tumor models. Here, we discuss the components and structure of tumor vascular niche and its impact on tumor progression.

Automated design of programmable enzyme-driven DNA circuits

Molecular programming allows for the bottom-up engineering of biochemical reaction networks in a controlled in vitro setting. These engineered biochemical reaction networks yield important insight in the design principles of biological systems and can potentially enrich molecular diagnostic systems. The DNA polymerase-nickase-exonuclease (PEN) toolbox has recently been used to program oscillatory and bistable biochemical networks using a minimal number of components. Previous work has reported the automatic construction of in silico descriptions of biochemical networks derived from the PEN toolbox, paving the way for generating networks of arbitrary size and complexity in vitro. Here, we report an automated approach that further bridges the gap between an in silico description and in vitro realization. A biochemical network of arbitrary complexity can be globally screened for parameter values that display the desired function and combining this approach with robustness analysis further increases the chance of successful in vitro implementation. Moreover, we present an automated design procedure for generating optimal DNA sequences, exhibiting key characteristics deduced from the in silico analysis. Our in silico method has been tested on a previously reported network, the Oligator, and has also been applied to the design of a reaction network capable of displaying adaptation in one of its components. Finally, we experimentally characterize unproductive sequestration of the exonuclease to phosphorothioate protected ssDNA strands. The strong nonlinearities in the degradation of active components caused by this unintended cross-coupling are shown computationally to have a positive effect on adaptation quality.

Literature-based automated reconstruction, expansion, and refinement of the TGF-β superfamily ligand-receptor network

The TGF-β pathway transduces a variety of extracellular signals into intracellular responses that control multiple cellular processes, including cell growth, apoptosis, and differentiation. It encompasses 33 ligands that interact with 7 type II receptors and 5 type I receptors at the plasma membrane to potentially form 1,155 ligand-receptor complexes in mammalian cells. Retrieving the information of the complexes that are actually formed from reading the literature might be tedious and prone to missing links. Here, we have developed an automated literature-mining procedure to obtain the interactions of the TGF-β ligand-receptor network. By querying the Information Hyperlinked over Proteins (iHOP) online service and processing the results, we were able to find pairwise interactions between ligands and receptors that allowed us to build the network automatically from the literature. Comparison with available published review papers indicates that this method is able to automatically reconstruct and expand the TGF-β superfamily ligand-receptor network. Retrieving and parsing the full text of the manuscripts containing the interactions allowed us to refine the network interactions for specific cell lines.

In silico identification of potential therapeutic targets in the TGF-β signal transduction pathway

The transforming growth factor-β (TGF-β) superfamily of cytokines controls fundamental cellular processes, such as proliferation, motility, differentiation, and apoptosis. This fundamental role is emphasized by the widespread presence of mutations of the core components of the TGF-β signal transduction pathway in a number of human diseases. Therefore, there is an increasing interest in the development of therapies to specifically target this pathway. Here we develop a computational approach to identify potential intervention points that are capable of restoring the normal signaling dynamics to the mutated system while maintaining the behavior of normal cells substantially unperturbed. We apply this approach explicitly to the TGF-β pathway to study the signaling dynamics of mutated and normal cells treated with inhibitory drugs and identify the processes in the pathway that are most susceptible to therapeutic intervention.

Characterization of negative feedback network motifs in the TGF-β signaling pathway

{Chung, 2009 #1}The transforming growth factor-β (TGF-β) superfamily of cytokines plays a fundamental role in a wide variety of cellular processes, including growth, differentiation, apoptosis, and tissue homeostasis. Its relevance is emphasized by the mutations of its core components that are associated with diverse human diseases, such as cancer and cardiovascular pathologies. A prominent regulator of the pathway is Smad7, which attenuates the signal and controls its duration in a cell-type-dependent manner through a negative feedback loop. Here, we characterize all the potential Smad7-mediated negative feedback network motifs and investigate their effects on the signaling dynamics upon stimulation with TGF-β and bone morphogenetic protein (BMP) ligands. The results show that the specific negative feedback implementation is a key determinant of both the response of the system to single and multiple ligands of the TGF-β superfamily and its robustness and sensitivity to parameter perturbations.

Prediction stability in a data-based, mechanistic model of σF regulation during sporulation in Bacillus subtilis

Mathematical modeling of biological networks can help to integrate a large body of information into a consistent framework, which can then be used to arrive at novel mechanistic insight and predictions. We have previously developed a detailed, mechanistic model for the regulation of σ ^F during sporulation in Bacillus subtilis. The model was based on a wide range of quantitative data, and once fitted to the data, the model made predictions that could be confirmed in experiments. However, the analysis was based on a single optimal parameter set. We wondered whether the predictions of the model would be stable for all optimal parameter sets. To that end we conducted a global parameter screen within the physiological parameter ranges. The screening approach allowed us to identify sensitive and sloppy parameters, and highlighted further required datasets during the optimization. Eventually, all parameter sets that reproduced all available data predicted the physiological situation correctly.

Computational modelling of Smad-mediated negative feedback and crosstalk in the TGF-β superfamily network

The transforming growth factor-β (TGF-β) signal transduction pathway controls many cellular processes, including differentiation, proliferation and apoptosis. It plays a fundamental role during development and it is dysregulated in many diseases. The factors that control the dynamics of the pathway, however, are not fully elucidated yet and so far computational approaches have been very limited in capturing the distinct types of behaviour observed under different cellular backgrounds and conditions into a single-model description. Here, we develop a detailed computational model for TGF-β signalling that incorporates elements of previous models together with crosstalking between Smad1/5/8 and Smad2/3 channels through a negative feedback loop dependent on Smad7. The resulting model accurately reproduces the diverse behaviour of experimental datasets for human keratinocytes, bovine aortic endothelial cells and mouse mesenchymal cells, capturing the dynamics of activation and nucleocytoplasmic shuttling of both R-Smad channels. The analysis of the model dynamics and its system properties revealed Smad7-mediated crosstalking between Smad1/5/8 and Smad2/3 channels as a major determinant in shaping the distinct responses to single and multiple ligand stimulation for different cell types.

Tgf-β1 inhibits Cftr biogenesis and prevents functional rescue of ΔF508-Cftr in primary differentiated human bronchial epithelial cells

CFTR is an integral transmembrane glycoprotein and a cAMP-activated Cl(-) channel. Mutations in the CFTR gene lead to Cystic Fibrosis (CF)-an autosomal recessive disease with majority of the morbidity and mortality resulting from airway infection, inflammation, and fibrosis. The most common disease-associated mutation in the CFTR gene-deletion of Phe508 (ΔF508) leads to a biosynthetic processing defect of CFTR. Correction of the defect and delivery of ΔF508-CFTR to the cell surface has been highly anticipated as a disease modifying therapy. Compared to promising results in cultured cell this approach was much less effective in CF patients in an early clinical trial. Although the cause of failure to rescue ΔF508-CFTR in the clinical trial has not been determined, presence of factor(s) that interfere with the rescue in vivo could be considered. The cytokine TGF-β1 is frequently elevated in CF patients. TGF-β1 has pleiotropic effects in different disease models and genetic backgrounds and little is known about TGF-β1 effects on CFTR in human airway epithelial cells. Moreover, there are no published studies examining TGF-β1 effects on the functional rescue of ΔF508-CFTR. Here we found that TGF-β1 inhibits CFTR biogenesis by reducing mRNA levels and protein abundance in primary differentiated human bronchial epithelial (HBE) cells from non-CF individuals. TGF-β1 inhibits CFTR biogenesis without compromising the epithelial phenotype or integrity of HBE cells. TGF-β1 also inhibits biogenesis and impairs the functional rescue of ΔF508-CFTR in HBE cells from patients homozygous for the ΔF508 mutation. Our data indicate that activation of TGF-β1 signaling may inhibit CFTR function in non-CF individuals and may interfere with therapies directed at correcting the processing defect of ΔF508-CFTR in CF patients.

Smad2 and Smad3 cooperate and antagonize simultaneously in vertebrate neurogenesis

The transforming growth factor beta (TGF-β) pathway plays key roles in development and cancer. (TGF-β) signaling converges on the Smad2 and Smad3 effectors, which can either cooperate or antagonize to regulate their transcriptional targets. Here we performed in vivo and in silico experiments to study how such cooperativity and antagonism might function during neurogenesis. In vivo electroporation experiments in the chick embryo neural tube show that Smad2 and Smad3 cooperate to promote neurogenesis, as well as the transcription of Smad3 specific targets. Smad2 knockdown enhances neurogenesis and the transcription of Smad3 specific targets. A mathematical model of the TGF-β pathway fits the experimental results and predicts that the proportions of the three different trimeric complexes formed dictates the transcriptional responses of the R-Smads. As such, Smad2 targets are activated solely by the Smad2-Smad2-Smad4 complex, while Smad3 targets are activated both by Smad2-Smad3 Smad4 and Smad3- Smad3-Smad4 trimers. Since we have modeled the Smad responses onto arbitrary genes, we propose that this mechanism might be extended to additional activities of TGF-β in development and disease.

Dynamics of TGF-β/Smad signaling

The physiological responses to TGF-β stimulation are diverse and vary amongst different cell types and environmental conditions. Even though the principal molecular components of the canonical and the non-canonical TGF-β signaling pathways have been largely identified, the mechanism that underlies the well-established context dependent physiological responses remains a mystery. Understanding how the components of TGF-β signaling function as a system and how this system functions in the context of the global cellular regulatory network requires a more quantitative and systematic approach. Here, we review the recent progress in understanding TGF-β biology using integration of mathematical modeling and quantitative experimental analysis. These studies reveal many interesting dynamics of TGF-β signaling and how cells quantitatively decode variable doses of TGF-β stimulation.

Inferring Biological Mechanisms by Data-Based Mathematical Modelling: Compartment-Specific Gene Activation during Sporulation in Bacillus subtilis as a Test Case

Biological functionality arises from the complex interactions of simple components. Emerging behaviour is difficult to recognize with verbal models alone, and mathematical approaches are important. Even few interacting components can give rise to a wide range of different responses, that is, sustained, transient, oscillatory, switch-like responses, depending on the values of the model parameters. A quantitative comparison of model predictions and experiments is therefore important to distinguish between competing hypotheses and to judge whether a certain regulatory behaviour is at all possible and plausible given the observed type and strengths of interactions and the speed of reactions. Here I will review a detailed model for the transcription factor σ(F), a regulator of cell differentiation during sporulation in Bacillus subtilis. I will focus in particular on the type of conclusions that can be drawn from detailed, carefully validated models of biological signaling networks. For most systems, such detailed experimental information is currently not available, but accumulating biochemical data through technical advances are likely to enable the detailed modelling of an increasing number of pathways. A major challenge will be the linking of such detailed models and their integration into a multiscale framework to enable their analysis in a larger biological context.