Prediction and validation of the distinct dynamics of transient and sustained ERK activation

Energy Landscape Reveals the Underlying Mechanism of Cancer-Adipose Conversion in Gene Network Models

Cancer is a systemic heterogeneous disease involving complex molecular networks. Tumor formation involves an epithelial-mesenchymal transition (EMT), which promotes both metastasis and plasticity of cancer cells. Recent experiments have proposed that cancer cells can be transformed into adipocytes via a combination of drugs. However, the underlying mechanisms for how these drugs work, from a molecular network perspective, remain elusive. To reveal the mechanism of cancer-adipose conversion (CAC), this study adopts a systems biology approach by combing mathematical modeling and molecular experiments, based on underlying molecular regulatory networks. Four types of attractors are identified, corresponding to epithelial (E), mesenchymal (M), adipose (A) and partial/intermediate EMT (P) cell states on the CAC landscape. Landscape and transition path results illustrate that intermediate states play critical roles in the cancer to adipose transition. Through a landscape control approach, two new therapeutic strategies for drug combinations are identified, that promote CAC. These predictions are verified by molecular experiments in different cell lines. The combined computational and experimental approach provides a powerful tool to explore molecular mechanisms for cell fate transitions in cancer networks. The results reveal underlying mechanisms of intermediate cell states that govern the CAC, and identified new potential drug combinations to induce cancer adipogenesis.

Single cell spatial biology over developmental time can decipher pediatric brain pathologies

Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.

Mitogen signaling strength and duration can control cell cycle decisions

Decades ago, mitogen-promoted signaling duration and strength were observed to be sensed by the cell and to be critical for its decisions: to proliferate or differentiate. Landmark publications established the importance of mitogen signaling not only in the G1 cell cycle phase but also through the S and the G2/M transition. Despite these early milestones, how mitogen signal duration and strength, short and strong or weaker and sustained, control cell fate has been largely unheeded. Here, we center on cardinal signaling-related questions, including (i) how fluctuating mitogenic signals are converted into cell proliferation-differentiation decisions and (ii) why extended duration of weak signaling is associated with differentiation, while bursts of strong and short induce proliferation but, if too strong and long, induce irreversible senescence. Our innovative broad outlook harnesses cell biology and protein conformational ensembles, helping us to define signaling strength, clarify cell cycle decisions, and thus cell fate.

High-throughput and proteome-wide discovery of endogenous biomolecular condensates

Phase separation inside mammalian cells regulates the formation of the biomolecular condensates that are related to gene expression, signalling, development and disease. However, a large population of endogenous condensates and their candidate phase-separating proteins have yet to be discovered in a quantitative and high-throughput manner. Here we demonstrate that endogenously expressed biomolecular condensates can be identified across a cell's proteome by sorting proteins across varying oligomeric states. We employ volumetric compression to modulate the concentrations of intracellular proteins and the degree of crowdedness, which are physical regulators of cellular biomolecular condensates. The changes in degree of the partition of proteins into condensates or phase separation led to varying oligomeric states of the proteins, which can be detected by coupling density gradient ultracentrifugation and quantitative mass spectrometry. In total, we identified 1,518 endogenous condensate proteins, of which 538 have not been reported before. Furthermore, we demonstrate that our strategy can identify condensate proteins that respond to specific biological processes.

A guide to ERK dynamics, part 1: mechanisms and models

Extracellular signal-regulated kinase (ERK) has long been studied as a key driver of both essential cellular processes and disease. A persistent question has been how this single pathway is able to direct multiple cell behaviors, including growth, proliferation, and death. Modern biosensor studies have revealed that the temporal pattern of ERK activity is highly variable and heterogeneous, and critically, that these dynamic differences modulate cell fate. This two-part review discusses the current understanding of dynamic activity in the ERK pathway, how it regulates cellular decisions, and how these cell fates lead to tissue regulation and pathology. In part 1, we cover the optogenetic and live-cell imaging technologies that first revealed the dynamic nature of ERK, as well as current challenges in biosensor data analysis. We also discuss advances in mathematical models for the mechanisms of ERK dynamics, including receptor-level regulation, negative feedback, cooperativity, and paracrine signaling. While hurdles still remain, it is clear that higher temporal and spatial resolution provide mechanistic insights into pathway circuitry. Exciting new algorithms and advanced computational tools enable quantitative measurements of single-cell ERK activation, which in turn inform better models of pathway behavior. However, the fact that current models still cannot fully recapitulate the diversity of ERK responses calls for a deeper understanding of network structure and signal transduction in general.

Identifying Fibroblast Growth Factor Receptor 3 as a Mediator of Periosteal Osteochondral Differentiation through the Construction of microRNA-Based Interaction Networks

Human periosteum-derived progenitor cells (hPDCs) have the ability to differentiate towards both the chondrogenic and osteogenic lineages. This coordinated and complex osteochondrogenic differentiation process permits endochondral ossification and is essential in bone development and repair. We have previously shown that humanised cultures of hPDCs enhance their osteochondrogenic potentials in vitro and in vivo; however, the underlying mechanisms are largely unknown. This study aimed to identify novel regulators of hPDC osteochondrogenic differentiation through the construction of miRNA-mRNA regulatory networks derived from hPDCs cultured in human serum or foetal bovine serum as an alternative in silico strategy to serum characterisation. Sixteen differentially expressed miRNAs (DEMis) were identified in the humanised culture. In silico analysis of the DEMis with TargetScan allowed for the identification of 1503 potential miRNA target genes. Upon comparison with a paired RNAseq dataset, a 4.5% overlap was observed (122 genes). A protein-protein interaction network created with STRING interestingly identified FGFR3 as a key network node, which was further predicted using multiple pathway analyses. Functional analysis revealed that hPDCs with the activating mutation FGFR3N540K displayed increased expressions of chondrogenic gene markers when cultured under chondrogenic conditions in vitro and displayed enhanced endochondral bone formation in vivo. A further histological analysis uncovered known downstream mediators involved in FGFR3 signalling and endochondral ossification to be upregulated in hPDC FGFR3N540K-seeded implants. This combinational approach of miRNA-mRNA-protein network analysis with in vitro and in vivo characterisation has permitted the identification of FGFR3 as a novel mediator of hPDC biology. Furthermore, this miRNA-based workflow may also allow for the identification of drug targets, which may be of relevance in instances of delayed fracture repair.

Photoswitchable binders enable temporal dissection of endogenous protein function

General methods for spatiotemporal control of specific endogenous proteins would be broadly useful for probing protein function in living cells. Synthetic protein binders that bind and inhibit endogenous protein targets can be obtained from nanobodies, designed ankyrin repeat proteins (DARPins), and other small protein scaffolds, but generalizable methods to control their binding activity are lacking. Here, we report robust single-chain photoswitchable DARPins (psDARPins) for bidirectional optical control of endogenous proteins. We created topological variants of the DARPin scaffold by computer-aided design so fusion of photodissociable dimeric Dronpa (pdDronpa) results in occlusion of target binding at baseline. Cyan light induces pdDronpa dissociation to expose the binding surface (paratope), while violet light restores pdDronpa dimerization and paratope caging. Since the DARPin redesign leaves the paratope intact, the approach was easily applied to existing DARPins for GFP, ERK, and Ras, as demonstrated by relocalizing GFP-family proteins and inhibiting endogenous ERK and Ras with optical control. Finally, a Ras-targeted psDARPin was used to determine that, following EGF-activation of EGFR, Ras is required for sustained EGFR to ERK signaling. In summary, psDARPins provide a generalizable strategy for precise spatiotemporal dissection of endogenous protein function.

The kinetics of signaling through the common FcRγ chain determine cytokine profiles in dendritic cells

The common Fc receptor γ (FcRγ) chain is a signaling subunit common to several immune receptors, but cellular responses induced by FcRγ-coupled receptors are diverse. We investigated the mechanisms by which FcRγ generates divergent signals when coupled to Dectin-2 and Mincle, structurally similar C-type lectin receptors that induce the release of different cytokines from dendritic cells. Chronological tracing of transcriptomic and epigenetic changes upon stimulation revealed that Dectin-2 induced early and strong signaling, whereas Mincle-mediated signaling was delayed, which reflects their expression patterns. Generation of early and strong FcRγ-Syk signaling by engineered chimeric receptors was sufficient to recapitulate a Dectin-2-like gene expression profile. Early Syk signaling selectively stimulated the activity of the calcium ion-activated transcription factor NFAT, which rapidly altered the chromatin status and transcription of the Il2 gene. In contrast, proinflammatory cytokines, such as TNF, were induced regardless of FcRγ signaling kinetics. These results suggest that the strength and timing of FcRγ-Syk signaling can alter the quality of cellular responses through kinetics-sensing signaling machineries.

Rate thresholds in cell signaling have functional and phenotypic consequences in non-linear time-dependent environments

All cells employ signal transduction pathways to respond to physiologically relevant extracellular cytokines, stressors, nutrient levels, hormones, morphogens, and other stimuli that vary in concentration and rate in healthy and diseased states. A central unsolved fundamental question in cell signaling is whether and how cells sense and integrate information conveyed by changes in the rate of extracellular stimuli concentrations, in addition to the absolute difference in concentration. We propose that different environmental changes over time influence cell behavior in addition to different signaling molecules or different genetic backgrounds. However, most current biomedical research focuses on acute environmental changes and does not consider how cells respond to environments that change slowly over time. As an example of such environmental change, we review cell sensitivity to environmental rate changes, including the novel mechanism of rate threshold. A rate threshold is defined as a threshold in the rate of change in the environment in which a rate value below the threshold does not activate signaling and a rate value above the threshold leads to signal activation. We reviewed p38/Hog1 osmotic stress signaling in yeast, chemotaxis and stress response in bacteria, cyclic adenosine monophosphate signaling in Amoebae, growth factors signaling in mammalian cells, morphogen dynamics during development, temporal dynamics of glucose and insulin signaling, and spatio-temproral stressors in the kidney. These reviewed examples from the literature indicate that rate thresholds are widespread and an underappreciated fundamental property of cell signaling. Finally, by studying cells in non-linear environments, we outline future directions to understand cell physiology better in normal and pathophysiological conditions.

Stochastic model of ERK-mediated progesterone receptor translocation, clustering and transcriptional activity

Progesterone receptor (PR) transcriptional activity is a key factor in the differentiation of the uterine endometrium. By consequence, progestin has been identified as an important treatment modality for endometrial cancer. PR transcriptional activity is controlled by extracellular-signal-regulated kinase (ERK) mediated phosphorylation, downstream of growth factor receptors such as EGFR. However, phosphorylation of PR also targets it for ubiquitination and destruction in the proteasome. Quantitative studies of these opposing roles are much needed toward validation of potential new progestin-based therapeutics. In this work, we propose a spatial stochastic model to study the effects of the opposing roles for PR phosphorylation on the levels of active transcription factor. Our numerical simulations confirm earlier in vitro experiments in endometrial cancer cell lines, identifying clustering as a mechanism that amplifies the ability of progesterone receptors to influence gene transcription. We additionally show the usefulness of a statistical method we developed to quantify and control variations in stochastic simulations in general biochemical systems, assisting modelers in defining minimal but meaningful numbers of simulations while guaranteeing outputs remain within a pre-defined confidence level.

Computational investigation of the dynamic control of cAMP signaling by PDE4 isoform types

Cyclic adenosine monophosphate (cAMP) is a generic signaling molecule that, through precise control of its signaling dynamics, exerts distinct cellular effects. Consequently, aberrant cAMP signaling can have detrimental effects. Phosphodiesterase 4 (PDE4) enzymes profoundly control cAMP signaling and comprise different isoform types of which the enzymatic activity is modulated by differential feedback mechanisms. Because these feedback dynamics are non-linear and occur coincidentally, their effects are difficult to examine experimentally, but can be well simulated computationally. Through understanding the role of PDE4 isoform types in regulating cAMP signaling, PDE4-targeted therapeutic strategies can be better specified. Here, we established a computational model to study how feedback mechanisms on different PDE4 isoform types lead to dynamic, isoform-specific control of cAMP signaling. Ordinary differential equations describing cAMP dynamics were implemented in the VirtualCell (VCell) environment. Simulations indicated that long PDE4 isoforms exert the most profound control on oscillatory cAMP signaling, as opposed to the PDE4-mediated control of single cAMP input pulses. Moreover, elevating cAMP levels or decreasing PDE4 levels revealed different effects on downstream signaling. Together these results underline that cAMP signaling is distinctly regulated by different PDE4 isoform types and that this isoform-specificity should be considered in both computational and experimental follow-up studies to better define PDE4 enzymes as therapeutic targets in diseases in which cAMP signaling is aberrant.

A Long-Lasting PARP1-Activation Mediates Signal-Induced Gene Expression

This overview presents recent evidence for a long-lasting PARP1 activation by a variety of signal transduction mechanisms, mediating signal-induced gene expression and chromatin remodeling. This mode of PARP1 activation has been reported in a variety of cell types, under physiological conditions. In this mechanism, PARP1 is not transiently activated by binding to DNA breaks. Moreover, damaged DNA interfered with this long-lasting PARP1 activation.

A text-based computational framework for patient -specific modeling for classification of cancers

Patient heterogeneity precludes cancer treatment and drug development; hence, development of methods for finding prognostic markers for individual treatment is urgently required. Here, we present Pasmopy (Patient-Specific Modeling in Python), a computational framework for stratification of patients using in silico signaling dynamics. Pasmopy converts texts and sentences on biochemical systems into an executable mathematical model. Using this framework, we built a model of the ErbB receptor signaling network, trained in cultured cell lines, and performed in silico simulation of 377 patients with breast cancer using The Cancer Genome Atlas (TCGA) transcriptome datasets. The temporal dynamics of Akt, extracellular signal-regulated kinase (ERK), and c-Myc in each patient were able to accurately predict the difference in prognosis and sensitivity to kinase inhibitors in triple-negative breast cancer (TNBC). Our model applies to any type of signaling network and facilitates the network-based use of prognostic markers and prediction of drug response.

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A text file describing biochemical systems is converted into an executable model

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Patient-specific models incorporate individual gene expression profiles

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In silico signaling dynamics can be utilized as prognostic biomarkers

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Personalized kinetic models are capable of predicting potential drug targets

Regulation of STAT3 signaling in IFNγ and IL10 pathways and in their cross-talk

The pro-inflammatory IFNγ-STAT1 pathway and anti-inflammatory IL10-STAT3 pathway elicit cellular responses primarily utilizing their canonical STATs. However IL10 mediated STAT1 and IFNγ mediated STAT3 activation is also observed, suggesting crosstalk of these functionally opposing signaling pathways can potentially reshape the canonical dynamics both STATs and alter the expression of their target genes. Herein, we measured the dynamics of STATs in response to different doses of IL10 or IFNγ and in their co-stimulation and employed quantitative modeling to understand the regulatory mechanisms controlling signal responses in individual and co-simulation scenarios. Our experiments show, STAT3 in particular, exhibits a bell-shaped dose-response while treated with IFNγ or IL10 and our model quantiatively captured the dose-dependent dynamics of both the STATs in both pathways. The model next predicted and subsequent experiments validated that STAT3 dynamics would robustly remain IL10 specific when subjected to a co-stimulation of both IFNγ and IL10. Genes common to both pathways also exhibited IL10 specific expression during the co-stimulation. The findings thus uncover anovel feature of the IL10-STAT3 signaling axis during pathway crosstalk. Finally, parameter sampling coupled to information theory based analysis showed that bell-shaped signal-response of STAT3 in both pathways is primarily dependent on receptor concentration whereas robustness of IL10-STAT3 signaling axis in co-stimulation results from the negative regulation of the IFNγ pathway.

Design and implementation of a microfluidic device capable of temporal growth factor delivery reveal filtering capabilities of the EGFR/ERK pathway

Utilizing microfluidics to mimic the dynamic temporal changes of growth factor and cytokine concentrations in vivo has greatly increased our understanding of how signal transduction pathways are structured to encode extracellular stimuli. To date, these devices have focused on delivering pulses of varying frequency, and there are limited cell culture models for delivering slowly increasing concentrations of stimuli that cells may experience in vivo. To examine this setting, we developed and validated a microfluidic device that can deliver increasing concentrations of growth factor over periods ranging from 6 to 24 h. Using this device and a fluorescent biosensor of extracellular-regulated kinase (ERK) activity, we delivered a slowly increasing concentration of epidermal growth factor (EGF) to human mammary epithelial cells and surprisingly observed minimal ERK activation, even at concentrations that stimulate robust activity in bolus delivery. The cells remained unresponsive to subsequent challenges with EGF, and immunocytochemistry suggested that the loss of an epidermal growth factor receptor was responsible. Cells were then challenged with faster rates of change of EGF, revealing an increased ERK activity as a function of rate of change. Specifically, both the fraction of cells that responded and the length of ERK activation time increased with the rate of change. This microfluidic device fills a gap in the current repertoire of in vitro microfluidic devices and demonstrates that slower, more physiological changes in growth factor presentation can reveal new regulatory mechanisms for how signal transduction pathways encode changes in the extracellular growth factor milieu.

Temporal pattern and synergy influence activity of ERK signaling pathways during L-LTP induction

Long-lasting long-term potentiation (L-LTP) is a cellular mechanism of learning and memory storage. Studies have demonstrated a requirement for extracellular signal-regulated kinase (ERK) activation in L-LTP produced by a diversity of temporal stimulation patterns. Multiple signaling pathways converge to activate ERK, with different pathways being required for different stimulation patterns. To answer whether and how different temporal patterns select different signaling pathways for ERK activation, we developed a computational model of five signaling pathways (including two novel pathways) leading to ERK activation during L-LTP induction. We show that calcium and cAMP work synergistically to activate ERK and that stimuli given with large intertrial intervals activate more ERK than shorter intervals. Furthermore, these pathways contribute to different dynamics of ERK activation. These results suggest that signaling pathways with different temporal sensitivities facilitate ERK activation to diversity of temporal patterns.

p52Shc regulates the sustainability of ERK activation in a RAF-independent manner

p52SHC (SHC) and GRB2 are adaptor proteins involved in the RAS/MAPK (ERK) pathway mediating signals from cell-surface receptors to various cytoplasmic proteins. To further examine their roles in signal transduction, we studied the translocation of fluorescently labeled SHC and GRB2 to the cell surface, caused by the activation of ERBB receptors by heregulin (HRG). We simultaneously evaluated activated ERK translocation to the nucleus. Unexpectedly, the translocation dynamics of SHC were sustained when those of GRB2 were transient. The sustained localization of SHC positively correlated with the sustained nuclear localization of ERK, which became more transient after SHC knockdown. SHC-mediated PI3K activation was required to maintain the sustainability of the ERK translocation regulating MEK but not RAF. In cells overexpressing ERBB1, SHC translocation became transient, and the HRG-induced cell fate shifted from a differentiation to a proliferation bias. Our results indicate that SHC and GRB2 functions are not redundant but that SHC plays the critical role in the temporal regulation of ERK activation.

Perfect adaptation in biology

A distinctive feature of many biological systems is their ability to adapt to persistent stimuli or disturbances that would otherwise drive them away from a desirable steady state. The resulting stasis enables organisms to function reliably while being subjected to very different external environments. This perspective concerns a stringent type of biological adaptation, robust perfect adaptation (RPA), that is resilient to certain network and parameter perturbations. As in engineered control systems, RPA requires that the regulating network satisfy certain structural constraints that cannot be avoided. We elucidate these ideas using biological examples from systems and synthetic biology. We then argue that understanding the structural constraints underlying RPA allows us to look past implementation details and offers a compelling means to unravel regulatory biological complexity.

Stochastic modulation evidences a transitory EGF-Ras-ERK MAPK activity induced by PRMT5

The extracellular signal-regulated kinase (ERK) mitogen-activated protein kinase (MAPK) pathway involves a three-step cascade of kinases that transduce signals and promote processes such as cell growth, development, and apoptosis. An aberrant response of this pathway is related to the proliferation of cell diseases and tumors. By using simulation modeling, we document that the protein arginine methyltransferase 5 (PRMT5) modulates the MAPK pathway and thus avoids an aberrant behavior. PRMT5 methylates the Raf kinase, reducing its catalytic activity and thereby, reducing the activation of ERK in time and amplitude. Two minimal computational models of the epidermal growth factor (EGF)-Ras-ERK MAPK pathway influenced by PRMT5 were proposed: a first model in which PRMT5 is activated by EGF and a second one in which PRMT5 is stimulated by the cascade response. The reported results show that PRMT5 reduces the time duration and the expression of the activated ERK in both cases, but only in the first model PRMT5 limits the EGF range that generates an ERK activation. Based on our data, we propose the protein PRMT5 as a regulatory factor to develop strategies to fight against an excessive activity of the MAPK pathway, which could be of use in chronic diseases and cancer.

A rate threshold mechanism regulates MAPK stress signaling and survival

Cells are exposed to changes in extracellular stimulus concentration that vary as a function of rate. However, how cells integrate information conveyed from stimulation rate along with concentration remains poorly understood. Here, we examined how varying the rate of stress application alters budding yeast mitogen-activated protein kinase (MAPK) signaling and cell behavior at the single-cell level. We show that signaling depends on a rate threshold that operates in conjunction with stimulus concentration to determine the timing of MAPK signaling during rate-varying stimulus treatments. We also discovered that the stimulation rate threshold and stimulation rate-dependent cell survival are sensitive to changes in the expression levels of the Ptp2 phosphatase, but not of another phosphatase that similarly regulates osmostress signaling during switch-like treatments. Our results demonstrate that stimulation rate is a regulated determinant of cell behavior and provide a paradigm to guide the dissection of major stimulation rate dependent mechanisms in other systems.

Light-regulated allosteric switch enables temporal and subcellular control of enzyme activity

Engineered allosteric regulation of protein activity provides significant advantages for the development of robust and broadly applicable tools. However, the application of allosteric switches in optogenetics has been scarce and suffers from critical limitations. Here, we report an optogenetic approach that utilizes an engineered Light-Regulated (LightR) allosteric switch module to achieve tight spatiotemporal control of enzymatic activity. Using the tyrosine kinase Src as a model, we demonstrate efficient regulation of the kinase and identify temporally distinct signaling responses ranging from seconds to minutes. LightR-Src off-kinetics can be tuned by modulating the LightR photoconversion cycle. A fast cycling variant enables the stimulation of transient pulses and local regulation of activity in a selected region of a cell. The design of the LightR module ensures broad applicability of the tool, as we demonstrate by achieving light-mediated regulation of Abl and bRaf kinases as well as Cre recombinase.

Kinetics of receptor tyrosine kinase activation define ERK signaling dynamics

In responses to activation of receptor tyrosine kinases (RTKs), crucial cell fate decisions depend on the duration and dynamics of ERK signaling. In PC12 cells, epidermal growth factor (EGF) induces transient ERK activation that leads to cell proliferation, whereas nerve growth factor (NGF) promotes sustained ERK activation and cell differentiation. These differences have typically been assumed to reflect distinct feedback mechanisms in the Raf-MEK-ERK signaling network, with the receptors themselves acting as simple upstream inputs. We failed to confirm the expected differences in feedback type when investigating transient versus sustained signaling downstream of the EGF receptor (EGFR) and NGF receptor (TrkA). Instead, we found that ERK signaling faithfully followed RTK dynamics when receptor signaling was modulated in different ways. EGFR activation kinetics, and consequently ERK signaling dynamics, were switched from transient to sustained when receptor internalization was inhibited with drugs or mutations, or when cells expressed a chimeric receptor likely to have impaired dimerization. In addition, EGFR and ERK signaling both became more sustained when substoichiometric levels of erlotinib were added to reduce duration of EGFR kinase activation. Our results argue that RTK activation kinetics play a crucial role in determining MAP kinase cascade signaling dynamics and cell fate decisions, and that signaling outcome can be modified by activating a given RTK in different ways.

Signaling diversity enabled by Rap1-regulated plasma membrane ERK with distinct temporal dynamics

A variety of different signals induce specific responses through a common, extracellular-signal regulated kinase (ERK)-dependent cascade. It has been suggested that signaling specificity can be achieved through precise temporal regulation of ERK activity. Given the wide distrubtion of ERK susbtrates across different subcellular compartments, it is important to understand how ERK activity is temporally regulated at specific subcellular locations. To address this question, we have expanded the toolbox of Förster Resonance Energy Transfer (FRET)-based ERK biosensors by creating a series of improved biosensors targeted to various subcellular regions via sequence specific motifs to measure spatiotemporal changes in ERK activity. Using these sensors, we showed that EGF induces sustained ERK activity near the plasma membrane in sharp contrast to the transient activity observed in the cytoplasm and nucleus. Furthermore, EGF-induced plasma membrane ERK activity involves Rap1, a noncanonical activator, and controls cell morphology and EGF-induced membrane protrusion dynamics. Our work strongly supports that spatial and temporal regulation of ERK activity is integrated to control signaling specificity from a single extracellular signal to multiple cellular processes.

The road to ERK activation: Do neurons take alternate routes?

The ERK cascade is a central signaling pathway that regulates a wide variety of cellular processes including proliferation, differentiation, learning and memory, development, and synaptic plasticity. A wide range of inputs travel from the membrane through different signaling pathway routes to reach activation of one set of output kinases, ERK1&2. The classical ERK activation pathway beings with growth factor activation of receptor tyrosine kinases. Numerous G-protein coupled receptors and ionotropic receptors also lead to ERK through increases in the second messengers calcium and cAMP. Though both types of pathways are present in diverse cell types, a key difference is that most stimuli to neurons, e.g. synaptic inputs, are transient, on the order of milliseconds to seconds, whereas many stimuli acting on non-neural tissue, e.g. growth factors, are longer duration. The ability to consolidate these inputs to regulate the activation of ERK in response to diverse signals raises the question of which factors influence the difference in ERK activation pathways. This review presents both experimental studies and computational models aimed at understanding the control of ERK activation and whether there are fundamental differences between neurons and other cells. Our main conclusion is that differences between cell types are quite subtle, often related to differences in expression pattern and quantity of some molecules such as Raf isoforms. In addition, the spatial location of ERK is critical, with regulation by scaffolding proteins producing differences due to colocalization of upstream molecules that may differ between neurons and other cells.

Application of information theory in systems biology

Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work.

Model-based analysis of response and resistance factors of cetuximab treatment in gastric cancer cell lines

Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a basis for the assessment of gastric cancer signaling and possibly for the development and discovery of predictive biomarkers.

Unraveling the causal differences between drug responders and non-responders is an important challenge. The information can help to understand molecular mechanisms and to guide the selection and design of targeted therapies. Here, we approach this problem for cetuximab treatment for gastric cancer using mechanistic mathematical modeling. The proposed model describes responder and non-responder gastric cancer cell lines and can predict the response in several validation experiments. Our analysis provides a differentiated view on mutations and explains, for instance, the relevance of MET mutations and the insignificance of PIK3CA mutation in the considered cell lines. The model might potentially provide the basis for understanding the recent failure of several clinical studies.

Neuron-derived exosomes with high miR-21-5p expression promoted polarization of M1 microglia in culture

BACKGROUND

Neuroinflammation is a characteristic pathological change of acute neurological deficit and chronic traumatic encephalopathy (CTE) after traumatic brain injury (TBI). Microglia are the key cell involved in neuroinflammation and neuronal injury. The type of microglia polarization determines the direction of neuroinflammation. MiR-21-5p elevated in neurons and microglia after TBI in our previous research. In this study, we explore the influence of miR-21-5p for neuroinflammation by regulating microglia polarization.

METHODS

In this study, PC12 and BV2 used to instead of neuron and microglia respectively. The co-cultured transwell system used to simulate interaction of PC12 and BV2 cells in vivo environment.

RESULTS

We found that PC12-derived exosomes with containing miR-21-5p were phagocytosed by microglia and induced microglia polarization, meanwhile, the expression of miR-21-5p was increased in M1 microglia cells. Polarization of M1 microglia aggravated the release of neuroinflammation factors, inhibited the neurite outgrowth, increased accumulation of P-tau and promoted the apoptosis of PC12 cells, which formed a model of cyclic cumulative damage. Simultaneously, we also got similar results in vivo experiments.

CONCLUSIONS

PC12-derived exosomes with containing miR-21-5p is the essential of this cyclic cumulative damage model. Therefore, regulating the expression of miR-21-5p or the secretion of exosomes may be an important novel strategy for the treatment of neuroinflammation after TBI.

Markers of pancreatic cancer stem cells and their clinical and therapeutic implications

Pancreatic cancer (PC) is the fourth most common cause of death among all cancers. Poor prognosis of PC may be caused by a prevalence of cancer stem cells (CSCs). CSCs are a population of cancer cells showing stem cell-like characteristics. CSCs have the ability to self-renew and may initiate tumorigenesis. PC CSCs express markers such as CD133, CD24, CD44, DCLK1, CXCR4, ESA, Oct4 and ABCB1. There is a wide complexity of interaction and relationships between CSC markers in PC. These markers are negative prognostic factors and are connected with tumor recurrence and clinical progression. Additionally, PC CSCs are resistant to treatment with gemcitabine. Thus, most current therapies for PC are ineffective. Numerous studies have shown, that targeting of these proteins may increase both disease-free and overall survival in PC.

Mathematical model of hypoxia and tumor signaling interplay reveals the importance of hypoxia and cell-to-cell variability in tumor growth inhibition

Background

Human tumor is a complex tissue with multiple heterogeneous hypoxic regions and significant cell-to-cell variability. Due to the complexity of the disease, the explanation of why anticancer therapies fail cannot be attributed to intrinsic or acquired drug resistance alone. Furthermore, there are inconsistent reports of hypoxia-induced kinase activities in different cancer cell-lines, where increase, decreases, or no change has been observed. Thus, we asked, why are there widely contrasting results in kinase activity under hypoxia in different cancer cell-lines and how does hypoxia play a role in anti-cancer drug sensitivity?

Results

We took a modeling approach to address these questions by analyzing the model simulation to explain why hypoxia driven signals can have dissimilar impact on tumor growth and alter the efficacy of anti-cancer drugs. Repeated simulations with varying concentrations of biomolecules followed by decision tree analysis reveal that the highly differential effects among heterogeneous subpopulation of tumor cells could be governed by varying concentrations of just a few key biomolecules. These biomolecules include activated serine/threonine-specific protein kinases (pRAF), mitogen-activated protein kinase kinase (pMEK), protein kinase B (pAkt), or phosphoinositide-4,5-bisphosphate 3-kinase (pPI3K). Additionally, the ratio of activated extracellular signal-regulated kinases (pERK) or pAkt to its respective total was a key factor in determining the sensitivity of pERK or pAkt to hypoxia.

Conclusion

This work offers a mechanistic insight into how hypoxia can affect the efficacy of anti-cancer drug that targets tumor signaling and provides a framework to identify the types of tumor cells that are either sensitive or resistant to anti-cancer therapy.

ModelBricks-modules for reproducible modeling improving model annotation and provenance

Most computational models in biology are built and intended for "single-use"; the lack of appropriate annotation creates models where the assumptions are unknown, and model elements are not uniquely identified. Simply recreating a simulation result from a publication can be daunting; expanding models to new and more complex situations is a herculean task. As a result, new models are almost always created anew, repeating literature searches for kinetic parameters, initial conditions and modeling specifics. It is akin to building a brick house starting with a pile of clay. Here we discuss a concept for building annotated, reusable models, by starting with small well-annotated modules we call ModelBricks. Curated ModelBricks, accessible through an open database, could be used to construct new models that will inherit ModelBricks annotations and thus be easier to understand and reuse. Key features of ModelBricks include reliance on a commonly used standard language (SBML), rule-based specification describing species as a collection of uniquely identifiable molecules, association with model specific numerical parameters, and more common annotations. Physical bricks can vary substantively; likewise, to be useful the structure of ModelBricks must be highly flexible-it should encapsulate mechanisms from single reactions to multiple reactions in a complex process. Ultimately, a modeler would be able to construct large models by using multiple ModelBricks, preserving annotations and provenance of model elements, resulting in a highly annotated model. We envision the library of ModelBricks to rapidly grow from community contributions. Persistent citable references will incentivize model creators to contribute new ModelBricks.

On Information Extraction and Decoding Mechanisms Improved by Noisy Amplification in Signaling Pathways

The cells need to process information about extracellular stimuli. They encode, transmit and decode the information to elicit an appropriate response. Studies aimed at understanding how such information is decoded in the signaling pathways to generate a specific cellular response have become essential. Eukaryotic cells decode information through two different mechanisms: the feed-forward loop and the promoter affinity. Here, we investigate how these two mechanisms improve information transmission. A detailed comparison is made between the stochastic model of the MAPK/ERK pathway and a stochastic minimal decoding model. The maximal amount of transmittable information was computed. The results suggest that the decoding mechanism of the MAPK/ERK pathway improve the channel capacity because it behaves as a noisy amplifier. We show a positive dependence between the noisy amplification and the amount of information extracted. Additionally, we show that the extrinsic noise can be tuned to improve information transmission. This investigation has revealed that the feed-forward loop and the promoter affinity motifs extract information thanks to processes of amplification and noise addition. Moreover, the channel capacity is enhanced when both decoding mechanisms are coupled. Altogether, these findings suggest novel characteristics in how decoding mechanisms improve information transmission.

Quantification of the Dynamic Phosphorylation Process of ERK Using Stable Isotope Dilution Selective Reaction Monitoring Mass Spectrometry

Mitogen-activated protein (MAP) kinase signaling is critical for various cellular responses, including cell proliferation, differentiation, and cell death. The MAP kinase cascade is conserved in the eukaryotic kingdom as a three-tiered kinase module-MAP kinase kinase kinase, MAP kinase kinase, and MAP kinase-that transduces signals via sequential phosphorylation upon stimulation. Dual phosphorylation of MAP kinase on the conserved threonine-glutamic acid-tyrosine (TEY) motif is essential for its catalytic activity and signal activation; however, the molecular mechanism by which the two residues are phosphorylated remains elusive. In the present study, the pattern of dual phosphorylation of extracellular signal-regulated kinase (ERK) is profiled on the TEY motif using stable isotope dilution (SID)-selective reaction monitoring (SRM) mass spectrometry (MS) to elucidate the order and magnitude of endogenous ERK phosphorylation in cellular model systems. The SID-SRM-MS analysis of phosphopeptides demonstrates that tyrosine phosphorylation in the TEY motif is dynamic, while threonine phosphorylation is static. Analyses of the mono-phosphorylatable mutants ERK^T202A and ERK^Y204F indicate that phosphorylation of tyrosine is not affected by the phosphorylation state of threonine, while threonine phosphorylation depends on tyrosine phosphorylation. The data suggest that dual phosphorylation of ERK is a highly ordered and restricted mechanism determined by tyrosine phosphorylation.

Communication codes in developmental signaling pathways

A handful of core intercellular signaling pathways play pivotal roles in a broad variety of developmental processes. It has remained puzzling how so few pathways can provide the precision and specificity of cell-cell communication required for multicellular development. Solving this requires us to quantitatively understand how developmentally relevant signaling information is actively sensed, transformed and spatially distributed by signaling pathways. Recently, single cell analysis and cell-based reconstitution, among other approaches, have begun to reveal the 'communication codes' through which information is represented in the identities, concentrations, combinations and dynamics of extracellular ligands. They have also revealed how signaling pathways decipher these features and control the spatial distribution of signaling in multicellular contexts. Here, we review recent work reporting the discovery and analysis of communication codes and discuss their implications for diverse developmental processes.

In silico modeling of phosphorylation dependent and independent c-Myc degradation

Background

c-Myc plays an important role in cell proliferation, cell growth and in differentiation, making it a key regulator for carcinogenesis and pluripotency. Tight control of c-myc turnover is required by ubiquitin-mediated degradation. This is achieved in the system by two F-box proteins Skp2 and FBXW7.

Results

Dynamic modelling technique was used to build two exclusive models for phosphorylation dependent degradation of Myc by FBXW7 (Model 1) and phosphorylation independent degradation by Skp2 (Model 2). Sensitivity analysis performed on these two models revealed that these models were corroborating experimental studies. It was also seen that Model 1 was more robust and perhaps more efficient in degrading c-Myc. These results questioned the existence of the two models in the system and to answer the question a combined model was hypothesised which had a decision making switch. The combined model had both Skp2 and FBXW7 mediated degradation where again the latter played a more important role. This model was able to achieve the lowest levels of ubiquitylated Myc and therefore functioned most efficiently in degradation of Myc.

Conclusion

In this report, c-Myc degradation by two F-box proteins was mathematically evaluated based on the importance of c-Myc turnover. The study was performed in a homeostatic system and therefore, prompts the exploration of c-Myc degradation in cancer state and in pluripotent state.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2846-x) contains supplementary material, which is available to authorized users.

Signal-induced PARP1-Erk synergism mediates IEG expression

A recently disclosed Erk-induced PARP1 activation mechanism mediates the expression of immediate early genes (IEGs) in response to a variety of extra- and intracellular signals implicated in memory acquisition, development and proliferation. Here, we review this mechanism, which is initiated by stimulation-induced binding of PARP1 to phosphorylated Erk translocated into the nucleus. This binding maintains long-lasting synergistic activity of these proteins, which offers a new pattern for targeted therapy.

Synergy with TGFβ ligands switches WNT pathway dynamics from transient to sustained during human pluripotent cell differentiation

WNT/β-catenin signaling is crucial to all stages of life. It controls early morphogenetic events in embryos, maintains stem cell niches in adults, and is dysregulated in many types of cancer. Despite its ubiquity, little is known about the dynamics of signal transduction or whether it varies across contexts. Here we probe the dynamics of signaling by monitoring nuclear accumulation of β-catenin, the primary transducer of canonical WNT signals, using quantitative live cell imaging. We show that β-catenin signaling responds adaptively to constant WNT signaling in pluripotent stem cells, and that these dynamics become sustained on differentiation. Varying dynamics were also observed in the response to WNT in commonly used mammalian cell lines. Signal attenuation in pluripotent cells is observed even at saturating doses, where ligand stability does not affect the dynamics. TGFβ superfamily ligands Activin and BMP, which coordinate with WNT signaling to pattern the gastrula, increase the β-catenin response in a manner independent of their ability to induce new WNT ligand production. Our results reveal how variables external to the pathway, including differentiation status and cross-talk with other pathways, dramatically alter WNT/β-catenin dynamics.

Single-cell quantification of the concentrations and dissociation constants of endogenous proteins

Kinetic simulation is a useful approach for elucidating complex cell-signaling systems. The numerical simulations required for kinetic modeling in live cells critically require parameters such as protein concentrations and dissociation constants (K_d ). However, only a limited number of parameters have been measured experimentally in living cells. Here we describe an approach for quantifying the concentration and K_d of endogenous proteins at the single-cell level with CRISPR/Cas9-mediated knock-in and fluorescence cross-correlation spectroscopy. First, the mEGFP gene was knocked in at the end of the mitogen-activated protein kinase 1 (MAPK1) gene, encoding extracellular signal-regulated kinase 2 (ERK2), through homology-directed repair or microhomology-mediated end joining. Next, the HaloTag gene was knocked in at the end of the ribosomal S6 kinase 2 (RSK2) gene. We then used fluorescence correlation spectroscopy to measure the protein concentrations of endogenous ERK2-mEGFP and RSK2-HaloTag fusion constructs in living cells, revealing substantial heterogeneities. Moreover, fluorescence cross-correlation spectroscopy analyses revealed temporal changes in the apparent K_d values of the binding between ERK2-mEGFP and RSK2-HaloTag in response to epidermal growth factor stimulation. Our approach presented here provides a robust and efficient method for quantifying endogenous protein concentrations and dissociation constants in living cells.

Constructing network topologies for multiple signal-encoding functions

Background

Cells use signaling protein networks to sense their environment and mediate specific responses. Information about environmental stress is usually encoded in the dynamics of the signaling molecules, and qualitatively distinct dynamics of the same signaling molecule can lead to dramatically different cell fates. Exploring the design principles of networks with multiple signal-encoding functions is important for understanding how distinct dynamic patterns are shaped and integrated by real cellular networks, and for building cells with targeted sensing–response functions via synthetic biology.

Results

In this paper, we investigate multi-node enzymatic regulatory networks with three signal-encoding functions, i.e., dynamic responses of oscillation, transient activation, and sustained activation upon step stimulation by three different inducers, respectively. Taking into account competition effects of the substrates for the same enzyme in the enzymatic reactions, we searched for robust subnetworks for each signal-encoding function by three-node-network enumeration and then integrated the three subnetworks together via node-merging. The obtained tri-functional networks consisted of four to six nodes, and the core structures of these networks were hybrids of the motifs for the subfunctions.

Conclusions

The simplest but relatively robust tri-functional networks demonstrated that the three functions were compatible within a simple negative feedback loop. Depending on the network structure, the competition effects of the substrates for the same enzyme within the networks could promote or hamper the target functions, and can create implicit functional motifs. Overall, the networks we obtained could in principle be synthesized to construct dynamic control circuits with multiple target functions.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0676-5) contains supplementary material, which is available to authorized users.

Detection of malignancy-associated phosphoproteome changes in human colorectal cancer induced by cell surface binding of growth-inhibitory galectin-4

Emerging evidence on efficient tumor growth regulation by endogenous lectins directs interest to determine on a proof-of-principle level the range of information on alterations provided by full-scale analysis using phosphoproteomics. In our pilot study, we tested galectin-4 (gal-4) that is a growth inhibitor for colon cancer cells (CRC), here working with the LS 180 line. In order to cover monitoring of short- and long-term effects stable isotope labeling by amino acids in cell culture-based quantitative phosphoproteomic analyses were conducted on LS 180 cell preparations collected 1 and 72 h after adding gal-4 to the culture medium. After short-term treatment, 981 phosphosites, all of them S/T based, were detected by phosphoproteomics. Changes higher than 1.5-fold were seen for eight sites in seven proteins. Most affected were the BET1 homolog (BET1), whose level of phosphorylation at S50 was about threefold reduced, and centromere protein F (CENPF), extent of phosphorylation at S3119 doubling in gal-4-treated cells. Phosphoproteome analysis after 72 h of treatment revealed marked changes at 33 S/T-based phosphosites from 29 proteins. Prominent increase of phosphorylation was observed for cofilin-1 at position S3. Extent of phosphorylation of the glutamine transporter SLC1A5 at position S503 was decreased by a factor of 3. Altered phosphorylation of BET1, CENPF, and cofilin-1 as well as a significant effect of gal-4 treatment on glutamine uptake by cells were substantiated by independent methods in the Vaco 432, Colo 205, CX 1, and HCT 116 cell lines. With the example of gal-4 which functions as a tumor suppressor in CRC cells, we were able to prove that cell surface binding of the lectin not only markedly influences the cell proteome, but also has a bearing on malignancy-associated intracellular protein phosphorylation. These results underscore the potential of this approach to give further work on elucidating the details of signaling underlying galectin-triggered growth inhibition a clear direction. © 2018 IUBMB Life, 71(3):364-375, 2019.

Optical Activation of TrkA Signaling

Nerve growth factor/tropomyosin receptor kinase A (NGF/TrkA) signaling plays a key role in neuronal development, function, survival, and growth. The pathway is implicated in neurodegenerative disorders including Alzheimer's disease, chronic pain, inflammation, and cancer. NGF binds the extracellular domain of TrkA, leading to the activation of the receptor's intracellular kinase domain. As TrkA signaling is highly dynamic, mechanistic studies would benefit from a tool with high spatial and temporal resolution. Here we present the design and evaluation of four strategies for light-inducible activation of TrkA in the absence of NGF. Our strategies involve the light-sensitive protein Arabidopsis cryptochrome 2 and its binding partner CIB1. We demonstrate successful recapitulation of native NGF/TrkA functions by optical induction of plasma membrane recruitment and homo-interaction of the intracellular domain of TrkA. This approach activates PI3K/AKT and Raf/ERK signaling pathways, promotes neurite growth in PC12 cells, and supports survival of dorsal root ganglion neurons in the absence of NGF. This ability to activate TrkA using light bestows high spatial and temporal resolution for investigating NGF/TrkA signaling.

Tamoxifen-Induced Apoptosis of MCF-7 Cells via GPR30/PI3K/MAPKs Interactions: Verification by ODE Modeling and RNA Sequencing

Tamoxifen (Nolvadex) is one of the most widely used and effective therapeutic agent for breast cancer. It benefits nearly 75% of patients with estrogen receptor (ER)-positive breast cancer that receive this drug. Its effectiveness is mainly attributed to its capacity to function as an ER antagonist, blocking estrogen binding sites on the receptor, and inhibiting the proliferative action of the receptor-hormone complex. Although, tamoxifen can induce apoptosis in breast cancer cells via upregulation of pro-apoptotic factors, it can also promote uterine hyperplasia in some women. Thus, tamoxifen as a multi-functional drug could have different effects on cells based on the utilization of effective concentrations or availability of specific co-factors. Evidence that tamoxifen functions as a GPR30 (G-Protein Coupled Receptor 30) agonist activating adenylyl cyclase and EGFR (Epidermal Growth Factor Receptor) intracellular signaling networks, provides yet another means of explaining the multi-functionality of tamoxifen. Here ordinary differential equation (ODE) modeling, RNA sequencing and real time qPCR analysis were utilized to establish the necessary data for gene network mapping of tamoxifen-stimulated MCF-7 cells, which express the endogenous ER and GPR30. The gene set enrichment analysis and pathway analysis approaches were used to categorize transcriptionally upregulated genes in biological processes. Of the 2,713 genes that were significantly upregulated following a 48 h incubation with 250 μM tamoxifen, most were categorized as either growth-related or pro-apoptotic intermediates that fit into the Tp53 and/or MAPK signaling pathways. Collectively, our results display that the effects of tamoxifen on the breast cancer MCF-7 cell line are mediated by the activation of important signaling pathways including Tp53 and MAPKs to induce apoptosis.

In Vivo Decoding Mechanisms of the Temporal Patterns of Blood Insulin by the Insulin-AKT Pathway in the Liver

Cells respond to various extracellular stimuli through a limited number of signaling pathways. One strategy to process such stimuli is to code the information into the temporal patterns of molecules. Although we showed that insulin selectively regulated molecules depending on its temporal patterns using Fao cells, the in vivo mechanism remains unknown. Here, we show how the insulin-AKT pathway processes the information encoded into the temporal patterns of blood insulin. We performed hyperinsulinemic-euglycemic clamp experiments and found that, in the liver, all temporal patterns of insulin are encoded into the insulin receptor, and downstream molecules selectively decode them through AKT. S6K selectively decodes the additional secretion information. G6Pase interprets the basal secretion information through FoxO1, while GSK3β decodes all secretion pattern information. Mathematical modeling revealed the mechanism via differences in network structures and from sensitivity and time constants. Given that almost all hormones exhibit distinct temporal patterns, temporal coding may be a general principle of system homeostasis by hormones.

PARP1-Erk synergism in proliferating cells

A synergism between PARP1 and phosphorylated Erk mediating IEG (immediate early gene) expression has been recently reported in cerebral neurons and cardiomyocytes. Stimulation induced PARP-Erk synergism was required for IEG expression underlying synaptic plasticity and long-term memory acquisition during learning. It was similarly required for cardiomyocytes development. Here, we identified this mechanism in Erk-induced gene expression promoting proliferation. This mechanism can be targeted in malignant cells.

Soluble Klotho causes hypomineralization in Klotho-deficient mice

The type I transmembrane protein αKlotho (Klotho) serves as a coreceptor for the phosphaturic hormone fibroblast growth factor 23 (FGF23) in kidney, while a truncated form of Klotho (soluble Klotho, sKL) is thought to exhibit multiple activities, including acting as a hormone, but whose mode(s) of action in different organ systems remains to be fully elucidated. FGF23 is expressed primarily in osteoblasts/osteocytes and aberrantly high levels in the circulation acting via signaling through an FGF receptor (FGFR)-Klotho coreceptor complex cause renal phosphate wasting and osteomalacia. We assessed the effects of exogenously added sKL on osteoblasts and bone using Klotho-deficient (kl/kl) mice and cell and organ cultures. sKL induced FGF23 signaling in bone and exacerbated the hypomineralization without exacerbating the hyperphosphatemia, hypercalcemia and hypervitaminosis D in kl/kl mice. The same effects were seen in rodent bone models in vitro, in which we also detected formation of a sKL complex with FGF23-FGFR and decreased Phex (gene responsible for X-linked hypophosphatemic rickets (XLH)/osteomalacia) expression. Further, sKL-FGF23-dependent hypomineralization in vitro was rescued by soluble PHEX. These data suggest that exogenously added sKL directly participates in FGF23 signaling in bone and that PHEX is a downstream effector of the sKL-FGF23-FGFR axis in bone.

Pathway crosstalk enables cells to interpret TGF-β duration

The detection and transmission of the temporal quality of intracellular and extracellular signals is an essential cellular mechanism. It remains largely unexplored how cells interpret the duration information of a stimulus. In this paper, we performed an integrated quantitative and computational analysis on TGF-β induced activation of SNAIL1, a key transcription factor that regulates several subsequent cell fate decisions such as apoptosis and epithelial-to-mesenchymal transition. We demonstrate that crosstalk among multiple TGF-β activated pathways forms a relay from SMAD to GLI1 that initializes and maintains SNAILl expression, respectively. SNAIL1 functions as a key integrator of information from TGF-β signaling distributed through upstream divergent pathways. The intertwined network serves as a temporal checkpoint, so that cells can generate a transient or sustained expression of SNAIL1 depending on TGF-β duration. Furthermore, we observed that TGF-β treatment leads to an unexpected accumulation of GSK3 molecules in an enzymatically active tyrosine phosphorylation form in Golgi apparatus and ER, followed by accumulation of GSK3 molecules in an enzymatically inhibitive serine phosphorylation in the nucleus. Subsequent model analysis and inhibition experiments revealed that the initial localized increase of GSK3 enzymatic activity couples to the positive feedback loop of the substrate Gli1 to form a network motif with multi-objective functions. That is, the motif is robust against stochastic fluctuations, and has a narrow distribution of response time that is insensitive to initial conditions. Specifically for TGF-β signaling, the motif ensures a smooth relay from SMAD to GLI1 on regulating SNAIL1 expression.

The topological requirements for robust perfect adaptation in networks of any size

Robustness, and the ability to function and thrive amid changing and unfavorable environments, is a fundamental requirement for living systems. Until now it has been an open question how large and complex biological networks can exhibit robust behaviors, such as perfect adaptation to a variable stimulus, since complexity is generally associated with fragility. Here we report that all networks that exhibit robust perfect adaptation (RPA) to a persistent change in stimulus are decomposable into well-defined modules, of which there exist two distinct classes. These two modular classes represent a topological basis for all RPA-capable networks, and generate the full set of topological realizations of the internal model principle for RPA in complex, self-organizing, evolvable bionetworks. This unexpected result supports the notion that evolutionary processes are empowered by simple and scalable modular design principles that promote robust performance no matter how large or complex the underlying networks become.

Robust perfect adaptation (RPA), the ability of a system to return to its pre-stimulus state in the presence of a new signal, enables organisms to respond to further changes in stimuli. Here, the authors identify the modular structure of the full set of network topologies that can confer RPA on complex networks.

Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data

Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlinear biochemical network model. The framework is based on the expectation-maximization algorithm combined with particle smoother and sparse regularization techniques. In this method, a “redundant” model consisting of an excessive number of nodes and regulatory paths is iteratively updated by eliminating unnecessary paths, resulting in an inference of the most likely model. Using artificial single-cell time-course data showing heterogeneous oscillatory behaviors, we demonstrated that this algorithm successfully inferred the true network without any prior knowledge of network topology or parameter values. Furthermore, we showed that both the regulatory paths among nodes and the optimal number of nodes in the network could be systematically determined. The method presented in this study provides a general framework for inferring a nonlinear biochemical network model from heterogeneous single-cell time-course data.

Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.