Resolving the Combinatorial Complexity of Smad Protein Complex Formation and Its Link to Gene Expression

TGF-β signalling limits effector function capacity of NK cell anti-tumour immunity in human bladder cancer

BACKGROUND

Natural killer (NK) cells are important innate immunity players and have unique abilities to recognize and eliminate cancer cells, particularly in settings of antibody-opsonization and antibody-dependant cellular cytotoxicity (ADCC). However, NK cell-based responses in bladder cancers to therapeutic antibodies are typically immunosuppressed, and these immunosuppressive mechanisms are largely unknown.

METHODS

Single cell RNA sequencing (scRNA-seq) and high-dimensional flow cytometry were used to investigate the phenotype of tumour-infiltrating NK cells in patients with bladder cancer. Further, in vitro, and in vivo models of this disease were used to validate these findings.

FINDINGS

NK cells within bladder tumours displayed reduced expression of FcγRIIIa/CD16, the critical Fc receptor involved in ADCC-mediated cytotoxicity, on both transcriptional and protein levels. Transcriptional signatures of transforming growth factor (TGF)-β-signalling, a pleiotropic cytokine known for its immunosuppressive and tissue residency-inducing effects, were upregulated in tumour-infiltrating NK cells. TGF-β mediated CD16 downregulation on NK cells, was further validated in vitro, which was accompanied by a transition into a tissue residency phenotype. This CD16 downregulation was also abrogated by TGF-βR signalling inhibition, which could also restore the ADCC ability of NK cells subject to TGF-β effects. In a humanized mouse model of bladder cancer, mice treated with a TGF-β inhibitor exhibited increased ADCC activity compared to mice treated only with antibodies.

INTERPRETATION

This study highlights how TGF-β-rich bladder cancers inhibit NK cell-mediated ADCC by downregulating CD16. TGF-β inhibition represents new avenues to reverse immunosuppression and enhance the tumoricidal capacity of NK cells in bladder cancer.

FUNDING

The Guimaraes Laboratory is funded by a US Department of Defense-Breast Cancer Research Program-Breakthrough Award Level 1 (#BC200025), a grant (#2019485) awarded through the Medical Research Future Fund (MRFF, with the support of the Queensland Children's Hospital Foundation, Microba Life Sciences, Richie's Rainbow Foundation, Translational Research Institute (TRI) and UQ), and a grant (#RSS_2023_085) funded by a Metro South Health Research Support Scheme. J.K.M.W. is funded by a UQ Research Training Program PhD Scholarship and N.O. is funded by a NHMRC Postgraduate Scholarship (#2021932).

Positive and negative feedback regulation of the TGF-β1 explains two equilibrium states in skin aging

During aging, skin homeostasis is essential for maintaining appearance, as well as biological defense of the human body. In this study, we identified thrombospondin-1 (THBS1) and fibromodulin (FMOD) as positive and negative regulators, respectively, of the TGF-β1-SMAD4 axis in human skin aging, based on in vitro and in vivo omics analyses and mathematical modeling. Using transcriptomic and epigenetic analyses of senescent dermal fibroblasts, TGF-β1 was identified as the key upstream regulator. Bifurcation analysis revealed a binary high-/low-TGF-β1 switch, with THBS1 as the main controller. Computational simulation of the TGF-β1 signaling pathway indicated that THBS1 expression was sensitively regulated, whereas FMOD was regulated robustly. Results of sensitivity analysis and validation showed that inhibition of SMAD4 complex formation was a promising method to control THBS1 production and senescence. Therefore, this study demonstrated the potential of combining data-driven target discovery with mathematical approaches to determine the mechanisms underlying skin aging.

Liebig's law of the minimum in the TGF-β/SMAD pathway

Cells use signaling pathways to sense and respond to their environments. The transforming growth factor-β (TGF-β) pathway produces context-specific responses. Here, we combined modeling and experimental analysis to study the dependence of the output of the TGF-β pathway on the abundance of signaling molecules in the pathway. We showed that the TGF-β pathway processes the variation of TGF-β receptor abundance using Liebig's law of the minimum, meaning that the output-modifying factor is the signaling protein that is most limited, to determine signaling responses across cell types and in single cells. We found that the abundance of either the type I (TGFBR1) or type II (TGFBR2) TGF-β receptor determined the responses of cancer cell lines, such that the receptor with relatively low abundance dictates the response. Furthermore, nuclear SMAD2 signaling correlated with the abundance of TGF-β receptor in single cells depending on the relative expression levels of TGFBR1 and TGFBR2. A similar control principle could govern the heterogeneity of signaling responses in other signaling pathways.

Receptor-activated transcription factors and beyond: multiple modes of Smad2/3-dependent transmission of TGF-β signaling

Transforming growth factor β (TGF-β) is a pleiotropic cytokine that is widely distributed throughout the body. Its receptor proteins, TGF-β type I and type II receptors, are also ubiquitously expressed. Therefore, the regulation of various signaling outputs in a context-dependent manner is a critical issue in this field. Smad proteins were originally identified as signal-activated transcription factors similar to signal transducer and activator of transcription proteins. Smads are activated by serine phosphorylation mediated by intrinsic receptor dual specificity kinases of the TGF-β family, indicating that Smads are receptor-restricted effector molecules downstream of ligands of the TGF-β family. Smad proteins have other functions in addition to transcriptional regulation, including post-transcriptional regulation of micro-RNA processing, pre-mRNA splicing, and m6A methylation. Recent technical advances have identified a novel landscape of Smad-dependent signal transduction, including regulation of mitochondrial function without involving regulation of gene expression. Therefore, Smad proteins are receptor-activated transcription factors and also act as intracellular signaling modulators with multiple modes of function. In this review, we discuss the role of Smad proteins as receptor-activated transcription factors and beyond. We also describe the functional differences between Smad2 and Smad3, two receptor-activated Smad proteins downstream of TGF-β, activin, myostatin, growth and differentiation factor (GDF) 11, and Nodal.

TGF-β signaling in health, disease, and therapeutics

Transforming growth factor (TGF)-β is a multifunctional cytokine expressed by almost every tissue and cell type. The signal transduction of TGF-β can stimulate diverse cellular responses and is particularly critical to embryonic development, wound healing, tissue homeostasis, and immune homeostasis in health. The dysfunction of TGF-β can play key roles in many diseases, and numerous targeted therapies have been developed to rectify its pathogenic activity. In the past decades, a large number of studies on TGF-β signaling have been carried out, covering a broad spectrum of topics in health, disease, and therapeutics. Thus, a comprehensive overview of TGF-β signaling is required for a general picture of the studies in this field. In this review, we retrace the research history of TGF-β and introduce the molecular mechanisms regarding its biosynthesis, activation, and signal transduction. We also provide deep insights into the functions of TGF-β signaling in physiological conditions as well as in pathological processes. TGF-β-targeting therapies which have brought fresh hope to the treatment of relevant diseases are highlighted. Through the summary of previous knowledge and recent updates, this review aims to provide a systematic understanding of TGF-β signaling and to attract more attention and interest to this research area.

Targeting SMAD-Dependent Signaling: Considerations in Epithelial and Mesenchymal Solid Tumors

SMADs are the canonical intracellular effector proteins of the TGF-β (transforming growth factor-β). SMADs translocate from plasma membrane receptors to the nucleus regulated by many SMAD-interacting proteins through phosphorylation and other post-translational modifications that govern their nucleocytoplasmic shuttling and subsequent transcriptional activity. The signaling pathway of TGF-β/SMAD exhibits both tumor-suppressing and tumor-promoting phenotypes in epithelial-derived solid tumors. Collectively, the pleiotropic nature of TGF-β/SMAD signaling presents significant challenges for the development of effective cancer therapies. Here, we review preclinical studies that evaluate the efficacy of inhibitors targeting major SMAD-regulating and/or -interacting proteins, particularly enzymes that may play important roles in epithelial or mesenchymal compartments within solid tumors.

A computational model of the DNA damage-induced IKK/ NF-κB pathway reveals a critical dependence on irradiation dose and PARP-1

The activation of IKK/NF-κB by genotoxic stress is a crucial process in the DNA damage response. Due to the anti-apoptotic impact of NF-κB, it can affect cell-fate decisions upon DNA damage and therefore interfere with tumor therapy-induced cell death. Here, we developed a dynamical model describing IKK/NF-κB signaling that faithfully reproduces quantitative time course data and enables a detailed analysis of pathway regulation. The approach elucidates a pathway topology with two hubs, where the first integrates signals from two DNA damage sensors and the second forms a coherent feedforward loop. The analyses reveal a critical role of the sensor protein PARP-1 in the pathway regulation. Introducing a method for calculating the impact of changes in individual components on pathway activity in a time-resolved manner, we show how irradiation dose influences pathway activation. Our results give a mechanistic understanding relevant for the interpretation of experimental and clinical studies.

Uncovering specific mechanisms across cell types in dynamical models

Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to gain insights into the underlying biological processes. Regularization techniques have been proposed and applied to identify mechanisms specific to two cell types, e.g., healthy and cancer cells, including the LASSO (least absolute shrinkage and selection operator). However, when analyzing more than two cell types, these approaches are not consistent, and require the selection of a reference cell type, which can affect the results. To make the regularization approach applicable to identifying cell-type specific mechanisms in any number of cell types, we propose to incorporate the clustered LASSO into the framework of ordinary differential equation modeling by penalizing the pairwise differences of the logarithmized fold-change parameters encoding a specific mechanism in different cell types. The symmetry introduced by this approach renders the results independent of the reference cell type. We discuss the necessary adaptations of state-of-the-art numerical optimization techniques and the process of model selection for this method. We assess the performance with realistic biological models and synthetic data, and demonstrate that it outperforms existing approaches. Finally, we also exemplify its application to published biological models including experimental data, and link the results to independent biological measurements.

Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models

Likelihood ratios are frequently utilized as basis for statistical tests, for model selection criteria and for assessing parameter and prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood ratios into p-values or confidence intervals requires the exact form of the test statistic's distribution. The lack of knowledge about this distribution for nonlinear ordinary differential equation (ODE) models requires an approximation which assumes the so-called asymptotic setting, i.e. a sufficiently large amount of data. Since the amount of data from quantitative molecular biology is typically limited in applications, this finite-sample case regularly occurs for mechanistic models of dynamical systems, e.g. biochemical reaction networks or infectious disease models. Thus, it is unclear whether the standard approach of using statistical thresholds derived for the asymptotic large-sample setting in realistic applications results in valid conclusions. In this study, empirical likelihood ratios for parameters from 19 published nonlinear ODE benchmark models are investigated using a resampling approach for the original data designs. Their distributions are compared to the asymptotic approximation and statistical thresholds are checked for conservativeness. It turns out, that corrections of the likelihood ratios in such finite-sample applications are required in order to avoid anti-conservative results.

The computational capabilities of many-to-many protein interaction networks

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors.

Realistic simulation of time-course measurements in systems biology

In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.

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.

Dysregulation of the immune response in TGF-β signalopathies

The transforming growth factor-β (TGF-β) family of cytokines exerts pleiotropic functions during embryonic development, tissue homeostasis and repair as well as within the immune system. Single gene defects in individual component of this signaling machinery cause defined Mendelian diseases associated with aberrant activation of TGF-β signaling, ultimately leading to impaired development, immune responses or both. Gene defects that affect members of the TGF-β cytokine family result in more restricted phenotypes, while those affecting downstream components of the signaling machinery induce broader defects. These rare disorders, also known as TGF-β signalopathies, provide the unique opportunity to improve our understanding of the role and the relevance of the TGF-β signaling in the human immune system. Here, we summarize this elaborate signaling pathway, review the diverse clinical presentations and immunological phenotypes observed in these patients and discuss the phenotypic overlap between humans and mice genetically deficient for individual components of the TGF-β signaling cascade.

The context-dependent, combinatorial logic of BMP signaling

Cell-cell communication systems typically comprise families of ligand and receptor variants that function together in combinations. Pathway activation depends on the complex way in which ligands are presented extracellularly and receptors are expressed by the signal-receiving cell. To understand the combinatorial logic of such a system, we systematically measured pairwise bone morphogenetic protein (BMP) ligand interactions in cells with varying receptor expression. Ligands could be classified into equivalence groups based on their profile of positive and negative synergies with other ligands. These groups varied with receptor expression, explaining how ligands can functionally replace each other in one context but not another. Context-dependent combinatorial interactions could be explained by a biochemical model based on the competitive formation of alternative signaling complexes with distinct activities. Together, these results provide insights into the roles of BMP combinations in developmental and therapeutic contexts and establish a framework for analyzing other combinatorial, context-dependent signaling systems.

Myostatin/Activin Receptor Ligands in Muscle and the Development Status of Attenuating Drugs

Muscle wasting disease indications are among the most debilitating and often deadly noncommunicable disease states. As a comorbidity, muscle wasting is associated with different neuromuscular diseases and myopathies, cancer, heart failure, chronic pulmonary and renal diseases, peripheral neuropathies, inflammatory disorders, and, of course, musculoskeletal injuries. Current treatment strategies are relatively ineffective and can at best only limit the rate of muscle degeneration. This includes nutritional supplementation and appetite stimulants as well as immunosuppressants capable of exacerbating muscle loss. Arguably, the most promising treatments in development attempt to disrupt myostatin and activin receptor signaling because these circulating factors are potent inhibitors of muscle growth and regulators of muscle progenitor cell differentiation. Indeed, several studies demonstrated the clinical potential of "inhibiting the inhibitors," increasing muscle cell protein synthesis, decreasing degradation, enhancing mitochondrial biogenesis, and preserving muscle function. Such changes can prevent muscle wasting in various disease animal models yet many drugs targeting this pathway failed during clinical trials, some from serious treatment-related adverse events and off-target interactions. More often, however, failures resulted from the inability to improve muscle function despite preserving muscle mass. Drugs still in development include antibodies and gene therapeutics, all with different targets and thus, safety, efficacy, and proposed use profiles. Each is unique in design and, if successful, could revolutionize the treatment of both acute and chronic muscle wasting. They could also be used in combination with other developing therapeutics for related muscle pathologies or even metabolic diseases.

Mini-batch optimization enables training of ODE models on large-scale datasets

Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Determinants of Ligand Specificity and Functional Plasticity in Type I Interferon Signaling

The Type I Interferon family of cytokines all act through the same cell surface receptor and induce phosphorylation of the same subset of response regulators of the STAT family. Despite their shared receptor, different Type I Interferons have different functions during immune response to infection. In particular, they differ in the potency of their induced anti-viral and anti-proliferative responses in target cells. It remains not fully understood how these functional differences can arise in a ligand-specific manner both at the level of STAT phosphorylation and the downstream function. We use a minimal computational model of Type I Interferon signaling, focusing on Interferon-α and Interferon-β. We validate the model with quantitative experimental data to identify the key determinants of specificity and functional plasticity in Type I Interferon signaling. We investigate different mechanisms of signal discrimination, and how multiple system components such as binding affinity, receptor expression levels and their variability, receptor internalization, short-term negative feedback by SOCS1 protein, and differential receptor expression play together to ensure ligand specificity on the level of STAT phosphorylation. Based on these results, we propose phenomenological functional mappings from STAT activation to downstream anti-viral and anti-proliferative activity to investigate differential signal processing steps downstream of STAT phosphorylation. We find that the negative feedback by the protein USP18, which enhances differences in signaling between Interferons via ligand-dependent refractoriness, can give rise to functional plasticity in Interferon-α and Interferon-β signaling, and explore other factors that control functional plasticity. Beyond Type I Interferon signaling, our results have a broad applicability to questions of signaling specificity and functional plasticity in signaling systems with multiple ligands acting through a bottleneck of a small number of shared receptors.

Conformational landscape of multidomain SMAD proteins

SMAD transcription factors, the main effectors of the TGFβ (transforming growth factor β) network, have a mixed architecture of globular domains and flexible linkers. Such a complicated architecture precluded the description of their full-length (FL) structure for many years. In this study, we unravel the structures of SMAD4 and SMAD2 proteins through an integrative approach combining Small-angle X-ray scattering, Nuclear Magnetic Resonance spectroscopy, X-ray, and computational modeling. We show that both proteins populate ensembles of conformations, with the globular domains tethered by disordered and flexible linkers, which defines a new dimension of regulation. The flexibility of the linkers facilitates DNA and protein binding and modulates the protein structure. Yet, SMAD4FL is monomeric, whereas SMAD2FL is in different monomer-dimer-trimer states, driven by interactions of the MH2 domains. Dimers are present regardless of the SMAD2FL activation state and concentration. Finally, we propose that SMAD2FL dimers are key building blocks for the quaternary structures of SMAD complexes.

Phosphorylation of SMAD3 in immune cells predicts survival of patients with early stage non-small cell lung cancer

Background

The interplay of immune and cancer cells takes place in the tumor microenvironment where multiple signals are exchanged. The transforming growth factor beta (TGFB) pathway is known to be dysregulated in lung cancer and can impede an effective immune response. However, the exact mechanisms are yet to be determined. Especially which cells respond and where does this signaling take place with respect to the local microenvironment.

Methods

Human non-small cell lung cancer samples were retrospectively analyzed by multiplexed immunohistochemistry for SMAD3 phosphorylation and programmed death ligand 1 expression in different immune cells with respect to their localization within the tumor tissue. Spatial relationships were studied to examine possible cell-cell interactions and analyzed in conjunction with clinical data.

Results

TGFB pathway activation in CD3, CD8, Foxp3 and CD68 cells, as indicated by SMAD3 phosphorylation, negatively impacts overall and partially disease-free survival of patients with lung cancerindependent of histological subtype. A high frequency of Foxp3 regulatory T cells positive for SMAD3 phosphorylation in close vicinity of CD8 T cells within the tumor discriminate a rapidly progressing group of patients with lung cancer.

Conclusions

TGFB pathway activation of local immune cells within the tumor microenvironment impacts survival of early stage lung cancer. This might benefit patients not eligible for targeted therapies or immune checkpoint therapy as a therapeutic option to re-activate the local immune response.

Narasin inhibits tumor metastasis and growth of ERα‑positive breast cancer cells by inactivation of the TGF‑β/SMAD3 and IL‑6/STAT3 signaling pathways

Treatment of human estrogen receptor (ER)-positive breast cancer (ER⁺ BC) using conventional chemotherapy remains a challenge and is often ineffective as a result of tumor metastasis. The present study aimed to investigate the ability of narasin, an ionophore antibiotic, to potentially inhibit tumor metastasis and growth in human ER⁺ BC. Narasin was found to have significant inhibitory abilities on cell proliferation, migration and invasion in ER⁺ BC cell lines MCF-7 and T47D compared with the triple-negative BC cell MDA-MB-231. For the in vivo studies, narasin effectively decreased the number of tumor metastasis nodules, tumor volume and weight without apparent toxicity in human MCF-7 nude mouse left ventricle injection tumor metastasis and xenograft models. Mechanistically, it demonstrated that exposure to TGF-β or IL-6 induced the expression of epithelial-mesenchymal transition (EMT) markers in ER⁺ BC cell lines. On the contrary, narasin dose-dependently reversed EMT by increasing the expression of E-cadherin and decreasing the expression of N-cadherin, vimentin, β-catenin and zinc finger E-box-binding homeobox 1 at the protein and gene expression levels. Gene microarray, molecular docking and western blotting were performed to demonstrate that those protein and gene expression levels are regulated by the inactivation of the TGF-β/phosphorylated (p)-SMAD3 and IL-6/p-STAT3 signaling pathways. Taken together, these findings indicated that narasin may be a promising candidate that can be further optimized for the treatment of human ER⁺ BC.

RAP-011 Rescues the Disease Phenotype in a Cellular Model of Congenital Dyserythropoietic Anemia Type II by Inhibiting the SMAD2-3 Pathway

Congenital dyserythropoietic anemia type II (CDA II) is a hypo-productive anemia defined by ineffective erythropoiesis through maturation arrest of erythroid precursors. CDA II is an autosomal recessive disorder due to loss-of-function mutations in SEC23B. Currently, management of patients with CDA II is based on transfusions, splenectomy, or hematopoietic stem-cell transplantation. Several studies have highlighted benefits of ACE-011 (sotatercept) treatment of ineffective erythropoiesis, which acts as a ligand trap against growth differentiation factor (GDF)11. Herein, we show that GDF11 levels are increased in CDA II, which suggests sotatercept as a targeted therapy for treatment of these patients. Treatment of stable clones of SEC23B-silenced erythroleukemia K562 cells with the iron-containing porphyrin hemin plus GDF11 increased expression of pSMAD2 and reduced nuclear localization of the transcription factor GATA1, with subsequent reduced gene expression of erythroid differentiation markers. We demonstrate that treatment of these SEC23B-silenced K562 cells with RAP-011, a "murinized" ortholog of sotatercept, rescues the disease phenotype by restoring gene expression of erythroid markers through inhibition of the phosphorylated SMAD2 pathway. Our data also demonstrate the effect of RAP-011 treatment in reducing the expression of erythroferrone in vitro, thus suggesting a possible beneficial role of the use of sotatercept in the management of iron overload in patients with CDA II.

The Role of TGFβ Signaling in Microglia Maturation and Activation

The pleiotropic cytokine transforming growth factor-beta 1 (TGFβ1) plays pivotal roles in different cell types, including immune cells such as T cells, monocytes/macrophages, and microglia. Microglia are essential during physiological and pathological events. Maturation of postnatal microglia, as well as the regulation of the complex functional repertoire of microglia, needs to be carefully orchestrated. However, an understanding of how mammalian microglia maturation and disease-associated microglia activation is regulated remains fragmentary. Here, we summarize recent observations made by employing transgenic approaches to silence microglial TGFβ signaling in mice. These revealed that TGFβ1 and TGFβ signaling are indispensable for microglia maturation, adult microglia homeostasis, and the control of microglia activation in central nervous system pathologies.

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.

Benchmark problems for dynamic modeling of intracellular processes

Motivation

Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing.

Results

We present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection.

Availability and implementation

The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models.

Supplementary information

Supplementary data are available at Bioinformatics online.

The transcriptional regulator ZNF398 mediates pluripotency and epithelial character downstream of TGF-beta in human PSCs

Human pluripotent stem cells (hPSCs) have the capacity to give rise to all differentiated cells of the adult. TGF-beta is used routinely for expansion of conventional hPSCs as flat epithelial colonies expressing the transcription factors POU5F1/OCT4, NANOG, SOX2. Here we report a global analysis of the transcriptional programme controlled by TGF-beta followed by an unbiased gain-of-function screening in multiple hPSC lines to identify factors mediating TGF-beta activity. We identify a quartet of transcriptional regulators promoting hPSC self-renewal including ZNF398, a human-specific mediator of pluripotency and epithelial character in hPSCs. Mechanistically, ZNF398 binds active promoters and enhancers together with SMAD3 and the histone acetyltransferase EP300, enabling transcription of TGF-beta targets. In the context of somatic cell reprogramming, inhibition of ZNF398 abolishes activation of pluripotency and epithelial genes and colony formation. Our findings have clear implications for the generation of bona fide hPSCs for regenerative medicine.

TGF-β Signaling

Transforming growth factor-β (TGF-β) represents an evolutionarily conserved family of secreted polypeptide factors that regulate many aspects of physiological embryogenesis and adult tissue homeostasis. The TGF-β family members are also involved in pathophysiological mechanisms that underlie many diseases. Although the family comprises many factors, which exhibit cell type-specific and developmental stage-dependent biological actions, they all signal via conserved signaling pathways. The signaling mechanisms of the TGF-β family are controlled at the extracellular level, where ligand secretion, deposition to the extracellular matrix and activation prior to signaling play important roles. At the plasma membrane level, TGF-βs associate with receptor kinases that mediate phosphorylation-dependent signaling to downstream mediators, mainly the SMAD proteins, and mediate oligomerization-dependent signaling to ubiquitin ligases and intracellular protein kinases. The interplay between SMADs and other signaling proteins mediate regulatory signals that control expression of target genes, RNA processing at multiple levels, mRNA translation and nuclear or cytoplasmic protein regulation. This article emphasizes signaling mechanisms and the importance of biochemical control in executing biological functions by the prototype member of the family, TGF-β.

Effects of suppressing Smads expression on wound healing in Hyriopsis cumingii

As a specific pearl mussel in China, Hyriopsis cumingii has enormous economic value. However, the organism damage caused by pearl insertion is immeasurable. TGF-β/Smad signal transduction pathways are involved in all phases of wound healing. We have previously reported on two cytoplasmic signal transduction factors, Smad3 and Smad5 in mussel H. cumingii (named HcSmads), suggesting their involvements in wound healing. Here, Smad4 was cloned and described. The full length cDNA of HcSmad4 was 2543 bp encoded 515 amino acids. Deduced HcSmad4 protein possessed conserved MH1 and MH2 domains, nuclear location signals (NLS), nuclear exput signals (NES) and Smad activation domain (SAD). Transcripts of Smad3, 4 and 5 were constitutively expressed in all detected tissues, at highest levels in muscles. Furthermore, HcSmad4 mRNA levels were significantly increased at incision site post wounding, and expression of downstream target genes of Smad4, such as HcMMP1, HcMMP19, HcTIMP1 and HcTIMP2 were upregulated to a certain extent. Whatever knocked down HcSmad3/4 or treated by specific inhibitors of Smad 3 (SIS3), expression levels of these genes displayed a significantly downregulated tendency compared with the wound group. In addition, histological evaluation suggested that Smad3 knockdown or SIS3 treatment was accelerated wound healing, and then Smad4 knockdown delayed the process of wound healing in mussels. These data implicate that Smad3/4 play an important role in tissue repair in mollusks.

Identification and Profiling of Environmental Chemicals That Inhibit the TGFβ/SMAD Signaling Pathway

The transforming growth factor beta (TGFβ) superfamily of secreted signaling molecules and their cognate receptors regulate cell fate and behaviors relevant to many developmental and disease processes. Disruption of TGFβ signaling during embryonic development can, for example, affect morphogenesis and differentiation through complex pathways that may be SMAD (Small Mothers Against Decapentaplegic) dependent or SMAD independent. In the present study, the SMAD Binding Element (SBE)-beta lactamase (bla) HEK 293T cell line, which responds to the activation of the SMAD2/3/4 complex, was used in a quantitative high-throughput screening (qHTS) assay to identify potential TGFβ disruptors in the Tox21 10K compound library. From the primary screening we identified several kinase inhibitors, organometallic compounds, and dithiocarbamates (DTCs) that inhibited TGFβ1-induced SMAD signaling of reporter gene activation independent of cytotoxicity. Counterscreen of SBE antagonists on human embryonic neural stem cells demonstrated cytotoxicity, providing additional evidence to support evaluation of these compounds for developmental toxicity. We profiled the inhibitory patterns of putative SBE antagonists toward other developmental signaling pathways, including wingless-related integration site (WNT), retinoic acid α receptor (RAR), and sonic hedgehog (SHH). The profiling results from SBE-bla assay identify chemicals that disrupt TGFβ/SMAD signaling as part of an integrated qHTS approach for prioritizing putative developmental toxicants.

Loss-of-Function in SMAD4 Might Not Be Critical for Human Natural Killer Cell Responsiveness to TGF-β

We characterized the NK cell phenotype and function in three family members with Hereditary Hemorrhagic Telangiectasia (HHT) due to heterozygous SMAD4 mutations. Loss-of-function mutation in this gene did not induce developmental effects to alter CD56bright or CD56dim NK cell subset proportions in peripheral blood; and did not result in major differences in either their IL-15-induced proliferation, or their cytokine secretion response to TGF-β1. These data suggest that SMAD4 plays a redundant role in downstream TGF-β signaling in NK cells.

Specificity, versatility, and control of TGF-β family signaling

Encoded in mammalian cells by 33 genes, the transforming growth factor-β (TGF-β) family of secreted, homodimeric and heterodimeric proteins controls the differentiation of most, if not all, cell lineages and many aspects of cell and tissue physiology in multicellular eukaryotes. Deregulation of TGF-β family signaling leads to developmental anomalies and disease, whereas enhanced TGF-β signaling contributes to cancer and fibrosis. Here, we review the fundamentals of the signaling mechanisms that are initiated upon TGF-β ligand binding to its cell surface receptors and the dependence of the signaling responses on input from and cooperation with other signaling pathways. We discuss how cells exquisitely control the functional presentation and activation of heteromeric receptor complexes of transmembrane, dual-specificity kinases and, thus, define their context-dependent responsiveness to ligands. We also introduce the mechanisms through which proteins called Smads act as intracellular effectors of ligand-induced gene expression responses and show that the specificity and impressive versatility of Smad signaling depend on cross-talk from other pathways. Last, we discuss how non-Smad signaling mechanisms, initiated by distinct ligand-activated receptor complexes, complement Smad signaling and thus contribute to cellular responses.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or K_D value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.

TINAGL1 promotes hepatocellular carcinogenesis through the activation of TGF-β signaling-medicated VEGF expression

Background and purpose

Tubulointerstitial nephritis antigen-like 1 (TINAGL1) is an extracellular matrix protein that plays an important role in cell adhesion and therefore modulates cell proliferation, migration, and differentiation. In addition, it is frequently upregulated in highly metastatic tumors. The aim of our study was to determine the role of TINAGL1 in the progression and metastasis of hepatocellular carcinoma (HCC).

Materials and methods

TINAGL1 mRNA levels were analyzed in HCC and adjacent non-tumorous samples by reverse transcription polymerase chain reaction (RT-PCR). Human HCC cell lines were transfected with lentiviral plasmids expressing either si-TINAGL1 or TINAGL1 and subjected to CCK-8, colony forming, transwell migration, Annexin V/propidium iodide, and 5-ethynyl-2′-deoxyuridine uptake assays. Suitably transfected HCC cells were injected into athymic nude mice to establish xenograft tumors that were imaged and measured on a weekly basis. Mediators of the TGF-β signaling pathway were analyzed by Western blot.

Results

TINAGL1 was upregulated in human HCC tissues and associated with poor prognosis. TINAGL1 knockdown suppressed HCC cell growth, proliferation, and migration and induced apoptosis in HCC cells, whereas TINAGL1 overexpression had opposite effects. In addition, inhibition of TINAGL1 retarded xenograft tumor growth in a nude mouse model. Mechanistically, TINAGL1 activated the TGF-β signaling pathway and increased VEGF secretion.

Conclusion

TINAGL1 promotes hepatocellular carcinogenesis and metastasis via the TGF-β/Smad3/VEGF axis and is a potential new biomarker of HCC.

Systemic network analysis identifies XIAP and IκBα as potential drug targets in TRAIL resistant BRAF mutated melanoma

Metastatic melanoma remains a life-threatening disease because most tumors develop resistance to targeted kinase inhibitors thereby regaining tumorigenic capacity. We show the 2nd generation hexavalent TRAIL receptor-targeted agonist IZI1551 to induce pronounced apoptotic cell death in mutBRAF melanoma cells. Aiming to identify molecular changes that may confer IZI1551 resistance we combined Dynamic Bayesian Network modelling with a sophisticated regularization strategy resulting in sparse and context-sensitive networks and show the performance of this strategy in the detection of cell line-specific deregulations of a signalling network. Comparing IZI1551-sensitive to IZI1551-resistant melanoma cells the model accurately and correctly predicted activation of NFκB in concert with upregulation of the anti-apoptotic protein XIAP as the key mediator of IZI1551 resistance. Thus, the incorporation of multiple regularization functions in logical network optimization may provide a promising avenue to assess the effects of drug combinations and to identify responders to selected combination therapies.

Systems biology: old news or new stimulus for biochemistry

In this issue of Essays in Biochemistry, biochemistry meets systems biology-a blind date that may hold all the promises, pitfalls and failures of a relationship where a new discipline has been sprung upon a well-established one. As the articles in this issue show, the blind date in this case has great potential to develop into a long-term relationship, where both partners share common values but can benefit from different complementary approaches. Together this partnership is well poised to address and solve some of the major challenges in modern biology.

JNK regulates muscle remodeling via myostatin/SMAD inhibition

Skeletal muscle has a remarkable plasticity to adapt and remodel in response to environmental cues, such as physical exercise. Endurance exercise stimulates improvements in muscle oxidative capacity, while resistance exercise induces muscle growth. Here we show that the c-Jun N-terminal kinase (JNK) is a molecular switch that when active, stimulates muscle fibers to grow, resulting in increased muscle mass. Conversely, when muscle JNK activation is suppressed, an alternative remodeling program is initiated, resulting in smaller, more oxidative muscle fibers, and enhanced aerobic fitness. When muscle is exposed to mechanical stress, JNK initiates muscle growth via phosphorylation of the transcription factor, SMAD2, at specific linker region residues leading to inhibition of the growth suppressor, myostatin. In human skeletal muscle, this JNK/SMAD signaling axis is activated by resistance exercise, but not endurance exercise. We conclude that JNK acts as a key mediator of muscle remodeling during exercise via regulation of myostatin/SMAD signaling.

Using Regularization to Infer Cell Line Specificity in Logical Network Models of Signaling Pathways

Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.