Integrative modeling and analysis of signaling crosstalk reveal molecular switches coordinating Yes-associated protein transcriptional activities

The transcriptional co-activator YAP forms complexes with distinct transcription factors, controlling cell fate decisions, such as proliferation and apoptosis. However, the mechanisms underlying its context-dependent function are poorly defined. This study explores the interplay between the TGF-β and Hippo pathways and their influence on YAP's association with specific transcription factors. By integrating iterative mathematical modeling with experimental validation, we uncover molecular switches, predominantly controlled by RASSF1A and ITCH, which dictate the formation of YAP-SMAD (proliferative) and YAP-p73 (apoptotic) complexes. Our results show that RASSF1A enhances the formation of apoptotic complexes, whereas ITCH promotes the formation of proliferative complexes. Notably, higher levels of ITCH transform YAP-SMAD activity from a transient to a sustained state, impacting cellular behaviors. Extending these findings to various breast cancer cell lines highlights the role of cellular context in YAP regulation. Our study provides new insights into the mechanisms of YAP transcriptional activities and their therapeutic implications.

Feed-forward stimulation of CAMK2 by the oncogenic pseudokinase PEAK1 generates a therapeutically 'actionable' signalling axis in triple negative breast cancer

The PEAK family of pseudokinases, comprising PEAK1-3, are signalling scaffolds that play oncogenic roles in several poor prognosis human cancers, including triple negative breast cancer (TNBC). However, therapeutic targeting of pseudokinases is challenging due to their lack of catalytic activity. To address this, we screened for PEAK1 effectors by affinity purification and mass spectrometry, identifying calcium/calmodulin-dependent protein kinase 2 (CAMK2)D and CAMK2G. PEAK1 promoted CAMK2D/G activation in TNBC cells via a novel feed-forward mechanism involving PEAK1/PLCγ1/Ca 2+ signalling and direct binding via a consensus CAMK2 interaction motif in the PEAK1 N-terminus. In turn, CAMK2 phosphorylated PEAK1 to enhance association with PEAK2, which is critical for PEAK1 oncogenic signalling. To achieve pharmacologic targeting of PEAK1/CAMK2, we repurposed RA306, a second generation CAMK2 inhibitor under pre-clinical development for treatment of cardiovascular disease. RA306 demonstrated on-target activity against CAMK2 in TNBC cells and inhibited PEAK1-enhanced migration and invasion in vitro . Moreover, RA306 significantly attenuated TNBC xenograft growth and blocked metastasis in a manner mirrored by CRISPR-mediated PEAK1 ablation. Overall, these studies establish PEAK1 as a critical cell signalling nexus, identify a novel mechanism for regulation of Ca 2+ signalling and its integration with tyrosine kinase signals, and identify CAMK2 as a therapeutically 'actionable' target downstream of PEAK1.

Integrative modeling uncovers p21-driven drug resistance and prioritizes therapies for PIK3CA-mutant breast cancer

Utility of PI3Kα inhibitors like BYL719 is limited by the acquisition of genetic and non-genetic mechanisms of resistance which cause disease recurrence. Several combination therapies based on PI3K inhibition have been proposed, but a way to systematically prioritize them for breast cancer treatment is still missing. By integrating published and in-house studies, we have developed in silico models that quantitatively capture dynamics of PI3K signaling at the network-level under a BYL719-sensitive versus BYL719 resistant-cell state. Computational predictions show that signal rewiring to alternative components of the PI3K pathway promote resistance to BYL719 and identify PDK1 as the most effective co-target with PI3Kα rescuing sensitivity of resistant cells to BYL719. To explore whether PI3K pathway-independent mechanisms further contribute to BYL719 resistance, we performed phosphoproteomics and found that selection of high levels of the cell cycle regulator p21 unexpectedly promoted drug resistance in T47D cells. Functionally, high p21 levels favored repair of BYL719-induced DNA damage and bypass of the associated cellular senescence. Importantly, targeted inhibition of the check-point inhibitor CHK1 with MK-8776 effectively caused death of p21-high T47D cells, thus establishing a new vulnerability of BYL719-resistant breast cancer cells. Together, our integrated studies uncover hidden molecular mediators causing resistance to PI3Kα inhibition and provide a framework to prioritize combination therapies for PI3K-mutant breast cancer.

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.

Meta-Dynamic Network Modelling for Biochemical Networks

ODE modelling requires accurate knowledge of parameter and state variable values to deliver accurate and robust predictions. Parameters and state variables, however, are rarely static and immutable entities, especially in a biological context. This observation undermines the predictions made by ODE models that rely on specific parameter and state variable values and limits the contexts in which their predictions remain accurate and useful. Meta-dynamic network (MDN) modelling is a technique that can be synergistically integrated into an ODE modelling pipeline to assist in overcoming these limitations. The core mechanic of MDN modelling is the generation of a large number of model instances, each with a unique set of parameters and/or state variable values, followed by the simulation of each to determine how parameter and state variable variation affects protein dynamics. This process reveals the range of possible protein dynamics for a given network topology. Since MDN modelling is integrated with traditional ODE modelling, it can also be used to investigate the underlying causal mechanics. This technique is particularly suited to the investigation of network behaviors in systems that are highly heterogenous or systems wherein the network properties can change over time. MDN is a collection of principles rather than a strict protocol, so in this chapter, we have introduced the core principles using an example, the Hippo-ERK crosstalk signalling network.

SynDISCO: A Mechanistic Modeling-Based Framework for Predictive Prioritization of Synergistic Drug Combinations Targeting Cell Signalling Networks

The widespread development of resistance to cancer monotherapies has prompted the need to identify combinatorial treatment approaches that circumvent drug resistance and achieve more durable clinical benefit. However, given the vast space of possible combinations of existing drugs, the inaccessibility of drug screens to candidate targets with no available drugs, and the significant heterogeneity of cancers, exhaustive experimental testing of combination treatments remains highly impractical. There is thus an urgent need to develop computational approaches that complement experimental efforts and aid the identification and prioritization of effective drug combinations. Here, we provide a practical guide to SynDISCO, a computational framework that leverages mechanistic ODE modeling to predict and prioritize synergistic combination treatments directed at signaling networks. We demonstrate the key steps of SynDISCO and its application to the EGFR-MET signaling network in triple negative breast cancer as an illustrative example. SynDISCO is, however, a network- and cancer-independent framework, and given a suitable ODE model of the network of interest, it could be leveraged to discover cancer-specific combination treatments.

Multi-Dimensional Analysis of Biochemical Network Dynamics Using pyDYVIPAC

Biochemical networks are dynamic, nonlinear, and high-dimensional systems. Realistic kinetic models of biochemical networks often comprise a multitude of kinetic parameters and state variables. Depending on the specific parameter values, a network can display one of a variety of possible dynamic behaviors such as monostable fixed point, damped oscillation, sustained oscillation, and/or bistability. Understanding how a network behaves under a particular parametric condition, and how its behavior changes as the model parameters are varied within the multidimensional parameter space are imperative for gaining a holistic understanding of the network dynamics. Such knowledge helps elucidate the parameter-to-dynamics mapping, uncover how cells make decisions under various pathophysiological contexts, and inform the design of biological circuits with desired behavior, where the latter is critical to the field of synthetic biology. In this chapter, we will present a practical guide to the multidimensional exploration, analysis, and visualization of network dynamics using pyDYVIPAC, which is a tool ideally suited to these purposes implemented in Python. The utility of pyDYVIPAC will be demonstrated using specific examples of biochemical networks with differing structures and dynamic properties via the interactive Jupyter Notebook environment.

ISGylation drives basal breast tumour progression by promoting EGFR recycling and Akt signalling

ISG15 is an ubiquitin-like modifier that is associated with reduced survival rates in breast cancer patients. The mechanism by which ISG15 achieves this however remains elusive. We demonstrate that modification of Rab GDP-Dissociation Inhibitor Beta (GDI2) by ISG15 (ISGylation) alters endocytic recycling of the EGF receptor (EGFR) in non-interferon stimulated cells using CRISPR-knock out models for ISGylation. By regulating EGFR trafficking, ISGylation enhances EGFR recycling and sustains Akt-signalling. We further show that Akt signalling positively correlates with levels of ISG15 and its E2-ligase in basal breast cancer cohorts, confirming the link between ISGylation and Akt signalling in human tumours. Persistent and enhanced Akt activation explains the more aggressive tumour behaviour observed in human breast cancers. We show that ISGylation can act as a driver of tumour progression rather than merely being a bystander.

Dynamic modelling of the PI3K/MTOR signalling network uncovers biphasic dependence of mTORC1 activity on the mTORC2 subunit SIN1

The PI3K/MTOR signalling network regulates a broad array of critical cellular processes, including cell growth, metabolism and autophagy. The mechanistic target of rapamycin (MTOR) kinase functions as a core catalytic subunit in two physically and functionally distinct complexes mTORC1 and mTORC2, which also share other common components including MLST8 (also known as GβL) and DEPTOR. Despite intensive research, how mTORC1 and 2 assembly and activity are coordinated, and how they are functionally linked remain to be fully characterized. This is due in part to the complex network wiring, featuring multiple feedback loops and intricate post-translational modifications. Here, we integrate predictive network modelling, in vitro experiments and -omics data analysis to elucidate the emergent dynamic behaviour of the PI3K/MTOR network. We construct new mechanistic models that encapsulate critical mechanistic details, including mTORC1/2 coordination by MLST8 (de)ubiquitination and the Akt-to-mTORC2 positive feedback loop. Model simulations validated by experimental studies revealed a previously unknown biphasic, threshold-gated dependence of mTORC1 activity on the key mTORC2 subunit SIN1, which is robust against cell-to-cell variation in protein expression. In addition, our integrative analysis demonstrates that ubiquitination of MLST8, which is reversed by OTUD7B, is regulated by IRS1/2. Our results further support the essential role of MLST8 in enabling both mTORC1 and 2’s activity and suggest MLST8 as a viable therapeutic target in breast cancer. Overall, our study reports a new mechanistic model of PI3K/MTOR signalling incorporating MLST8-mediated mTORC1/2 formation and unveils a novel regulatory linkage between mTORC1 and mTORC2.

Signalling networks are the key information-processing machineries that underpin the ability of living cells to respond proportionately to extra- (and intra-) cellular cues. The PI3K/MTOR signalling network is one of the most important signalling networks in human cells that regulates cellular response to critical hormones such as insulin; yet our understanding of the network behaviour remains far from complete. Here, we employed a highly integrative approach that combines predictive mathematical modelling, biological experimentation, and data analysis to gain novel systems-level insights into PI3K/MTOR signalling. We constructed new mathematical models of this complex network incorporating important regulatory mechanisms. In contrary to commonly-held views that mTORC2 lies upstream and is a positive regulator of mTORC1, we found that their relationship is highly non-linear and dose dependent. This finding has major implications for anti-mTORC2 therapy as depending on the cellular contexts, inhibiting mTORC2 may either reduce or enhance mTORC1 activation, the latter could inadvertently dampen the effect of mTORC2 blockade. Furthermore, our results demonstrate that MLST8 is required for the assembly and activity of both MTOR complexes and suggest MLST8 is a viable therapeutic target in breast cancer.

Akt phosphorylates insulin receptor substrate to limit PI3K-mediated PIP3 synthesis

The phosphoinositide 3-kinase (PI3K)-Akt network is tightly controlled by feedback mechanisms that regulate signal flow and ensure signal fidelity. A rapid overshoot in insulin-stimulated recruitment of Akt to the plasma membrane has previously been reported, which is indicative of negative feedback operating on acute timescales. Here, we show that Akt itself engages this negative feedback by phosphorylating insulin receptor substrate (IRS) 1 and 2 on a number of residues. Phosphorylation results in the depletion of plasma membrane-localised IRS1/2, reducing the pool available for interaction with the insulin receptor. Together these events limit plasma membrane-associated PI3K and phosphatidylinositol (3,4,5)-trisphosphate (PIP3) synthesis. We identified two Akt-dependent phosphorylation sites in IRS2 at S306 (S303 in mouse) and S577 (S573 in mouse) that are key drivers of this negative feedback. These findings establish a novel mechanism by which the kinase Akt acutely controls PIP3 abundance, through post-translational modification of the IRS scaffold.

Feedback, Crosstalk and Competition: Ingredients for Emergent Non-Linear Behaviour in the PI3K/mTOR Signalling Network

The PI3K/mTOR signalling pathway plays a central role in the governing of cell growth, survival and metabolism. As such, it must integrate and decode information from both external and internal sources to guide efficient decision-making by the cell. To facilitate this, the pathway has evolved an intricate web of complex regulatory mechanisms and elaborate crosstalk with neighbouring signalling pathways, making it a highly non-linear system. Here, we describe the mechanistic biological details that underpin these regulatory mechanisms, covering a multitude of negative and positive feedback loops, feed-forward loops, competing protein interactions, and crosstalk with major signalling pathways. Further, we highlight the non-linear and dynamic network behaviours that arise from these regulations, uncovered through computational and experimental studies. Given the pivotal role of the PI3K/mTOR network in cellular homeostasis and its frequent dysregulation in pathologies including cancer and diabetes, a coherent and systems-level understanding of the complex regulation and consequential dynamic signalling behaviours within this network is imperative for advancing biology and development of new therapeutic approaches.

Signaling Heterogeneity is Defined by Pathway Architecture and Intercellular Variability in Protein Expression

Insulin's activation of PI3K/Akt signaling, stimulates glucose uptake by enhancing delivery of GLUT4 to the cell surface. Here we examined the origins of intercellular heterogeneity in insulin signaling. Akt activation alone accounted for ~25% of the variance in GLUT4, indicating that additional sources of variance exist. The Akt and GLUT4 responses were highly reproducible within the same cell, suggesting the variance is between cells (extrinsic) and not within cells (intrinsic). Generalized mechanistic models (supported by experimental observations) demonstrated that the correlation between the steady-state levels of two measured signaling processes decreases with increasing distance from each other and that intercellular variation in protein expression (as an example of extrinsic variance) is sufficient to account for the variance in and between Akt and GLUT4. Thus, the response of a population to insulin signaling is underpinned by considerable single-cell heterogeneity that is largely driven by variance in gene/protein expression between cells.

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Insulin signaling is heterogeneous between cells in the same population

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The temporal response of signaling components within a cell is highly reproducible

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Upstream responses (Akt) can only partially predict downstream response (GLUT4)

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Protein expression variance is a driver of intercellular signaling heterogeneity

Control of Glucocorticoid Receptor Levels by PTEN Establishes a Failsafe Mechanism for Tumor Suppression

The PTEN tumor suppressor controls cell death and survival by regulating functions of various molecular targets. While the role of PTEN lipid-phosphatase activity on PtdIns(3,4,5)P3 and inhibition of PI3K pathway is well characterized, the biological relevance of PTEN protein-phosphatase activity remains undefined. Here, using knockin (KI) mice harboring cancer-associated and functionally relevant missense mutations, we show that although loss of PTEN lipid-phosphatase function cooperates with oncogenic PI3K to promote rapid mammary tumorigenesis, the additional loss of PTEN protein-phosphatase activity triggered an extensive cell death response evident in early and advanced mammary tumors. Omics and drug-targeting studies revealed that PI3Ks act to reduce glucocorticoid receptor (GR) levels, which are rescued by loss of PTEN protein-phosphatase activity to restrain cell survival. Thus, we find that the dual regulation of GR by PI3K and PTEN functions as a rheostat that can be exploited for the treatment of PTEN loss-driven cancers.

Asparagine Hydroxylation is a Reversible Post-translational Modification

Amino acid hydroxylation is a common post-translational modification, which generally regulates protein interactions or adds a functional group that can be further modified. Such hydroxylation is currently considered irreversible, necessitating the degradation and re-synthesis of the entire protein to reset the modification. Here we present evidence that the cellular machinery can reverse FIH-mediated asparagine hydroxylation on intact proteins. These data suggest that asparagine hydroxylation is a flexible and dynamic post-translational modification akin to modifications involved in regulating signaling networks, such as phosphorylation, methylation and ubiquitylation.

Cervical pessary vs vaginal progesterone for prevention of preterm birth in women with twin pregnancy and short cervix: economic analysis following randomized controlled trial

OBJECTIVE

To compare the cost-effectiveness of cervical pessary vs vaginal progesterone to prevent preterm birth and neonatal morbidity in women with twin pregnancy and a short cervix.

METHODS

Between 4 March 2016 and 3 June 2017, we performed this economic analysis following a randomized controlled trial (RCT), performed at My Duc Hospital, Ho Chi Minh City, Vietnam, that compared cervical pessary to vaginal progesterone in women with twin pregnancy and cervical length < 38 mm between 16 and 22 weeks of gestation. We used morbidity-free neonatal survival as a measure of effectiveness. Data on pregnancy outcome, maternal morbidity and neonatal complications were collected prospectively from medical files; additional information was obtained via telephone interviews with the patients. The incremental cost-effectiveness ratio was calculated as the incremental cost required to achieve one extra surviving morbidity-free neonate in the pessary group compared with in the progesterone group. Probabilistic and one-way sensitivity analyses were also performed.

RESULTS

During the study period, we screened 1113 women with twin pregnancy, of whom 300 fulfilled the inclusion criteria of the RCT and gave informed consent to participate. These women were assigned randomly to receive cervical pessary (n = 150) or vaginal progesterone (n = 150), with two women and one woman, respectively, being lost to follow-up. The rate of morbidity-free neonatal survival was significantly higher in the pessary group compared with the progesterone group (n = 241/296 (81.4%) vs 219/298 (73.5%); relative risk, 1.11 (95% CI, 1.02-1.21), P = 0.02). The mean total cost per woman was 3146 € in the pessary group vs 3570 € in the progesterone group (absolute difference, -424 € (95% CI, -842 to -3 €), P = 0.048). The cost per morbidity-free neonate was significantly lower in the pessary group compared with that in the progesterone group (2492 vs 2639 €; absolute difference, -147 € (95% CI, -284 to 10 €), P = 0.035).

CONCLUSION

In women with twin pregnancy and a short cervix, cervical pessary improves significantly the rate of morbidity-free neonatal survival while reducing costs, as compared with vaginal progesterone. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

FGFR3 signaling and function in triple negative breast cancer

Background

Triple negative breast cancer (TNBC) accounts for 16% of breast cancers and represents an aggressive subtype that lacks targeted therapeutic options. In this study, mass spectrometry (MS)-based tyrosine phosphorylation profiling identified aberrant FGFR3 activation in a subset of TNBC cell lines. This kinase was therefore evaluated as a potential therapeutic target.

Methods

MS-based tyrosine phosphorylation profiling was undertaken across a panel of 24 TNBC cell lines. Immunoprecipitation and Western blot were used to further characterize FGFR3 phosphorylation. Indirect immunofluorescence and confocal microscopy were used to determine FGFR3 localization. The selective FGFR1–3 inhibitor, PD173074 and siRNA knockdowns were used to characterize the functional role of FGFR3 in vitro. The TCGA and Metabric breast cancer datasets were interrogated to identify FGFR3 alterations and how they relate to breast cancer subtype and overall patient survival.

Results

High FGFR3 expression and phosphorylation were detected in SUM185PE cells, which harbor a FGFR3-TACC3 gene fusion. Low FGFR3 phosphorylation was detected in CAL51, MFM-223 and MDA-MB-231 cells. In SUM185PE cells, the FGFR3-TACC3 fusion protein contributed the majority of phosphorylated FGFR3, and largely localized to the cytoplasm and plasma membrane, with staining at the mitotic spindle in a small subset of cells. Knockdown of the FGFR3-TACC3 fusion and wildtype FGFR3 in SUM185PE cells decreased FRS2, AKT and ERK phosphorylation, and induced cell death. Knockdown of wildtype FGFR3 resulted in only a trend for decreased proliferation. PD173074 significantly decreased FRS2, AKT and ERK activation, and reduced SUM185PE cell proliferation. Cyclin A and pRb were also decreased in the presence of PD173074, while cleaved PARP was increased, indicating cell cycle arrest in G1 phase and apoptosis. Knockdown of FGFR3 in CAL51, MFM-223 and MDA-MB-231 cells had no significant effect on cell proliferation. Interrogation of public datasets revealed that increased FGFR3 expression in breast cancer was significantly associated with reduced overall survival, and that potentially oncogenic FGFR3 alterations (eg mutation and amplification) occur in the TNBC/basal, luminal A and luminal B subtypes, but are rare.

Conclusions

These results indicate that targeting FGFR3 may represent a therapeutic option for TNBC, but only for patients with oncogenic FGFR3 alterations, such as the FGFR3-TACC3 fusion.

Global redox proteome and phosphoproteome analysis reveals redox switch in Akt

Protein oxidation sits at the intersection of multiple signalling pathways, yet the magnitude and extent of crosstalk between oxidation and other post-translational modifications remains unclear. Here, we delineate global changes in adipocyte signalling networks following acute oxidative stress and reveal considerable crosstalk between cysteine oxidation and phosphorylation-based signalling. Oxidation of key regulatory kinases, including Akt, mTOR and AMPK influences the fidelity rather than their absolute activation state, highlighting an unappreciated interplay between these modifications. Mechanistic analysis of the redox regulation of Akt identified two cysteine residues in the pleckstrin homology domain (C60 and C77) to be reversibly oxidized. Oxidation at these sites affected Akt recruitment to the plasma membrane by stabilizing the PIP₃ binding pocket. Our data provide insights into the interplay between oxidative stress-derived redox signalling and protein phosphorylation networks and serve as a resource for understanding the contribution of cellular oxidation to a range of diseases.

Crosstalk between protein oxidation and other post-translational modifications remains unexplored. Here, the authors map the phosphoproteome, cysteine redox proteome and total proteome of adipocytes under acute oxidative stress and reveal crosstalk between cysteine oxidation and phosphorylation-based signalling.

Coupled feedback regulation of nuclear factor of activated T-cells (NFAT) modulates activation-induced cell death of T cells

A properly functioning immune system is vital for an organism’s wellbeing. Immune tolerance is a critical feature of the immune system that allows immune cells to mount effective responses against exogenous pathogens such as viruses and bacteria, while preventing attack to self-tissues. Activation-induced cell death (AICD) in T lymphocytes, in which repeated stimulations of the T-cell receptor (TCR) lead to activation and then apoptosis of T cells, is a major mechanism for T cell homeostasis and helps maintain peripheral immune tolerance. Defects in AICD can lead to development of autoimmune diseases. Despite its importance, the regulatory mechanisms that underlie AICD remain poorly understood, particularly at an integrative network level. Here, we develop a dynamic multi-pathway model of the integrated TCR signalling network and perform model-based analysis to characterize the network-level properties of AICD. Model simulation and analysis show that amplified activation of the transcriptional factor NFAT in response to repeated TCR stimulations, a phenomenon central to AICD, is tightly modulated by a coupled positive-negative feedback mechanism. NFAT amplification is predominantly enabled by a positive feedback self-regulated by NFAT, while opposed by a NFAT-induced negative feedback via Carabin. Furthermore, model analysis predicts an optimal therapeutic window for drugs that help minimize proliferation while maximize AICD of T cells. Overall, our study provides a comprehensive mathematical model of TCR signalling and model-based analysis offers new network-level insights into the regulation of activation-induced cell death in T cells.

Network rewiring, adaptive resistance and combating strategies in breast cancer

Resistance to targeted anti-cancer drugs is a complex phenomenon and a major challenge in cancer treatment. It is becoming increasingly evident that a form of acquired drug resistance known as “adaptive resistance” is a common cause of treatment failure and patient relapse in many cancers. Unlike classical resistance mechanisms that are acquired via genomic alterations, adaptive resistance is instead driven by non-genomic changes involving rapid and dynamic rewiring of signalling and/or transcriptional networks following therapy, enabled by complex pathway crosstalk and feedback regulation. Such network rewiring allows tumour cells to adapt to the drug treatment, circumvent the initial drug challenge and continue to survive in the presence of the drug. Despite its great clinical importance, adaptive resistance remains largely under-studied and poorly defined. This review is focused on recent findings which provide new insights into the mechanisms underlying adaptive resistance in breast cancer, highlighting how breast tumour cells rewire intracellular signalling pathways to overcome the stress of initial targeted therapy. In particular, we investigate adaptive resistance to targeted inhibition of two major oncogenic signalling axes frequently dysregulated in breast cancer, the PI3K-AKT-mTOR and RAS-MAPK signalling pathways; and discuss potential combination treatment strategies that overcome such resistance. In addition, we highlight application of quantitative and computational modelling as a novel integrative and powerful approach to gain network-level understanding of network rewiring, and rationally identify and prioritise effective drug combinations.

Systems modelling of the EGFR-PYK2-c-Met interaction network predicts and prioritizes synergistic drug combinations for triple-negative breast cancer

Prediction of drug combinations that effectively target cancer cells is a critical challenge for cancer therapy, in particular for triple-negative breast cancer (TNBC), a highly aggressive breast cancer subtype with no effective targeted treatment. As signalling pathway networks critically control cancer cell behaviour, analysis of signalling network activity and crosstalk can help predict potent drug combinations and rational stratification of patients, thus bringing therapeutic and prognostic values. We have previously showed that the non-receptor tyrosine kinase PYK2 is a downstream effector of EGFR and c-Met and demonstrated their crosstalk signalling in basal-like TNBC. Here we applied a systems modelling approach and developed a mechanistic model of the integrated EGFR-PYK2-c-Met signalling network to identify and prioritize potent drug combinations for TNBC. Model predictions validated by experimental data revealed that among six potential combinations of drug pairs targeting the central nodes of the network, including EGFR, c-Met, PYK2 and STAT3, co-targeting of EGFR and PYK2 and to a lesser extent of EGFR and c-Met yielded strongest synergistic effect. Importantly, the synergy in co-targeting EGFR and PYK2 was linked to switch-like cell proliferation-associated responses. Moreover, simulations of patient-specific models using public gene expression data of TNBC patients led to predictive stratification of patients into subgroups displaying distinct susceptibility to specific drug combinations. These results suggest that mechanistic systems modelling is a powerful approach for the rational design, prediction and prioritization of potent combination therapies for individual patients, thus providing a concrete step towards personalized treatment for TNBC and other tumour types.

We applied a systems modelling approach combining mechanistic modelling and biological experimentation to identify effective drug combinations for triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer with no approved targeted treatment. The model predicted and prioritized the synergistic combinations as confirmed by experimental data, demonstrating the power of this approach. Moreover, analysis of clinical data of TNBC patients and patient-specific modelling simulation enabled us to stratify the patients into subgroups with distinct susceptibility to specific drug combinations, and thus defined a subset of patient that could benefit from the combined treatments.

Dynamic modelling of the mTOR signalling network reveals complex emergent behaviours conferred by DEPTOR

The mechanistic Target of Rapamycin (mTOR) signalling network is an evolutionarily conserved network that controls key cellular processes, including cell growth and metabolism. Consisting of the major kinase complexes mTOR Complex 1 and 2 (mTORC1/2), the mTOR network harbours complex interactions and feedback loops. The DEP domain-containing mTOR-interacting protein (DEPTOR) was recently identified as an endogenous inhibitor of both mTORC1 and 2 through direct interactions, and is in turn degraded by mTORC1/2, adding an extra layer of complexity to the mTOR network. Yet, the dynamic properties of the DEPTOR-mTOR network and the roles of DEPTOR in coordinating mTORC1/2 activation dynamics have not been characterised. Using computational modelling, systems analysis and dynamic simulations we show that DEPTOR confers remarkably rich and complex dynamic behaviours to mTOR signalling, including abrupt, bistable switches, oscillations and co-existing bistable/oscillatory responses. Transitions between these distinct modes of behaviour are enabled by modulating DEPTOR expression alone. We characterise the governing conditions for the observed dynamics by elucidating the network in its vast multi-dimensional parameter space, and develop strategies to identify core network design motifs underlying these dynamics. Our findings provide new systems-level insights into the complexity of mTOR signalling contributed by DEPTOR.

Computational Modeling of the Dynamics of Spatiotemporal Rho GTPase Signaling: A Systematic Review

The Rho family of GTPases are known to play pivotal roles in the regulation of fundamental cellular processes, ranging from cell migration and polarity to wound healing and regulation of actin cytoskeleton. Over the past decades, accumulating experimental work has increasingly mapped out the mechanistic details and interactions between members of the family and their regulators, establishing detailed interaction circuits within the Rho GTPase signaling network. These circuits have served as a vital foundation based on which a multitude of mathematical models have been developed to explain experimental data, gain deeper insights into the biological phenomenon they describe, as well as make new testable predictions and hypotheses. Due to the diverse nature and purpose of these models, they often vary greatly in size, scope, complexity, and formulation. Here, we provide a systematic, categorical, and comprehensive account of the recent modeling studies of Rho family GTPases, with an aim to offer a broad perspective of the field. The modeling limitations and possible future research directions are also discussed.

Dissecting Cell-Fate Determination Through Integrated Mathematical Modeling of the ERK/MAPK Signaling Pathway

The past three decades have witnessed an enormous progress in the elucidation of the ERK/MAPK signaling pathway and its involvement in various cellular processes. Because of its importance and complex wiring, the ERK pathway has been an intensive subject for mathematical modeling, which facilitates the unraveling of key dynamic properties and behaviors of the pathway. Recently, however, it became evident that the pathway does not act in isolation but closely interacts with many other pathways to coordinate various cellular outcomes under different pathophysiological contexts. This has led to an increasing number of integrated, large-scale models that link the ERK pathway to other functionally important pathways. In this chapter, we first discuss the essential steps in model development and notable models of the ERK pathway. We then use three examples of integrated, multipathway models to investigate how crosstalk of ERK signaling with other pathways regulates cell-fate decision-making in various physiological and disease contexts. Specifically, we focus on ERK interactions with the phosphoinositide-3 kinase (PI3K), c-Jun N-terminal kinase (JNK), and β-adrenergic receptor (β-AR) signaling pathways. We conclude that integrated modeling in combination with wet-lab experimentation have been and will be instrumental in gaining an in-depth understanding of ERK signaling in multiple biological contexts.

Mir143-BBC3 cascade reduces microglial survival via interplay between apoptosis and autophagy: Implications for methamphetamine-mediated neurotoxicity

BBC3 (BCL2 binding component 3) is a known apoptosis inducer; however, its role in microglial survival remains poorly understood. In addition to the classical transcription factor TRP53, Mir143 is involved in BBC3 expression at the post-transcriptional level. Here, we identify unique roles of Mir143-BBC3 in mediating microglial survival via the regulation of the interplay between apoptosis and autophagy. Autophagy inhibition accelerated methamphetamine-induced apoptosis, whereas autophagy induction attenuated the decrease in microglial survival. Moreover, anti-Mir143-dependent BBC3 upregulation reversed the methamphetamine-induced decrease in microglial survival via the regulation of apoptosis and autophagy. The in vivo relevance of these findings was confirmed in mouse models, which demonstrated that the microinjection of anti-Mir143 into the hippocampus ameliorated the methamphetamine-induced decrease in microglia as well as that observed in heterozygous Mir143(+/-) mice. These findings provide new insight regarding the specific contributions of Mir143-BBC3 to microglial survival in the context of drug abuse.

HER2-HER3 dimer quantification by FLIM-FRET predicts breast cancer metastatic relapse independently of HER2 IHC status

Overexpression of HER2 is an important prognostic marker, and the only predictive biomarker of response to HER2-targeted therapies in invasive breast cancer. HER2-HER3 dimer has been shown to drive proliferation and tumor progression, and targeting of this dimer with pertuzumab alongside chemotherapy and trastuzumab, has shown significant clinical utility. The purpose of this study was to accurately quantify HER2-HER3 dimerisation in formalin fixed paraffin embedded (FFPE) breast cancer tissue as a novel prognostic biomarker.FFPE tissues were obtained from patients included in the METABRIC (Molecular Taxonomy of Breast Cancer International Consortium) study. HER2-HER3 dimerisation was quantified using an improved fluorescence lifetime imaging microscopy (FLIM) histology-based analysis. Analysis of 131 tissue microarray cores demonstrated that the extent of HER2-HER3 dimer formation as measured by Förster Resonance Energy Transfer (FRET) determined through FLIM predicts the likelihood of metastatic relapse up to 10 years after surgery (hazard ratio 3.91 (1.61-9.5), p = 0.003) independently of HER2 expression, in a multivariate model. Interestingly there was no correlation between the level of HER2 protein expressed and HER2-HER3 heterodimer formation. We used a mathematical model that takes into account the complex interactions in a network of all four HER proteins to explain this counterintuitive finding.Future utility of this technique may highlight a group of patients who do not overexpress HER2 protein but are nevertheless dependent on the HER2-HER3 heterodimer as driver of proliferation. This assay could, if validated in a group of patients treated with, for instance pertuzumab, be used as a predictive biomarker to predict for response to such targeted therapies.

Protein-protein interactions generate hidden feedback and feed-forward loops to trigger bistable switches, oscillations and biphasic dose-responses

Protein-protein interactions (PPIs) defined as reversible association of two proteins to form a complex, are undoubtedly among the most common interaction motifs featured in cells. Recent large-scale proteomic studies have revealed an enormously complex interactome of the cell, consisting of tens of thousands of PPIs with numerous signalling hubs. PPIs have functional roles in regulating a wide range of cellular processes including signal transduction and post-translational modifications, and de-regulation of PPIs is implicated in many diseases including cancers and neuro-degenerative disorders. Despite the ubiquitous appearance and physiological significance of PPIs, our understanding of the dynamic and functional consequences of these simple motifs remains incomplete, particularly when PPIs occur within large biochemical networks. We employ quantitative, dynamic modelling to computationally analyse salient dynamic features of the PPI motifs and PPI-containing signalling networks varying in topological architecture. Our analyses surprisingly reveal that simple reversible PPI motifs, when being embedded into signalling cascades, could give rise to extremely rich and complex regulatory dynamics in the absence of explicit positive and negative feedback loops. Our work represents a systematic investigation of the dynamic properties of PPIs in signalling networks, and the results shed light on how this simple event may potentiate diverse and intricate behaviours in vivo.

Substrate-Trapped Interactors of PHD3 and FIH Cluster in Distinct Signaling Pathways

Amino acid hydroxylation is a post-translational modification that regulates intra- and inter-molecular protein-protein interactions. The modifications are regulated by a family of 2-oxoglutarate- (2OG) dependent enzymes and, although the biochemistry is well understood, until now only a few substrates have been described for these enzymes. Using quantitative interaction proteomics, we screened for substrates of the proline hydroxylase PHD3 and the asparagine hydroxylase FIH, which regulate the HIF-mediated hypoxic response. We were able to identify hundreds of potential substrates. Enrichment analysis revealed that the potential substrates of both hydroxylases cluster in the same pathways but frequently modify different nodes of signaling networks. We confirm that two proteins identified in our screen, MAPK6 (Erk3) and RIPK4, are indeed hydroxylated in a FIH- or PHD3-dependent mechanism. We further determined that FIH-dependent hydroxylation regulates RIPK4-dependent Wnt signaling, and that PHD3-dependent hydroxylation of MAPK6 protects the protein from proteasomal degradation.

Understanding complexity in the HIF signaling pathway using systems biology and mathematical modeling

Hypoxia is a common micro-environmental stress which is experienced by cells during a range of physiologic and pathophysiologic processes. The identification of the hypoxia-inducible factor (HIF) as the master regulator of the transcriptional response to hypoxia transformed our understanding of the mechanism underpinning the hypoxic response at the molecular level and identified HIF as a potentially important new therapeutic target. It has recently become clear that multiple levels of regulatory control exert influence on the HIF pathway giving the response a complex and dynamic activity profile. These include positive and negative feedback loops within the HIF pathway as well as multiple levels of crosstalk with other signaling pathways. The emerging model reflects a multi-level regulatory network that affects multiple aspects of the physiologic response to hypoxia including proliferation, apoptosis, and differentiation. Understanding the interplay between the molecular mechanisms involved in the dynamic regulation of the HIF pathway at a systems level is critically important in defining new appropriate therapeutic targets for human diseases including ischemia, cancer, and chronic inflammation. Here, we review our current knowledge of the regulatory circuits which exert influence over the HIF response and give examples of in silico model-based predictions of the dynamic behaviour of this system.

Molecular Mechanisms Involving Sigma-1 Receptor in Cell Apoptosis of BV-2 Microglial Cells Induced by Methamphetamine

It has been well documented that methamphetamine induces microglial activation and death, however, the molecular mechanisms underlying this process remain poorly understood. In the present study, we demonstrated the involvement of sigma-1 receptor (σ-1R) in methamphetamine-mediated microglial apoptosis. Exposure of BV-2 cells to methamphetamine induces cell apoptosis through its cognate receptor σ-1R, followed by activation of the mitogen-activated protein kinases, phosphatidylinositol-3' kinase/Akt as well as the downstream transcription factor p53 pathways. Blockage of σ -1R significantly inhibited the increased pro-apoptotic proteins such as Bax, Caspase-3 and Caspase-9 induced by methamphetamine. In conclusion, these findings underscore the critical role of σ-1R in microglial apoptosis induced by methamphetamine. Understanding the link between σ -1R and apoptosis will lead to development of therapeutic strategies targeting methamphetamine-mediated microglial death/dysfunction.

Phosphorylation of RAF Kinase Dimers Drives Conformational Changes that Facilitate Transactivation

RAF kinases are key players in the MAPK signaling pathway and are important targets for personalized cancer therapy. RAF dimerization is part of the physiological activation mechanism, together with phosphorylation, and is known to convey resistance to RAF inhibitors. Herein, molecular dynamics simulations are used to show that phosphorylation of a key N-terminal acidic (NtA) motif facilitates RAF dimerization by introducing several interprotomer salt bridges between the αC-helix and charged residues upstream of the NtA motif. Additionally, we show that the R-spine of RAF interacts with a conserved Trp residue in the vicinity of the NtA motif, connecting the active sites of two protomers and thereby modulating the cooperative interactions in the RAF dimer. Our findings provide a first structure-based mechanism for the auto-transactivation of RAF and could be generally applicable to other kinases, opening new pathways for overcoming dimerization-related drug resistance.

Relationship between serum response factor and androgen receptor in prostate cancer

BACKGROUND

Serum response factor (SRF) is an important transcription factor in castrate-resistant prostate cancer (CRPC). Since CRPC is associated with androgen receptor (AR) hypersensitivity, we investigated the relationship between SRF and AR.

MATERIALS AND METHODS

Transcriptional activity was assessed by luciferase assay. Cell proliferation was measured by MTT and flow cytometry. Protein expression in patients was assessed by immunohistochemistry.

RESULTS

To investigate AR involvement in SRF response to androgen, AR expression was down-regulated using siRNA. This resulted in the abrogation of SRF induction post-DHT. Moreover, DHT stimulation failed to induce SRF transcriptional activity in AR-negative PC346 DCC cells, which was only restored following AR over-expression. Next, SRF expression was down-regulated by siRNA, resulting in AR increased transcriptional activity in castrate-resistant LNCaP Abl cells but not in the parental LNCaP. This negative feedback loop in the resistant cells was confirmed by immunohistochemistry which showed a negative correlation between AR and SRF expression in CRPC bone metastases and a positive correlation in androgen-naïve prostatectomies. Cell proliferation was next assessed following SRF inhibition, demonstrating that SRF inhibition is more effective than AR inhibition in castrate-resistant cells.

CONCLUSION

Our data support SRF as a promising therapeutic target in combination with current treatments.

MST2-RASSF protein-protein interactions through SARAH domains

The detailed, atomistic-level understanding of molecular signaling along the tumor-suppressive Hippo signaling pathway that controls tissue homeostasis by balancing cell proliferation and death through apoptosis is a promising avenue for the discovery of novel anticancer drug targets. The activation of kinases such as Mammalian STE20-Like Protein Kinases 1 and 2 (MST1 and MST2)-modulated through both homo- and heterodimerization (e.g. interactions with Ras association domain family, RASSF, enzymes)-is a key upstream event in this pathway and remains poorly understood. On the other hand, RASSFs (such as RASSF1A or RASSF5) act as important apoptosis activators and tumor suppressors, although their exact regulatory roles are also unclear. We present recent molecular studies of signaling along the Ras-RASSF-MST pathway, which controls growth and apoptosis in eukaryotic cells, including a variety of modern molecular modeling and simulation techniques. Using recently available structural information, we discuss the complex regulatory scenario according to which RASSFs perform dual signaling functions, either preventing or promoting MST2 activation, and thus control cell apoptosis. Here, we focus on recent studies highlighting the special role being played by the specific interactions between the helical Salvador/RASSF/Hippo (SARAH) domains of MST2 and RASSF1a or RASSF5 enzymes. These studies are crucial for integrating atomistic-level mechanistic information about the structures and conformational dynamics of interacting proteins, with information available on their system-level functions in cellular signaling.

Competing to coordinate cell fate decisions: the MST2-Raf-1 signaling device

How do biochemical signaling pathways generate biological specificity? This question is fundamental to modern biology, and its enigma has been accentuated by the discovery that most proteins in signaling networks serve multifunctional roles. An answer to this question may lie in analyzing network properties rather than individual traits of proteins in order to elucidate design principles of biochemical networks that enable biological decision-making. We discuss how this is achieved in the MST2/Hippo-Raf-1 signaling network with the help of mathematical modeling and model-based analysis, which showed that competing protein interactions with affinities controlled by dynamic protein modifications can function as Boolean computing devices that determine cell fate decisions. In addition, we discuss areas of interest for future research and highlight how systems approaches would be of benefit.

Ubiquitin chain specific auto-ubiquitination triggers sustained oscillation, bistable switches and excitable firing

Ubiquitin modification of cellular proteins commonly targets them for proteosomal degradation, but can also convey non‐proteolytic functions. Over the past years, advances in experimental approaches have helped uncover the extensive involvement of ubiquitination in protein regulation. However, our understanding of the dynamics of the ubiquitination‐related networks have lagged behind. A common regulatory theme for many E3 ligases is the ability to self‐catalyse their own ubiquitination without involving external E3 ligating enzymes. Here, the authors have explored computational models of both proteolytic and non‐proteolytic auto‐ubiquitination of E3 ligases and characterised the dynamic properties of these regulatory motifs. Remarkably, in both cases auto‐ubiquitination coupled with multi‐step de‐ubiquitination process can bring about sustained oscillatory behaviour. In addition, the same basic wiring structures can trigger bistable switches of activity and excitable firing of the dynamic responses of the ubiquitinated E3 ligase. Bifurcation analysis allows one to derive parametric conditions that govern these dynamics. They also show that these complex non‐linear behaviours persist for a more detailed mechanistic description that involves the E1 and E2 enzymes. Their work therefore provides new insights into the dynamic features of auto‐ubiquitination in different cellular contexts.

Nonlinear signalling networks and cell-to-cell variability transform external signals into broadly distributed or bimodal responses

We show theoretically and experimentally a mechanism behind the emergence of wide or bimodal protein distributions in biochemical networks with nonlinear input-output characteristics (the dose-response curve) and variability in protein abundance. Large cell-to-cell variation in the nonlinear dose-response characteristics can be beneficial to facilitate two distinct groups of response levels as opposed to a graded response. Under the circumstances that we quantify mathematically, the two distinct responses can coexist within a cellular population, leading to the emergence of a bimodal protein distribution. Using flow cytometry, we demonstrate the appearance of wide distributions in the hypoxia-inducible factor-mediated response network in HCT116 cells. With help of our theoretical framework, we perform a novel calculation of the magnitude of cell-to-cell heterogeneity in the dose-response obtained experimentally.

Species differential regulation of COX2 can be described by an NFκB-dependent logic AND gate

Cyclooxygenase 2 (COX2), a key regulatory enzyme of the prostaglandin/eicosanoid pathway, is an important target for anti-inflammatory therapy. It is highly induced by pro-inflammatory cytokines in a Nuclear factor kappa B (NFκB)-dependent manner. However, the mechanisms determining the amplitude and dynamics of this important pro-inflammatory event are poorly understood. Furthermore, there is significant difference between human and mouse COX2 expression in response to the inflammatory stimulus tumor necrosis factor alpha (TNFα). Here, we report the presence of a molecular logic AND gate composed of two NFκB response elements (NREs) which controls the expression of human COX2 in a switch-like manner. Combining quantitative kinetic modeling and thermostatistical analysis followed by experimental validation in iterative cycles, we show that the human COX2 expression machinery regulated by NFκB displays features of a logic AND gate. We propose that this provides a digital, noise-filtering mechanism for a tighter control of expression in response to TNFα, such that a threshold level of NFκB activation is required before the promoter becomes active and initiates transcription. This NFκB-regulated AND gate is absent in the mouse COX2 promoter, most likely contributing to its differential graded response in promoter activity and protein expression to TNFα. Our data suggest that the NFκB-regulated AND gate acts as a novel mechanism for controlling the expression of human COX2 to TNFα, and its absence in the mouse COX2 provides the foundation for further studies on understanding species-specific differential gene regulation.

Integrated signaling pathway and gene expression regulatory model to dissect dynamics of Escherichia coli challenged mammary epithelial cells

Cells transform external stimuli, through the activation of signaling pathways, which in turn activate gene regulatory networks, in gene expression. As more omics data are generated from experiments, eliciting the integrated relationship between the external stimuli, the signaling process in the cell and the subsequent gene expression is a major challenge in systems biology. The complex system of non-linear dynamic protein interactions in signaling pathways and gene networks regulates gene expression. The complexity and non-linear aspects have resulted in the study of the signaling pathway or the gene network regulation in isolation. However, this limits the analysis of the interaction between the two components and the identification of the source of the mechanism differentiating the gene expression profiles. Here, we present a study of a model of the combined signaling pathway and gene network to highlight the importance of integrated modeling. Based on the experimental findings we developed a compartmental model and conducted several simulation experiments. The model simulates the mRNA expression of three different cytokines (RANTES, IL8 and TNFα) regulated by the transcription factor NFκB in mammary epithelial cells challenged with E. coli. The analysis of the gene network regulation identifies a lack of robustness and therefore sensitivity for the transcription factor regulation. However, analysis of the integrated signaling and gene network regulation model reveals distinctly different underlying mechanisms in the signaling pathway responsible for the variation between the three cytokine's mRNA expression levels. Our key findings reveal the importance of integrating the signaling pathway and gene expression dynamics in modeling. Modeling infers valid research questions which need to be verified experimentally and can assist in the design of future biological experiments.

Abstract 4599: Negative feedback loop between serum response factor and androgen receptor in castrate-resistant prostate cancer

Despite initial response to androgen ablation, patients with advanced disease relapse to develop castrate-resistant prostate cancer (CRPC) which is difficult to treat. We previously identified Serum Response Factor (SRF) as an important transcription factor in an in vitro model of CRPC. Since CRPC is associated with androgen receptor (AR) hypersensitivity and previous studies linked SRF with AR, we investigated the relationship between SRF and AR.

DHT stimulation increased SRF transcriptional activity in both parental LNCaP cells and their castration-resistant Abl subline. To investigate AR involvement in this process, AR expression was down-regulated using siRNA, resulting in the abrogation of SRF induction post-DHT. Moreover, DHT stimulation failed to induce SRF transcriptional activity in the AR-negative PC346 DCC cells. However, when AR was over-expressed, SRF induction post-DHT was restored. To explore whether this was reciprocal, SRF was down-regulated by siRNA and AR transcriptional activity and protein expression were assessed. These experiments showed AR increased activity and expression following SRF knock-down, suggesting a negative feedback loop, which was confirmed by immunohistochemistry (IHC) in CRPC bone metastases (n=42), showing a negative association between SRF and AR positivity. This negative feedback was also supported by a computational model based on our experimental data.

We next assessed cell proliferation following SRF inhibition and demonstrated that SRF inhibition by siRNA or CCG-1423 was as effective as AR inhibition in decreasing cell proliferation. Moreover concomitant inhibition of AR and SRF showed a synergistic effect.

To confirm SRF relevance in clinical samples, we performed IHC staining for SRF on 94 primary tumours and 21 castration-resistant patients who received a transurethral resection of the prostate. This analysis showed that SRF nuclear staining was associated with castration-resistance with 95% of castration-resistant patients showing positive SRF staining versus only 50% of primary tumours. In addition SRF expression in bone metastases in a cohort of 38 patients who died of CRPC, was an independent predictor of survival from (a) prostate cancer diagnosis and (b) diagnosis with CRPC.

Overall our data highlight the importance of SRF in CRPC and support SRF as a promising therapeutic target alone or in combination with current treatments.

Citation Format: Maria Prencipe, Gillian O'Hurley, Amanda O'Neill, Dara Lundon, Lan K. Nguyen, Susie Boyce, Colm Morrissey, Helmut Klocker, Elaine W. Kay, William Gallagher, William Watson. Negative feedback loop between serum response factor and androgen receptor in castrate-resistant prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4599. doi:10.1158/1538-7445.AM2014-4599

The ErbB4 CYT2 variant protects EGFR from ligand-induced degradation to enhance cancer cell motility

The epidermal growth factor receptor (EGFR) is a member of the ErbB family that can promote the migration and proliferation of breast cancer cells. Therapies that target EGFR can promote the dimerization of EGFR with other ErbB receptors, which is associated with the development of drug resistance. Understanding how interactions among ErbB receptors alter EGFR biology could provide avenues for improving cancer therapy. We found that EGFR interacted directly with the CYT1 and CYT2 variants of ErbB4 and the membrane-anchored intracellular domain (mICD). The CYT2 variant, but not the CYT1 variant, protected EGFR from ligand-induced degradation by competing with EGFR for binding to a complex containing the E3 ubiquitin ligase c-Cbl and the adaptor Grb2. Cultured breast cancer cells overexpressing both EGFR and ErbB4 CYT2 mICD exhibited increased migration. With molecular modeling, we identified residues involved in stabilizing the EGFR dimer. Mutation of these residues in the dimer interface destabilized the complex in cells and abrogated growth factor-stimulated cell migration. An exon array analysis of 155 breast tumors revealed that the relative mRNA abundance of the ErbB4 CYT2 variant was increased in ER+ HER2- breast cancer patients, suggesting that our findings could be clinically relevant. We propose a mechanism whereby competition for binding to c-Cbl in an ErbB signaling heterodimer promotes migration in response to a growth factor gradient.

Polyubiquitin chain assembly and organization determine the dynamics of protein activation and degradation

Protein degradation via ubiquitination is a major proteolytic mechanism in cells. Once a protein is destined for degradation, it is tagged by multiple ubiquitin (Ub) molecules. The synthesized polyubiquitin chains can be recognized by the 26S proteosome where proteins are degraded. These chains form through multiple ubiquitination cycles that are similar to multi-site phosphorylation cycles. As kinases and phosphatases, two opposing enzymes (E3 ligases and deubiquitinases DUBs) catalyze (de)ubiquitination cycles. Although multi-ubiquitination cycles are fundamental mechanisms of controlling protein concentrations within a cell, their dynamics have never been explored. Here, we fill this knowledge gap. We show that under permissive physiological conditions, the formation of polyubiquitin chain of length greater than two and subsequent degradation of the ubiquitinated protein, which is balanced by protein synthesis, can display bistable, switch-like responses. Interestingly, the occurrence of bistability becomes pronounced, as the chain grows, giving rise to “all-or-none” regulation at the protein levels. We give predictions of protein distributions under bistable regime awaiting experimental verification. Importantly, we show for the first time that sustained oscillations can robustly arise in the process of formation of ubiquitin chain, largely due to the degradation of the target protein. This new feature is opposite to the properties of multi-site phosphorylation cycles, which are incapable of generating oscillation if the total abundance of interconverted protein forms is conserved. We derive structural and kinetic constraints for the emergence of oscillations, indicating that a competition between different substrate forms and the E3 and DUB is critical for oscillation. Our work provides the first detailed elucidation of the dynamical features brought about by different molecular setups of the polyubiquitin chain assembly process responsible for protein degradation.

Hypoxia-inducible factor (HIF) network: insights from mathematical models

Oxygen is a crucial molecule for cellular function. When oxygen demand exceeds supply, the oxygen sensing pathway centred on the hypoxia inducible factor (HIF) is switched on and promotes adaptation to hypoxia by up-regulating genes involved in angiogenesis, erythropoiesis and glycolysis. The regulation of HIF is tightly modulated through intricate regulatory mechanisms. Notably, its protein stability is controlled by the oxygen sensing prolyl hydroxylase domain (PHD) enzymes and its transcriptional activity is controlled by the asparaginyl hydroxylase FIH (factor inhibiting HIF-1).To probe the complexity of hypoxia-induced HIF signalling, efforts in mathematical modelling of the pathway have been underway for around a decade. In this paper, we review the existing mathematical models developed to describe and explain specific behaviours of the HIF pathway and how they have contributed new insights into our understanding of the network. Topics for modelling included the switch-like response to decreased oxygen gradient, the role of micro environmental factors, the regulation by FIH and the temporal dynamics of the HIF response. We will also discuss the technical aspects, extent and limitations of these models. Recently, HIF pathway has been implicated in other disease contexts such as hypoxic inflammation and cancer through crosstalking with pathways like NFκB and mTOR. We will examine how future mathematical modelling and simulation of interlinked networks can aid in understanding HIF behaviour in complex pathophysiological situations. Ultimately this would allow the identification of new pharmacological targets in different disease settings.

A dynamic model of the hypoxia-inducible factor 1α (HIF-1α) network

Activation of the hypoxia-inducible factor (HIF) pathway is a critical step in the transcriptional response to hypoxia. Although many of the key proteins involved have been characterised, the dynamics of their interactions in generating this response remain unclear. In the present study, we have generated a comprehensive mathematical model of the HIF-1α pathway based on core validated components and dynamic experimental data, and confirm the previously described connections within the predicted network topology. Our model confirms previous work demonstrating that the steps leading to optimal HIF-1α transcriptional activity require sequential inhibition of both prolyl- and asparaginyl-hydroxylases. We predict from our model (and confirm experimentally) that there is residual activity of the asparaginyl-hydroxylase FIH (factor inhibiting HIF) at low oxygen tension. Furthermore, silencing FIH under conditions where prolyl-hydroxylases are inhibited results in increased HIF-1α transcriptional activity, but paradoxically decreases HIF-1α stability. Using a core module of the HIF network and mathematical proof supported by experimental data, we propose that asparaginyl hydroxylation confers a degree of resistance upon HIF-1α to proteosomal degradation. Thus, through in vitro experimental data and in silico predictions, we provide a comprehensive model of the dynamic regulation of HIF-1α transcriptional activity by hydroxylases and use its predictive and adaptive properties to explain counter-intuitive biological observations.