Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics

Cell State Transition Models Stratify Breast Cancer Cell Phenotypes and Reveal New Therapeutic Targets

Understanding signaling patterns of transformation and controlling cell phenotypes is a challenge of current biology. Here we applied a cell State Transition Assessment and Regulation (cSTAR) approach to a perturbation dataset of single cell phosphoproteomic patterns of multiple breast cancer (BC) and normal breast tissue-derived cell lines. Following a separation of luminal, basal, and normal cell states, we identified signaling nodes within core control networks, delineated causal connections, and determined the primary drivers underlying oncogenic transformation and transitions across distinct BC subtypes. Whereas cell lines within the same BC subtype have different mutational and expression profiles, the architecture of the core network was similar for all luminal BC cells, and mTOR was a main oncogenic driver. In contrast, core networks of basal BC were heterogeneous and segregated into roughly four major subclasses with distinct oncogenic and BC subtype drivers. Likewise, normal breast tissue cells were separated into two different subclasses. Based on the data and quantified network topologies, we derived mechanistic cSTAR models that serve as digital cell twins and allow the deliberate control of cell movements within a Waddington landscape across different cell states. These cSTAR models suggested strategies of normalizing phosphorylation networks of BC cell lines using small molecule inhibitors.

Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Deciphering the History of ERK Activity from Fixed-Cell Immunofluorescence Measurements

The Ras/ERK pathway drives cell proliferation and other oncogenic behaviors, and quantifying its activity in situ is of high interest in cancer diagnosis and therapy. Pathway activation is often assayed by measuring phosphorylated ERK. However, this form of measurement overlooks dynamic aspects of signaling that can only be observed over time. In this study, we combine a live, single-cell ERK biosensor approach with multiplexed immunofluorescence staining of downstream target proteins to ask how well immunostaining captures the dynamic history of ERK activity. Combining linear regression, machine learning, and differential equation models, we develop an interpretive framework for immunostains, in which Fra-1 and pRb levels imply long term activation of ERK signaling, while Egr-1 and c-Myc indicate recent activation. We show that this framework can distinguish different classes of ERK dynamics within a heterogeneous population, providing a tool for annotating ERK dynamics within fixed tissues.

Patterns of change in regulatory modules of chemical reaction systems induced by network modification

Cellular functions are realized through the dynamics of chemical reaction networks formed by thousands of chemical reactions. Numerical studies have empirically demonstrated that small differences in network structures among species or tissues can cause substantial changes in dynamics. However, a general principle for behavior changes in response to network structure modifications is not known. The chemical reaction system possesses substructures called buffering structures, which are characterized by a certain topological index being zero. It was proven that the steady-state response to modulation of reaction parameters inside a buffering structure is localized in the buffering structure. In this study, we developed a method to systematically identify the loss or creation of buffering structures induced by the addition of a single degradation reaction from network structure alone. This makes it possible to predict the qualitative and macroscopic changes in regulation that will be caused by the network modification. This method was applied to two reaction systems: the central metabolic system and the mitogen-activated protein kinases signal transduction system. Our method enables identification of reactions that are important for biological functions in living systems.

From Cell States to Cell Fates: Control of Cell State Transitions

We examine the coordinated behavior of thousands of genes in cell fate transitions through genome expression as an integrated dynamical system using the concepts of self-organized criticality and coherent stochastic behavior. To quantify the effects of the collective behavior of genes, we adopted the flux balance approach and developed it in a new tool termed expression flux analysis (EFA). Here we describe this tool and demonstrate how its application to specific experimental genome-wide expression data provides new insights into the dynamics of the cell-fate transitions. Particularly, we show that in cell fate change, specific stochastic perturbations can spread over the entire system to guide distinct cell fate transitions through switching cyclic flux flow in the genome engine. Utilization of EFA enables us to elucidate a unified genomic mechanism for when and how cell-fate change occurs through critical transitions.

A guide to ERK dynamics, part 2: downstream decoding

Signaling by the extracellular signal-regulated kinase (ERK) pathway controls many cellular processes, including cell division, death, and differentiation. In this second installment of a two-part review, we address the question of how the ERK pathway exerts distinct and context-specific effects on multiple processes. We discuss how the dynamics of ERK activity induce selective changes in gene expression programs, with insights from both experiments and computational models. With a focus on single-cell biosensor-based studies, we summarize four major functional modes for ERK signaling in tissues: adjusting the size of cell populations, gradient-based patterning, wave propagation of morphological changes, and diversification of cellular gene expression states. These modes of operation are disrupted in cancer and other related diseases and represent potential targets for therapeutic intervention. By understanding the dynamic mechanisms involved in ERK signaling, there is potential for pharmacological strategies that not only simply inhibit ERK, but also restore functional activity patterns and improve disease outcomes.

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.

Reversing pathological cell states: the road less travelled can extend the therapeutic horizon

Acquisition of omics data advances at a formidable pace. Yet, our ability to utilize these data to control cell phenotypes and design interventions that reverse pathological states lags behind. Here, we posit that cell states are determined by core networks that control cell-wide networks. To steer cell fate decisions, core networks connecting genotype to phenotype must be reconstructed and understood. A recent method, cell state transition assessment and regulation (cSTAR), applies perturbation biology to quantify causal connections and mechanistically models how core networks influence cell phenotypes. cSTAR models are akin to digital cell twins enabling us to purposefully convert pathological states back to physiologically normal states. While this capability has a range of applications, here we discuss reverting oncogenic transformation.

PI3K signaling through a biochemical systems lens

Following 3 decades of extensive research into PI3K signaling, it is now evidently clear that the underlying network does not equate to a simple ON/OFF switch. This is best illustrated by the multifaceted nature of the many diseases associated with aberrant PI3K signaling, including common cancers, metabolic disease, and rare developmental disorders. However, we are still far from a complete understanding of the fundamental control principles that govern the numerous phenotypic outputs that are elicited by activation of this well-characterized biochemical signaling network, downstream of an equally diverse set of extrinsic inputs. At its core, this is a question on the role of PI3K signaling in cellular information processing and decision making. Here, we review the determinants of accurate encoding and decoding of growth factor signals and discuss outstanding questions in the PI3K signal relay network. We emphasize the importance of quantitative biochemistry, in close integration with advances in single-cell time-resolved signaling measurements and mathematical modeling.

Synchronization between Attractors: Genomic Mechanism of Cell-Fate Change

Herein, we provide a brief overview of complex systems theory approaches to investigate the genomic mechanism of cell-fate changes. Cell trajectories across the epigenetic landscape, whether in development, environmental responses, or disease progression, are controlled by extensively coordinated genome-wide gene expression changes. The elucidation of the mechanisms underlying these coherent expression changes is of fundamental importance in cell biology and for paving the road to new therapeutic approaches. In previous studies, we pointed at dynamic criticality as a plausible characteristic of genome-wide transition dynamics guiding cell fate. Whole-genome expression develops an engine-like organization (genome engine) in order to establish an autonomous dynamical system, capable of both homeostasis and transition behaviors. A critical set of genes behaves as a critical point (CP) that serves as the organizing center of cell-fate change. When the system is pushed away from homeostasis, the state change that occurs at the CP makes local perturbation spread over the genome, demonstrating self-organized critical (SOC) control of genome expression. Oscillating-Mode genes (which normally keep genome expression on pace with microenvironment fluctuations), when in the presence of an effective perturbative stimulus, drive the dynamics of synchronization, and thus guide the cell-fate transition.

Targeted Inhibition of Select Extracellular Signal-regulated Kinases 1 and 2 Functions Mitigates Pathological Features of Asthma in Mice

ERK1/2 (extracellular signal-regulated kinases 1 and 2) regulate the activity of various transcription factors that contribute to asthma pathogenesis. Although an attractive drug target, broadly inhibiting ERK1/2 is challenging because of unwanted cellular toxicities. We have identified small molecule inhibitors with a benzenesulfonate scaffold that selectively inhibit ERK1/2-mediated activation of AP-1 (activator protein-1). Herein, we describe the findings of targeting ERK1/2-mediated substrate-specific signaling with the small molecule inhibitor SF-3-030 in a murine model of house dust mite (HDM)-induced asthma. In 8- to 10-week-old BALB/c mice, allergic asthma was established by repeated intranasal HDM (25 μg/mouse) instillation for 3 weeks (5 days/week). A subgroup of mice was prophylactically dosed with 10 mg/kg SF-3-030/DMSO intranasally 30 minutes before the HDM challenge. Following the dosing schedule, mice were evaluated for alterations in airway mechanics, inflammation, and markers of airway remodeling. SF-3-030 treatment significantly attenuated HDM-induced elevation of distinct inflammatory cell types and cytokine concentrations in BAL and IgE concentrations in the lungs. Histopathological analysis of lung tissue sections revealed diminished HDM-induced pleocellular peribronchial inflammation, mucus cell metaplasia, collagen accumulation, thickening of airway smooth muscle mass, and expression of markers of cell proliferation (Ki-67 and cyclin D1) in mice treated with SF-3-030. Furthermore, SF-3-030 treatment attenuated HDM-induced airway hyperresponsiveness in mice. Finally, mechanistic studies using transcriptome and proteome analyses suggest inhibition of HDM-induced genes involved in inflammation, cell proliferation, and tissue remodeling by SF-3-030. These preclinical findings demonstrate that function-selective inhibition of ERK1/2 signaling mitigates multiple features of asthma in a murine model.

Systems biology of protein network

The development of comprehensive measurement technology for biomolecules and the development of computer hardware and algorithms, enhanced by human genome research, have greatly contributed to the advancement of predictive biology. However, to make that happen, it was essential to have a common concept of sharing data in a standard format. In this article, I would like to briefly review how such concepts were developed in Japan for the foundation of today's biological sciences.

Mathematical model of the cell signaling pathway based on the extended Boolean network model with a stochastic process

BACKGROUND

In cell signaling pathways, proteins interact with each other to determine cell fate in response to either cell-extrinsic (micro-environmental) or intrinsic cues. One of the well-studied pathways, the mitogen-activated protein kinase (MAPK) signaling pathway, regulates cell processes such as differentiation, proliferation, apoptosis, and survival in response to various micro-environmental stimuli in eukaryotes. Upon micro-environmental stimulus, receptors on the cell membrane become activated. Activated receptors initiate a cascade of protein activation in the MAPK pathway. This activation involves protein binding, creating scaffold proteins, which are known to facilitate effective MAPK signaling transduction.

RESULTS

This paper presents a novel mathematical model of a cell signaling pathway coordinated by protein scaffolding. The model is based on the extended Boolean network approach with stochastic processes. Protein production or decay in a cell was modeled considering the stochastic process, whereas the protein-protein interactions were modeled based on the extended Boolean network approach. Our model fills a gap in the binary set applied to previous models. The model simultaneously considers the stochastic process directly. Using the model, we simulated a simplified mitogen-activated protein kinase (MAPK) signaling pathway upon stimulation of both a single receptor at the initial time and multiple receptors at several time points. Our simulations showed that the signal is amplified as it travels down to the pathway from the receptor, generating substantially amplified downstream ERK activity. The noise generated by the stochastic process of protein self-activity in the model was also amplified as the signaling propagated through the pathway.

CONCLUSIONS

The signaling transduction in a simplified MAPK signaling pathway could be explained by a mathematical model based on the extended Boolean network model with a stochastic process. The model simulations demonstrated signaling amplifications when it travels downstream, which was already observed in experimental settings. We also highlight the importance of stochastic activity in regulating protein inactivation.

Network inference from perturbation time course data

Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.

Enteric VIP-producing neurons maintain gut microbiota homeostasis through regulating epithelium fucosylation

Interactions between the enteric nervous system (ENS) and intestinal epithelium are thought to play a vital role in intestinal homeostasis. How the ENS monitors the frontier with commensal and pathogenic microbes while maintaining epithelial function remains unclear. Here, by combining subdiaphragmatic vagotomy with transcriptomics, chemogenetic strategy, and coculture of enteric neuron-intestinal organoid, we show that enteric neurons expressing VIP shape the α1,2-fucosylation of intestinal epithelial cells (IECs). Mechanistically, neuropeptide VIP activates fut2 expression via the Erk1/2-c-Fos pathway through the VIPR1 receptor on IECs. We further demonstrate that perturbation of enteric neurons leads to gut dysbiosis through α1,2-fucosylation in the steady state and results in increased susceptibility to alcohol-associated liver disease (ALD). This was attributed to an imbalance between beneficial Bifidobacterium and opportunistic pathogenic Enterococcus faecalis in ALD. In addition, Bifidobacterium α1,2-fucosidase may promote Bifidobacterium adhesion to the mucosal surface, which restricts Enterococcus faecalis overgrowth and prevents ALD progression.

Protocol for stratification of triple-negative breast cancer patients using in silico signaling dynamics

Personalized kinetic models can predict potential biomarkers and drug targets. Here, we provide a step-by-step approach for building an executable mathematical model from text and integrating transcriptomic datasets. We additionally describe the steps to personalize the mechanistic model and to stratify patients with triple-negative breast cancer (TNBC) based on in silico signaling dynamics. This protocol can also be applied to any signaling pathway for patient-specific modeling.

For complete details on the use and execution of this protocol, please refer to Imoto et al. (2022).

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A computational framework for patient-specific modeling

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Integration of clinical data and cell line data for model calibration

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Building a mechanistic dynamic model from .txt file

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Stratification of patients with breast cancer based on in silico signaling dynamics

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Transcriptional Regulatory Role of NELL2 in Preproenkephalin Gene Expression

Preproenkephalin (PPE) is a precursor molecule for multiple endogenous opioid peptides Leu-enkephalin (ENK) and Met-ENK, which are involved in a wide variety of modulatory functions in the nervous system. Despite the functional importance of ENK in the brain, the effect of brain-derived factor(s) on PPE expression is unknown. We report the dual effect of neural epidermal growth factor (EGF)-likelike 2 (NELL2) on PPE gene expression. In cultured NIH3T3 cells, transfection of NELL2 expression vectors induced an inhibition of PPE transcription intracellularly, in parallel with downregulation of protein kinase C signaling pathways and extracellular signal-regulated kinase. Interestingly, these phenomena were reversed when synthetic NELL2 was administered extracellularly. The in vivo disruption of NELL2 synthesis resulted in an increase in PPE mRNA level in the rat brain, suggesting that the inhibitory action of intracellular NELL2 predominates the activation effect of extracellular NELL2 on PPE gene expression in the brain. Biochemical and molecular studies with mutant NELL2 structures further demonstrated the critical role of EGF-like repeat domains in NELL2 for regulation of PPE transcription. These are the first results to reveal the spatio-specific role of NELL2 in the homeostatic regulation of PPE gene expression.

A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

Modulation of Viral Replication, Apoptosis and Antiviral Response by Induction and Mutual Regulation of EGR and AP-1 Family Genes During Coronavirus Infection

Coronaviruses have evolved a variety of strategies to exploit normal cellular processes and signalling pathways for their efficient reproduction in a generally hostile cellular environment. One immediate-early response gene (IEG) family, the AP-1 gene family, was previously shown to be activated by coronavirus infection. In this study, we report that another IEG family, the EGR family, is also activated in cells infected with four different coronaviruses in three genera, i.e. gammacoronavirus infectious bronchitis virus (IBV), alphacoronaviruses porcine epidemic diarrhoea virus (PEDV) and human coronavirus-229E (HCoV-229E), and betacoronavirus HCoV-OC43. Knockdown of EGR1 reduced the expression of cJUN and cFOS, and knockdown of cJUN and/or cFOS reduced the expression of EGR1, demonstrating that these two IEG families may be cross-activated and mutual regulated. Furthermore, ERK1/2 was identified as an upstream kinase, and JNK and p38 as inhibitors of EGR1 activation in coronavirus-infected cells. However, upregulation of EGR family genes, in particular EGR1, appears to play a differential role in regulating viral replication, apoptosis and antiviral response. EGR1 was shown to play a limited role in regulation of coronavirus replication, and an anti-apoptotic role in cells infected with IBV or PEDV, but not in cells infected with HCoV-229E. Upregulation of EGR1 may also play a differential role in the regulation of antiviral response against different coronaviruses. This study reveals a novel regulatory network shared by different coronaviruses in the immediate-early response of host cells to infection.

Optogenetic actuator - ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Combining single-cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single-cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK-RAF and the ERK-RSK2-SOS negative feedback operate simultaneously to regulate ERK dynamics. Bypassing the RSK2-mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2-dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2-mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs, and its perturbation can enhance the efficiency of MAPK inhibitors.

EGFR ligands synergistically increase IL-17A-induced expression of psoriasis signature genes in human keratinocytes via IκBζ and Bcl3

Various epidermal growth factor receptor (EGFR) ligands are highly expressed in the epidermis of psoriasis lesions, and abnormal EGFR activation appears to be involved in the pathogenesis of psoriasis. However, how EGFR signaling contributes to the development of psoriasis is unclear. Interleukin (IL)-17A, a critical effector of the IL-23/IL-17A pathway, increases the expression of psoriasis signature genes in keratinocytes and plays an essential role in the pathogenesis of psoriasis by inducing IκBζ, a critical transcriptional regulator in psoriasis. In this study, we stimulated primary human keratinocytes with IL-17A and various EGFR ligands to investigate whether EGFR ligands regulate the expression of psoriasis signature genes. In cultured normal human keratinocytes and a living skin equivalent, EGFR ligands did not induce psoriasis-related gene expression, but significantly enhanced the IL-17A-mediated induction of various psoriasis signature genes, including antimicrobial peptides, cytokines, and chemokines. This was dependent on an EGFR activation-mediated synergistic increase in IL-17A-induced IκBζ expression and was partially mediated by the EGFR-dependent upregulation of Bcl3. Therefore, EGFR ligands can act as synergistic agents of IL-17A signaling by stimulating the epidermal production of psoriasis signature genes in psoriasis lesions. This study reveals a potential mechanism by which EGFR signaling contributes to the pathogenesis of psoriasis. This article is protected by copyright. All rights reserved.

A synthetic gene circuit for imaging-free detection of signaling pulses

Cells employ intracellular signaling pathways to sense and respond to changes in their external environment. In recent years, live-cell biosensors have revealed complex pulsatile dynamics in many pathways, but studies of these signaling dynamics are limited by the necessity of live-cell imaging at high spatiotemporal resolution. Here, we describe an approach to infer pulsatile signaling dynamics from a single measurement in fixed cells using a pulse-detecting gene circuit. We computationally screened for circuits with the capability to selectively detect signaling pulses, revealing an incoherent feedforward topology that robustly performs this computation. We implemented the motif experimentally for the Erk signaling pathway using a single engineered transcription factor and fluorescent protein reporter. Our "recorder of Erk activity dynamics" (READer) responds sensitively to spontaneous and stimulus-driven Erk pulses. READer circuits open the door to permanently labeling transient, dynamic cell populations to elucidate the mechanistic underpinnings and biological consequences of signaling dynamics.

Structure and function differences in the prelimbic cortex to basolateral amygdala circuit mediate trait vulnerability in a novel model of acute social defeat stress in male mice

Stressful life events are ubiquitous and well-known to negatively impact mental health. However, in both humans and animal models, there is large individual variability in how individuals respond to stress, with some but not all experiencing long-term adverse consequences. While there is growing understanding of the neurobiological underpinnings of the stress response, much less is known about how neurocircuits shaped by lifetime experiences are activated during an initial stressor and contribute to this selective vulnerability versus resilience. We developed a model of acute social defeat stress (ASDS) that allows classification of male mice into "susceptible" (socially avoidant) versus "resilient" (expressing control-level social approach) one hour after exposure to six minutes of social stress. Using circuit tracing and high-resolution confocal imaging, we explored differences in activation and dendritic spine density and morphology in the prelimbic cortex to basolateral amygdala (PL→BLA) circuit in resilient versus susceptible mice. Susceptible mice had greater PL→BLA recruitment during ASDS and activated PL→BLA neurons from susceptible mice had more and larger mushroom spines compared to resilient mice. We hypothesized identified structure/function differences indicate an overactive PL→BLA response in susceptible mice and used an intersectional chemogenetic approach to inhibit the PL→BLA circuit during or prior to ASDS. We found in both cases that this blocked ASDS-induced social avoidance. Overall, we show PL→BLA structure/function differences mediate divergent behavioral responses to ASDS in male mice. These results support PL→BLA circuit overactivity during stress as a biomarker of trait vulnerability and potential target for prevention of stress-induced psychopathology.

A Unified Genomic Mechanism of Cell-Fate Change

The purpose of our studies is to elucidate the nature of massive control of the whole genome expression with a particular emphasis on cell-fate change. The whole genome expression is coordinated by the emergence of a critical point (CP: a peculiar set of biphasic genes) with the genome acting as an integrated dynamical system. In response to stimuli, the genome expression self-organizes into local sub-, near-, and super-critical states, each exhibiting distinct collective behaviors with its center of mass acting as a local attractor, coexisting with the whole genome attractor (GA). The CP serves as the organizing center of cell-fate change, and its activation makes local perturbation to spread over the genome affecting GA. The activation of CP is in turn elicited by genes with elevated temporal variance (oscillating-mode genes), normally in charge to keep genome expression at pace with microenvironment fluctuations. When oscillation exceeds a given threshold, the CP synchronizes with the GA driving genome expression state transition. The expression synchronization wave invading the entire genome is fostered by the fusion-splitting dynamics of silencing pericentromere-associated heterochromatin domains and the consequent folding-unfolding transitions of transcribing euchromatin domains. The proposed mechanism is a unified step toward a time-evolutional transition theory of biological regulation.

Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI

Ordinary differential equation models are used to represent intracellular signaling pathways in silico, aiding and guiding biological experiments to elucidate intracellular regulation. To construct such quantitative and predictive models of intracellular interactions, unknown parameters need to be estimated. Most of biological data are expressed in relative or arbitrary units, raising the question of how to compare model simulations with data. It has recently been shown that for models with large number of unknown parameters, fitting algorithms using a data-driven normalization of the simulations (DNS) performs best in terms of the convergence time and parameter identifiability. DNS approach compares model simulations and corresponding data both normalized by the same normalization procedure, without requiring additional parameters to be estimated, as necessary for widely used scaling factor-based methods. However, currently there is no parameter estimation software that directly supports DNS. In this chapter, we show how to apply DNS to dynamic models of systems and synthetic biology using PEPSSBI (Parameter Estimation Pipeline for Systems and Synthetic Biology). PEPSSBI is the first software that supports DNS, through algorithmically supported data normalization and objective function construction. PEPSSBI also supports model import using SBML and repeated parameter estimation runs executed in parallel either on a personal computer or a multi-CPU cluster.

Oncogenic mutant RAS signaling activity is rescaled by the ERK/MAPK pathway

Activating mutations in RAS are present in ~ 30% of human tumors, and the resulting aberrations in ERK/MAPK signaling play a central role in oncogenesis. However, the form of these signaling changes is uncertain, with activating RAS mutants linked to both increased and decreased ERK activation in vivo. Rationally targeting the kinase activity of this pathway requires clarification of the quantitative effects of RAS mutations. Here, we use live‐cell imaging in cells expressing only one RAS isoform to quantify ERK activity with a new level of accuracy. We find that despite large differences in their biochemical activity, mutant KRAS isoforms within cells have similar ranges of ERK output. We identify roles for pathway‐level effects, including variation in feedback strength and feedforward modulation of phosphatase activity, that act to rescale pathway sensitivity, ultimately resisting changes in the dynamic range of ERK activity while preserving responsiveness to growth factor stimuli. Our results reconcile seemingly inconsistent reports within the literature and imply that the signaling changes induced by RAS mutations early in oncogenesis are subtle.

Liver-Specific Overexpression of Prostasin Attenuates High-Fat Diet-Induced Metabolic Dysregulation in Mice

The liver has a most indispensable role in glucose and lipid metabolism where we see some of the most serious worldwide health problems. The serine protease prostasin (PRSS8) cleaves toll-like receptor 4 (TLR4) and regulates hepatic insulin sensitivity under PRSS8 knockout condition. However, liver substrate proteins of PRSS8 other than TLR4 and the effect to glucose and lipid metabolism remain unclarified with hepatic elevation of PRSS8 expression. Here we show that high-fat-diet-fed liver-specific PRSS8 transgenic mice improved glucose tolerance and hepatic steatosis independent of body weight. PRSS8 amplified extracellular signal-regulated kinase phosphorylation associated with matrix metalloproteinase 14 activation in vivo and in vitro. Moreover, in humans, serum PRSS8 levels reduced more in type 2 diabetes mellitus (T2DM) patients than healthy controls and were lower in T2DM patients with increased maximum carotid artery intima media thickness (>1.1 mm). These results identify the regulatory mechanisms of PRSS8 overexpression over glucose and lipid metabolism, as well as excessive hepatic fat storage.

Growth factor-dependent ErbB vesicular dynamics couple receptor signaling to spatially and functionally distinct Erk pools

Growth factor-dependent vesicular dynamics allow cells to regulate the spatial distribution of growth factor receptors and thereby their coupling to downstream signaling effectors that guide cellular responses. We found that the ErbB ligands epidermal growth factor (EGF) and heregulin (HRG) generated distinct spatiotemporal patterns of cognate receptor activities to activate distinct subcellular pools of the extracellular signal-regulated kinase (Erk). Sustained plasma membrane activity of the receptor tyrosine kinases ErbB2/ErbB3 signaled to Erk complexed with the scaffold protein KSR to promote promigratory EphA2 phosphorylation and cellular motility upon HRG stimulation. In contrast, receptor-saturating EGF stimuli caused proliferation-inducing transient activation of cytoplasmic Erk due to the rapid internalization of EGF receptors (EGFR or ErbB1) toward endosomes. Paradoxically, promigratory signaling mediated by Erk complexed to KSR was sustained at low EGF concentrations by vesicular recycling that maintained steady-state amounts of active, phosphorylated EGFR at the plasma membrane. Thus, the effect of ligand identity and concentration on determining ErbB vesicular dynamics constitutes a mechanism by which cells can transduce growth factor composition through spatially distinct Erk pools to enable functionally diverse cellular responses.

A systematic analysis of signaling reactivation and drug resistance

Increasing evidence suggests that the reactivation of initially inhibited signaling pathways causes drug resistance. Here, we analyze how network topologies affect signaling responses to drug treatment. Network-dependent drug resistance is commonly attributed to negative and positive feedback loops. However, feedback loops by themselves cannot completely reactivate steady-state signaling. Newly synthesized negative feedback regulators can induce a transient overshoot but cannot fully restore output signaling. Complete signaling reactivation can only occur when at least two routes, an activating and inhibitory, connect an inhibited upstream protein to a downstream output. Irrespective of the network topology, drug-induced overexpression or increase in target dimerization can restore or even paradoxically increase downstream pathway activity. Kinase dimerization cooperates with inhibitor-mediated alleviation of negative feedback. Our findings inform drug development by considering network context and optimizing the design drug combinations. As an example, we predict and experimentally confirm specific combinations of RAF inhibitors that block mutant NRAS signaling.

Kholodenko et al. uncover signaling network circuitries and molecular mechanisms necessary and sufficient for complete reactivation or overshoot of steady-state signaling after kinase inhibitor treatment. The two means to revive signaling output fully are through network topology or reactivation of the kinase activity of the primary drug target. Blocking RAF dimer activity by a combination of type I½ and type II RAF inhibitors efficiently blocks mutant NRAS-driven ERK signaling.

Sublytic C5b-9 induces glomerular mesangial cell proliferation via ERK1/2-dependent SOX9 phosphorylation and acetylation by enhancing Cyclin D1 in rat Thy-1 nephritis

Glomerular mesangial cell (GMC) proliferation is a histopathological alteration in human mesangioproliferative glomerulonephritis (MsPGN) or in animal models of MsPGN, e.g., the rat Thy-1 nephritis (Thy-1N) model. Although sublytic C5b-9 assembly on the GMC membrane can trigger cell proliferation, the mechanisms are still undefined. We found that sublytic C5b-9-induced rat GMC proliferation was driven by extracellular signal-regulated kinase 1/2 (ERK1/2), sry-related HMG-box 9 (SOX9), and Cyclin D1. Here, ERK1/2 phosphorylation was a result of the calcium influx-PKC-α-Raf-MEK1/2 axis activated by sublytic C5b-9, and Cyclin D1 gene transcription was enhanced by ERK1/2-dependent SOX9 binding to the Cyclin D1 promoter (-582 to -238 nt). In addition, ERK1/2 not only interacted with SOX9 in the cell nucleus to mediate its phosphorylation at serine residues 64 (a new site identified by mass spectrometry) and 181 (a known site), but also indirectly induced SOX9 acetylation by elevating the expression of general control non-repressed protein 5 (GCN5), which together resulted in Cyclin D1 synthesis and GMC proliferation. Moreover, our in vivo experiments confirmed that silencing these genes ameliorated the lesions of Thy-1N rats and reduced SOX9 phosphorylation, acetylation and Cyclin D1 expression. Furthermore, the renal tissue sections of MsPGN patients also showed higher phosphorylation or expression of ERK1/2, SOX9, and Cyclin D1. In summary, these findings suggest that sublytic C5b-9-induced GMC proliferation in rat Thy-1N requires SOX9 phosphorylation and acetylation via enhanced Cyclin D1 gene transcription, which may provide a new insight into human MsPGN pathogenesis.

Building patient-specific models for receptor tyrosine kinase signaling networks

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

Pulsatile MAPK Signaling Modulates p53 Activity to Control Cell Fate Decisions at the G2 Checkpoint for DNA Damage

Cell-autonomous changes in p53 expression govern the duration and outcome of cell-cycle arrest at the G2 checkpoint for DNA damage. Here, we report that mitogen-activated protein kinase (MAPK) signaling integrates extracellular cues with p53 dynamics to determine cell fate at the G2 checkpoint. Optogenetic tools and quantitative cell biochemistry reveal transient oscillations in MAPK activity dependent on ataxia-telangiectasia-mutated kinase after DNA damage. MAPK inhibition alters p53 dynamics and p53-dependent gene expression after checkpoint enforcement, prolonging G2 arrest. In contrast, sustained MAPK signaling induces the phosphorylation of CDC25C, and consequently, the accumulation of pro-mitotic kinases, thereby relaxing checkpoint stringency and permitting cells to evade prolonged G2 arrest and senescence induction. We propose a model in which this MAPK-mediated mechanism integrates extracellular cues with cell-autonomous p53-mediated signals, to safeguard genomic integrity during tissue proliferation. Early steps in oncogene-driven carcinogenesis may imbalance this tumor-suppressive mechanism to trigger genome instability.

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DNA damage elicits opposing pro-survival and pro-arrest responses via MAPK and p53

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MAPK pulsations modulate p53-dependent transcription to determine cell fate

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MAPK/p53 signal dynamics control the stringency of the G2 DNA damage checkpoint

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MAPK/p53 integrate extracellular and intracellular cues to protect genome integrity

Differentiating cancer cells reveal early large-scale genome regulation by pericentric domains

Finding out how cells prepare for fate change during differentiation commitment was our task. To address whether the constitutive pericentromere-associated domains (PADs) may be involved, we used a model system with known transcriptome data, MCF-7 breast cancer cells treated with the ErbB3 ligand heregulin (HRG), which induces differentiation and is used in the therapy of cancer. PAD-repressive heterochromatin (H3K9me3), centromere-associated-protein-specific, and active euchromatin (H3K4me3) antibodies, real-time PCR, acridine orange DNA structural test (AOT), and microscopic image analysis were applied. We found a two-step DNA unfolding after 15–20 and 60 min of HRG treatment, respectively. This behavior was consistent with biphasic activation of the early response genes (c-fos - fosL1/myc) and the timing of two transcriptome avalanches reported in the literature. In control, the average number of PADs negatively correlated with their size by scale-free distribution, and centromere clustering in turn correlated with PAD size, both indicating that PADs may create and modulate a suprachromosomal network by fusing and splitting a constant proportion of the constitutive heterochromatin. By 15 min of HRG treatment, the bursting unraveling of PADs from the nucleolus boundary occurred, coinciding with the first step of H3K4me3 chromatin unfolding, confirmed by AOT. The second step after 60 min of HRG treatment was associated with transcription of long noncoding RNA from PADs and peaking of fosL1/c-myc response. We hypothesize that the bursting of PAD clusters under a critical silencing threshold pushes the first transcription avalanche, whereas the destruction of the PAD network enables genome rewiring needed for differentiation repatterning, mediated by early response bivalent genes.

RIP1-dependent linear and nonlinear recruitments of caspase-8 and RIP3 respectively to necrosome specify distinct cell death outcomes

There remains a significant gap in our quantitative understanding of crosstalk between apoptosis and necroptosis pathways. By employing the SWATH-MS technique, we quantified absolute amounts of up to thousands of proteins in dynamic assembling/de-assembling of TNF signaling complexes. Combining SWATH-MS-based network modeling and experimental validation, we found that when RIP1 level is below ~1000 molecules/cell (mpc), the cell solely undergoes TRADD-dependent apoptosis. When RIP1 is above ~1000 mpc, pro-caspase-8 and RIP3 are recruited to necrosome respectively with linear and nonlinear dependence on RIP1 amount, which well explains the co-occurrence of apoptosis and necroptosis and the paradoxical observations that RIP1 is required for necroptosis but its increase down-regulates necroptosis. Higher amount of RIP1 (>~46,000 mpc) suppresses apoptosis, leading to necroptosis alone. The relation between RIP1 level and occurrence of necroptosis or total cell death is biphasic. Our study provides a resource for encoding the complexity of TNF signaling and a quantitative picture how distinct dynamic interplay among proteins function as basis sets in signaling complexes, enabling RIP1 to play diverse roles in governing cell fate decisions.

A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data-Application to the ErbB Receptor Signaling Pathway

Simple Summary

Temporal signaling dynamics are important for controlling the fate decisions of mammalian cells. In this study, we developed BioMASS, a computational platform for prediction and analysis of signaling dynamics using RNA-sequencing gene expression data. We first constructed a detailed mechanistic model of early transcriptional regulation mediated by ErbB receptor signaling pathway. After training the model parameters against phosphoprotein time-course datasets obtained from breast cancer cell lines, the model successfully predicted signaling activities of another untrained cell line. The result indicates that the parameters of molecular interactions in these different cell types are not particularly unique to the cell type, and the expression levels of the components of the signaling network are sufficient to explain the complex dynamics of the networks. Our method can be further expanded to predict signaling activity from clinical gene expression data for in silico drug screening for personalized medicine.

Abstract

A current challenge in systems biology is to predict dynamic properties of cell behaviors from public information such as gene expression data. The temporal dynamics of signaling molecules is critical for mammalian cell commitment. We hypothesized that gene expression levels are tightly linked with and quantitatively control the dynamics of signaling networks regardless of the cell type. Based on this idea, we developed a computational method to predict the signaling dynamics from RNA sequencing (RNA-seq) gene expression data. We first constructed an ordinary differential equation model of ErbB receptor → c-Fos induction using a newly developed modeling platform BioMASS. The model was trained with kinetic parameters against multiple breast cancer cell lines using autologous RNA-seq data obtained from the Cancer Cell Line Encyclopedia (CCLE) as the initial values of the model components. After parameter optimization, the model proceeded to prediction in another untrained breast cancer cell line. As a result, the model learned the parameters from other cells and was able to accurately predict the dynamics of the untrained cells using only the gene expression data. Our study suggests that gene expression levels of components within the ErbB network, rather than rate constants, can explain the cell-specific signaling dynamics, therefore playing an important role in regulating cell fate.

Systems-Level Properties of EGFR-RAS-ERK Signaling Amplify Local Signals to Generate Dynamic Gene Expression Heterogeneity

Intratumoral heterogeneity is associated with aggressive tumor behavior, therapy resistance, and poor patient outcomes. Such heterogeneity is thought to be dynamic, shifting over periods of minutes to hours in response to signaling inputs from the tumor microenvironment. However, models of this process have been inferred from indirect or post-hoc measurements of cell state, leaving the temporal details of signaling-driven heterogeneity undefined. Here, we developed a live-cell model system in which microenvironment-driven signaling dynamics can be directly observed and linked to variation in gene expression. Our analysis reveals that paracrine signaling between two cell types is sufficient to drive continual diversification of gene expression programs. This diversification emerges from systems-level properties of the EGFR-RAS-ERK signaling cascade, including intracellular amplification of amphiregulin-mediated paracrine signals and differential kinetic filtering by target genes including Fra-1, c-Myc, and Egr1. Our data enable more precise modeling of paracrine-driven transcriptional variation as a generator of gene expression heterogeneity. A record of this paper's transparent peer review process is included in the Supplemental Information.

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.

Cell-Fate Determination from Embryo to Cancer Development: Genomic Mechanism Elucidated

Elucidation of the genomic mechanism that guides the cell-fate change is one of the fundamental issues of biology. We previously demonstrated that whole genome expression is coordinated by the emergence of a critical point at both the cell-population and single-cell levels through the physical principle of self-organized criticality. In this paper, we further examine the genomic mechanism that determines the cell-fate changes from embryo to cancer development. The state of the critical point, acting as the organizing center of the cell fate, determines whether the genome resides in a super- or sub-critical state. In the super-critical state, a specific stochastic perturbation can spread over the entire system through the "genome engine", an autonomous critical-control genomic system, whereas in the sub-critical state, the perturbation remains at a local level. The cell-fate changes when the genome becomes super-critical. We provide a consistent framework to develop a time-evolutional transition theory for the biological regulation of the cell-fate change.

ERK signalling: a master regulator of cell behaviour, life and fate

The proteins extracellular signal-regulated kinase 1 (ERK1) and ERK2 are the downstream components of a phosphorelay pathway that conveys growth and mitogenic signals largely channelled by the small RAS GTPases. By phosphorylating widely diverse substrates, ERK proteins govern a variety of evolutionarily conserved cellular processes in metazoans, the dysregulation of which contributes to the cause of distinct human diseases. The mechanisms underlying the regulation of ERK1 and ERK2, their mode of action and their impact on the development and homeostasis of various organisms have been the focus of much attention for nearly three decades. In this Review, we discuss the current understanding of this important class of kinases. We begin with a brief overview of the structure, regulation, substrate recognition and subcellular localization of ERK1 and ERK2. We then systematically discuss how ERK signalling regulates six fundamental cellular processes in response to extracellular cues. These processes are cell proliferation, cell survival, cell growth, cell metabolism, cell migration and cell differentiation.

Data-Driven Modeling Identifies TIRAP-Independent MyD88 Activation Complex and Myddosome Assembly Strategy in LPS/TLR4 Signaling

TLR4 complexes are essential for the initiation of the LPS-induced innate immune response. The Myddosome, which mainly contains TLR4, TIRAP, MyD88, IRAK1/4 and TRAF6 proteins, is regarded as a major complex of TLR4. Although the Myddosome has been well studied, a quantitative description of the Myddosome assembly dynamics is still lacking. Furthermore, whether some unknown TLR4 complexes exist remains unclear. In this study, we constructed a SWATH-MS data-based mathematical model that describes the component assembly dynamics of TLR4 complexes. In addition to Myddosome, we suggest that a TIRAP-independent MyD88 activation complex is formed upon LPS stimulation, in which TRAF6 is not included. Furthermore, quantitative analysis reveals that the distribution of components in TIRAP-dependent and -independent MyD88 activation complexes are LPS stimulation-dependent. The two complexes compete for recruiting IRAK1/4 proteins. MyD88 forms higher-order assembly in the Myddosome and we show that the strategy to form higher-order assembly is also LPS stimulation-dependent. MyD88 forms a long chain upon weak stimulation, but forms a short chain upon strong stimulation. Higher-order assembly of MyD88 is directly determined by the level of TIRAP in the Myddosome, providing a formation mechanism for efficient signaling transduction. Taken together, our study provides an enhanced understanding of component assembly dynamics and strategies in TLR4 complexes.

Inferring the transcriptional regulatory mechanism of signal-dependent gene expression via an integrative computational approach

Eukaryotic transcription factors (TFs) coordinate different upstream signals to regulate the expression of their target genes. To unveil this regulatory network in B-cell receptor signaling, we developed a computational pipeline to systematically analyze the extracellular signal-regulated kinase (ERK)- and IκB kinase (IKK)-dependent transcriptome responses. We combined a bilinear regression method and kinetic modeling to identify the signal-to-TF and TF-to-gene dynamics, respectively. We input a set of time-course experimental data for B cells and concentrated on transcriptional activators. The results show that the combination of TFs differentially controlled by ERK and IKK could contribute divergent expression dynamics in orchestrating the B-cell response. Our findings provide insights into the regulatory mechanisms underlying signal-dependent gene expression in eukaryotic cells.

Detection of biological switches using the method of Gröebner bases

BACKGROUND

Bistability and ability to switch between two stable states is the hallmark of cellular responses. Cellular signaling pathways often contain bistable switches that regulate the transmission of the extracellular information to the nucleus where important biological functions are executed.

RESULTS

In this work we show how the method of Gröebner bases can be used to detect bistability and output switchability. The method of Gröebner bases can be seen as a multivariate, non-linear generalization of the Gaussian elimination for linear systems which conveniently seperates the variables and drastically simplifies the simultaneous solution of polynomial equations. A necessary condition for fixed-point state bistability is for the Gröbner basis to have three distinct solutions for the state. A sufficient condition is provided by the eigenvalues of the local Jacobians. We also introduce the concept of output switchability which is defined as the ability of an output of a bistable system to switch between two different stable steady-state values. It is shown that bistability does not necessarily guarantee switchability of every state variable of the system. We further show that, for a bistable system, the necessary conditions for output switchability can be derived using the Gröebner basis. The theoretical results are incorporated into an analysis procedure and applied to several systems including the AKT (Protein kinase B), RAS (Rat Sarcoma) and MAPK (Mitogen-activated protein kinase) signal transduction pathways. Results demonstrate that the Gröebner bases can be conveniently used to analyze biological switches by simultaneously detecting bistability and output switchability.

CONCLUSION

The Gröebner bases provides a novel methodology to analyze bistability. Results clarify the distinction between bistability and output switchability which is lacking in the literature. We have shown that theoretically, it is possible to have an output subspace of an n-dimensional bistable system where certain variables cannot switch. It is possible to construct such systems as we have done with two reaction networks.

Principles and applications of optogenetics in developmental biology

The development of multicellular organisms is controlled by highly dynamic molecular and cellular processes organized in spatially restricted patterns. Recent advances in optogenetics are allowing protein function to be controlled with the precision of a pulse of laser light in vivo, providing a powerful new tool to perturb developmental processes at a wide range of spatiotemporal scales. In this Primer, we describe the most commonly used optogenetic tools, their application in developmental biology and in the nascent field of synthetic morphogenesis.

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.

Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

As malignant transformation requires synchronization of growth-driving signaling (S) and metabolic (M) pathways, defining cancer-specific S-M interconnected networks (SMINs) could lead to better understanding of oncogenic processes. In a systems-biology approach, we developed a mathematical model for SMINs in mutated EGF receptor (EGFRvIII) compared to wild-type EGF receptor (EGFRwt) expressing glioblastoma multiforme (GBM). Starting with experimentally validated human protein-protein interactome data for S-M pathways, and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data, we designed a dynamic model for EGFR-driven GBM-specific information flow. Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model. This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways, explain the robustness of oncogenic SMINs, predict drug escape, and assist identification of drug targets and the development of combination therapies.

Complex and highly dynamic interconnected networks allow cancer to take different routes and circumvent chemotherapy. Therefore, understanding these context-specific networks and their dynamics of molecular interactions driven by different oncogenic signaling and metabolic pathways is very much needed to predict drug targets and the effect of therapeutics. We incorporated high-throughput transcriptome and proteome data into mathematical models to deduce properties of cancer cells through systems biology approach. Here we report the development, testing and validation of an integrated systems biology model of information flow between signaling and metabolic pathways to understand the regulation of the interconnection between them in cancer. Our model efficiently identified unique connections and key nodes important in signaling-metabolic information flow. We predicted some potential novel targets before performing actual drug tests. We have successfully applied this model to identify the interconnections altered in the constitutive signaling of the mutated EGFR by comparing EGF-dependent and wild-type EGFR signaling in glioblastoma multiforme.

Signaling Dynamics Control Cell Fate in the Early Drosophila Embryo

The Erk mitogen-activated protein kinase plays diverse roles in animal development. Its widespread reuse raises a conundrum: when a single kinase like Erk is activated, how does a developing cell know which fate to adopt? We combine optogenetic control with genetic perturbations to dissect Erk-dependent fates in the early Drosophila embryo. We find that Erk activity is sufficient to "posteriorize" 88% of the embryo, inducing gut endoderm-like gene expression and morphogenetic movements in all cells within this region. Gut endoderm fate adoption requires at least 1 h of signaling, whereas a 30-min Erk pulse specifies a distinct ectodermal cell type, intermediate neuroblasts. We find that the endoderm-ectoderm cell fate switch is controlled by the cumulative load of Erk activity, not the duration of a single pulse. The fly embryo thus harbors a classic example of dynamic control, where the temporal profile of Erk signaling selects between distinct physiological outcomes.

Single-cell stabilization method identifies gonadotrope transcriptional dynamics and pituitary cell type heterogeneity

Immediate-early response genes (IEGs) are rapidly and transiently induced following an extracellular signal. Elucidating the IEG response patterns in single cells (SCs) requires assaying large numbers of timed samples at high accuracy while minimizing handling effects. To achieve this, we developed and validated RNA stabilization Buffer for Examination of Single-cell Transcriptomes (RNA-Best), a versatile single-step cell and tissue preservation protocol that stabilizes RNA in intact SCs without perturbing transcription patterns. We characterize for the first time SC heterogeneity in IEG responses to pulsatile gonadotropin-releasing hormone (GnRH) stimuli in pituitary gonadotrope cells. Our study identifies a gene-specific hierarchical pattern of all-or-none transcript induction elicited by increasing concentrations of GnRH. This quantal pattern of gene activation raises the possibility that IEG activation, when accurately resolved at the SC level, may be mediated by gene bits that behave as pure binary switches.