An integrative model links multiple inputs and signaling pathways to the onset of DNA synthesis in hepatocytes

Genome-scale CRISPR screening in a single mouse liver

A complete understanding of the genetic determinants underlying mammalian physiology and disease is limited by the capacity for high-throughput genetic dissection in the living organism. Genome-wide CRISPR screening is a powerful method for uncovering the genetic regulation of cellular processes, but the need to stably deliver single guide RNAs to millions of cells has largely restricted its implementation to ex vivo systems. There thus remains a need for accessible high-throughput functional genomics in vivo. Here, we establish genome-wide screening in the liver of a single mouse and use this approach to uncover regulation of hepatocyte fitness. We uncover pathways not identified in cell culture screens, underscoring the power of genetic dissection in the organism. The approach we developed is accessible, scalable, and adaptable to diverse phenotypes and applications. We have hereby established a foundation for high-throughput functional genomics in a living mammal, enabling comprehensive investigation of physiology and disease.

Predicting liver regeneration following major resection

Breakdown of synthesis, excretion and detoxification defines liver failure. Post-hepatectomy liver failure (PHLF) is specific for liver resection and a rightfully feared complication due to high lethality and limited therapeutic success. Individual cytokine and growth factor profiles may represent potent predictive markers for recovery of liver function. We aimed to investigate these profiles in post-hepatectomy regeneration. This study combined a time-dependent cytokine and growth factor profiling dataset of a training (30 patients) and a validation (14 patients) cohorts undergoing major liver resection with statistical and predictive models identifying individual pathway signatures. 2319 associations were tested. Primary hepatocytes isolated from patient tissue samples were stimulated and their proliferation was analysed through DNA content assay. Common expression trajectories of cytokines and growth factors with strong correlation to PHLF, morbidity and mortality were identified despite highly individual perioperative dynamics. Especially, dynamics of EGF, HGF, and PLGF were associated with mortality. PLGF was additionally associated with PHLF and complications. A global association-network was calculated and validated to investigate interdependence of cytokines and growth factors with clinical attributes. Preoperative cytokine and growth factor signatures were identified allowing prediction of mortality following major liver resection by regression modelling. Proliferation analysis of corresponding primary human hepatocytes showed associations of individual regenerative potential with clinical outcome. Prediction of PHLF was possible on as early as first postoperative day (POD1) with AUC above 0.75. Prediction of PHLF and mortality is possible on POD1 with liquid-biopsy based risk profiling. Further utilization of these models would allow tailoring of interventional strategies according to individual profiles.

RORα contributes to the maintenance of genome ploidy in the liver of mice with diet-induced nonalcoholic steatohepatitis

Hepatic polyploidization is closely linked to the progression of nonalcoholic fatty liver disease (NAFLD); however, the underlying molecular mechanism is not clearly understood. In this study, we demonstrated the role of retinoic acid-related orphan receptor α (RORα) in the maintenance of genomic integrity, particularly in the pathogenesis of NAFLD, using the high-fat diet (HFD)-fed liver-specific RORα knockout (RORα-LKO) mouse model. First, we observed that the loss of hepatic retinoic acid receptor-related orphan receptor α (RORα) accelerated hepatocyte nuclear polyploidization after HFD feeding. In 70% partial hepatectomy experiments, enrichment of hepatocyte polyploidy was more obvious in the RORα-LKO animals, which was accompanied by early progression to the S phase and blockade of the G2/M transition, suggesting a potential role of RORα in suppressing hepatocyte polyploidization in the regenerating liver. An analysis of a publicly available RNA sequencing (RNA-seq) and chromatin immunoprecipitation-seq dataset, together with the Search Tool of the Retrieval of Interacting Genes/Proteins database resource, revealed that DNA endoreplication was the top-enriched biological process Gene Ontology term. Furthermore, we found that E2f7 and E2f8, which encode key transcription factors for DNA endoreplication, were the downstream targets of RORα-induced transcriptional repression. Finally, we showed that the administration of JC1-40, an RORα activator (5 mg/kg body wt), significantly reduced hepatic nuclear polyploidization in the HFD-fed mice. Together, our observations suggest that the RORα-induced suppression of hepatic polyploidization may provide new insights into the pathological polyploidy of NAFLD and may contribute to the development of therapeutic strategies for the treatment of NAFLD.NEW & NOTEWORTHY It has been reported that hepatic polyploidization is closely linked to the progression of NAFLD. Here, we showed that the genetic depletion of hepatic RORα in mice accelerated hepatocyte polyploidization after high-fat diet feeding. The mechanism could be the RORα-mediated repression of E2f7 and E2f8, key transcription factors for DNA endoreplication. Thus, preservation of genome integrity by RORα could provide a new insight for developing therapeutics against the disease.

A Robustness Analysis of Dynamic Boolean Models of Cellular Circuits

With ever growing amounts of omics data, the next challenge in biological research is the interpretation of these data to gain mechanistic insights about cellular function. Dynamic models of cellular circuits that capture the activity levels of proteins and other molecules over time offer great expressive power by allowing the simulation of the effects of specific internal or external perturbations on the workings of the cell. However, the study of such models is at its infancy and no large-scale analysis of the robustness of real models to changing conditions has been conducted to date. Here we provide a computational framework to study the robustness of such models using a combination of stochastic simulations and integer linear programming techniques. We apply our framework to a large collection of cellular circuits and benchmark the results against randomized models. We find that the steady states of real circuits tend to be more robust in multiple aspects compared with their randomized counterparts.

Creation of Three-Dimensional Liver Tissue Models from Experimental Images for Systems Medicine

In this chapter, we illustrate how three-dimensional liver tissue models can be created from experimental image modalities by utilizing a well-established processing chain of experiments, microscopic imaging, image processing, image analysis and model construction. We describe how key features of liver tissue architecture are quantified and translated into model parameterizations, and show how a systematic iteration of experiments and model simulations often leads to a better understanding of biological phenomena in systems biology and systems medicine.

A blueprint for human whole-cell modeling

Whole-cell dynamical models of human cells are a central goal of systems biology. Such models could help researchers understand cell biology and help physicians treat disease. Despite significant challenges, we believe that human whole-cell models are rapidly becoming feasible. To develop a plan for achieving human whole-cell models, we analyzed the existing models of individual cellular pathways, surveyed the biomodeling community, and reflected on our experience developing whole-cell models of bacteria. Based on these analyses, we propose a plan for a project, termed the Human Whole-Cell Modeling Project, to achieve human whole-cell models. The foundations of the plan include technology development, standards development, and interdisciplinary collaboration.

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.

BMP-9 interferes with liver regeneration and promotes liver fibrosis

OBJECTIVE

Bone morphogenetic protein (BMP)-9, a member of the transforming growth factor-β family of cytokines, is constitutively produced in the liver. Systemic levels act on many organs and tissues including bone and endothelium, but little is known about its hepatic functions in health and disease.

DESIGN

Levels of BMP-9 and its receptors were analysed in primary liver cells. Direct effects of BMP-9 on hepatic stellate cells (HSCs) and hepatocytes were studied in vitro, and the role of BMP-9 was examined in acute and chronic liver injury models in mice.

RESULTS

Quiescent and activated HSCs were identified as major BMP-9 producing liver cell type. BMP-9 stimulation of cultured hepatocytes inhibited proliferation, epithelial to mesenchymal transition and preserved expression of important metabolic enzymes such as cytochrome P450. Acute liver injury caused by partial hepatectomy or single injections of carbon tetrachloride (CCl₄) or lipopolysaccharide (LPS) into mice resulted in transient downregulation of hepatic BMP-9 mRNA expression. Correspondingly, LPS stimulation led to downregulation of BMP-9 expression in cultured HSCs. Application of BMP-9 after partial hepatectomy significantly enhanced liver damage and disturbed the proliferative response. Chronic liver damage in BMP-9-deficient mice or in mice adenovirally overexpressing the selective BMP-9 antagonist activin-like kinase 1-Fc resulted in reduced deposition of collagen and subsequent fibrosis.

CONCLUSIONS

Constitutive expression of low levels of BMP-9 stabilises hepatocyte function in the healthy liver. Upon HSC activation, endogenous BMP-9 levels increase in vitro and in vivo and high levels of BMP-9 cause enhanced damage upon acute or chronic injury.

Cell cycle proliferation decisions: the impact of single cell analyses

Cell proliferation is a fundamental requirement for organismal development and homeostasis. The mammalian cell division cycle is tightly controlled to ensure complete and precise genome duplication and segregation of replicated chromosomes to daughter cells. The onset of DNA replication marks an irreversible commitment to cell division, and the accumulated efforts of many decades of molecular and cellular studies have probed this cellular decision, commonly called the restriction point. Despite a long-standing conceptual framework of the restriction point for progression through G1 phase into S phase or exit from G1 phase to quiescence (G0), recent technical advances in quantitative single cell analysis of mammalian cells have provided new insights. Significant intercellular heterogeneity revealed by single cell studies and the discovery of discrete subpopulations in proliferating cultures suggests the need for an even more nuanced understanding of cell proliferation decisions. In this review, we describe some of the recent developments in the cell cycle field made possible by quantitative single cell experimental approaches.

Qualitative Dynamical Modelling Can Formally Explain Mesoderm Specification and Predict Novel Developmental Phenotypes

Given the complexity of developmental networks, it is often difficult to predict the effect of genetic perturbations, even within coding genes. Regulatory factors generally have pleiotropic effects, exhibit partially redundant roles, and regulate highly interconnected pathways with ample cross-talk. Here, we delineate a logical model encompassing 48 components and 82 regulatory interactions involved in mesoderm specification during Drosophila development, thereby providing a formal integration of all available genetic information from the literature. The four main tissues derived from mesoderm correspond to alternative stable states. We demonstrate that the model can predict known mutant phenotypes and use it to systematically predict the effects of over 300 new, often non-intuitive, loss- and gain-of-function mutations, and combinations thereof. We further validated several novel predictions experimentally, thereby demonstrating the robustness of model. Logical modelling can thus contribute to formally explain and predict regulatory outcomes underlying cell fate decisions.

T160‐phosphorylated CDK2 defines threshold for HGF dependent proliferation in primary hepatocytes

Liver regeneration is a tightly controlled process mainly achieved by proliferation of usually quiescent hepatocytes. The specific molecular mechanisms ensuring cell division only in response to proliferative signals such as hepatocyte growth factor (HGF) are not fully understood. Here, we combined quantitative time-resolved analysis of primary mouse hepatocyte proliferation at the single cell and at the population level with mathematical modeling. We showed that numerous G1/S transition components are activated upon hepatocyte isolation whereas DNA replication only occurs upon additional HGF stimulation. In response to HGF, Cyclin:CDK complex formation was increased, p21 rather than p27 was regulated, and Rb expression was enhanced. Quantification of protein levels at the restriction point showed an excess of CDK2 over CDK4 and limiting amounts of the transcription factor E2F-1. Analysis with our mathematical model revealed that T160 phosphorylation of CDK2 correlated best with growth factor-dependent proliferation, which we validated experimentally on both the population and the single cell level. In conclusion, we identified CDK2 phosphorylation as a gate-keeping mechanism to maintain hepatocyte quiescence in the absence of HGF.

Context-specific flow through the MEK/ERK module produces cell- and ligand-specific patterns of ERK single and double phosphorylation

The same pathway, such as the mitogen-activated protein kinase (MAPK) pathway, can produce different cellular responses, depending on stimulus or cell type. We examined the phosphorylation dynamics of the MAPK kinase MEK and its targets extracellular signal-regulated kinase 1 and 2 (ERK1/2) in primary hepatocytes and the transformed keratinocyte cell line HaCaT A5 exposed to either hepatocyte growth factor or interleukin-6. By combining quantitative mass spectrometry with dynamic modeling, we elucidated network structures for the reversible threonine and tyrosine phosphorylation of ERK in both cell types. In addition to differences in the phosphorylation and dephosphorylation reactions, the HaCaT network model required two feedback mechanisms, which, as the experimental data suggested, involved the induction of the dual-specificity phosphatase DUSP6 and the scaffold paxillin. We assayed and modeled the accumulation of the double-phosphorylated and active form of ERK1/2, as well as the dynamics of the changes in the monophosphorylated forms of ERK1/2. Modeling the differences in the dynamics of the changes in the distributions of the phosphorylated forms of ERK1/2 suggested that different amounts of MEK activity triggered context-specific responses, with primary hepatocytes favoring the formation of double-phosphorylated ERK1/2 and HaCaT A5 cells that produce both the threonine-phosphorylated and the double-phosphorylated form. These differences in phosphorylation distributions explained the threshold, sensitivity, and saturation of the ERK response. We extended the findings of differential ERK phosphorylation profiles to five additional cultured cell systems and matched liver tumor and normal tissue, which revealed context-specific patterns of the various forms of phosphorylated ERK.

Systems biology of IL-6, IL-12 family cytokines

Interleukin-6-type cytokines play important roles in the communication between cells of multicellular organisms. They are involved in the regulation of complex cellular processes such as proliferation and differentiation and act as key player during inflammation and immune response. A major challenge is to understand how these complex non-linear processes are connected and regulated. Systems biology approaches are used to tackle this challenge in an iterative process of quantitative experimental and mathematical analyses. Here we review quantitative experimental studies and systems biology approaches dealing with the function of Interleukin-6-type cytokines in physiological and pathophysiological conditions. These approaches cover the analyses of signal transduction on a cellular level up to pharmacokinetic and pharmacodynamic studies on a whole organism level.

Disentangling the Complexity of HGF Signaling by Combining Qualitative and Quantitative Modeling

Signaling pathways are characterized by crosstalk, feedback and feedforward mechanisms giving rise to highly complex and cell-context specific signaling networks. Dissecting the underlying relations is crucial to predict the impact of targeted perturbations. However, a major challenge in identifying cell-context specific signaling networks is the enormous number of potentially possible interactions. Here, we report a novel hybrid mathematical modeling strategy to systematically unravel hepatocyte growth factor (HGF) stimulated phosphoinositide-3-kinase (PI3K) and mitogen activated protein kinase (MAPK) signaling, which critically contribute to liver regeneration. By combining time-resolved quantitative experimental data generated in primary mouse hepatocytes with interaction graph and ordinary differential equation modeling, we identify and experimentally validate a network structure that represents the experimental data best and indicates specific crosstalk mechanisms. Whereas the identified network is robust against single perturbations, combinatorial inhibition strategies are predicted that result in strong reduction of Akt and ERK activation. Thus, by capitalizing on the advantages of the two modeling approaches, we reduce the high combinatorial complexity and identify cell-context specific signaling networks.

Unraveling liver complexity from molecular to organ level: challenges and perspectives

Biological responses are determined by information processing at multiple and highly interconnected scales. Within a tissue the individual cells respond to extracellular stimuli by regulating intracellular signaling pathways that in turn determine cell fate decisions and influence the behavior of neighboring cells. As a consequence the cellular responses critically impact tissue composition and architecture. Understanding the regulation of these mechanisms at different scales is key to unravel the emergent properties of biological systems. In this perspective, a multidisciplinary approach combining experimental data with mathematical modeling is introduced. We report the approach applied within the Virtual Liver Network to analyze processes that regulate liver functions from single cell responses to the organ level using a number of examples. By facilitating interdisciplinary collaborations, the Virtual Liver Network studies liver regeneration and inflammatory processes as well as liver metabolic functions at multiple scales, and thus provides a suitable example to identify challenges and point out potential future application of multi-scale systems biology.

Suppression of TDO-mediated tryptophan catabolism in glioblastoma cells by a steroid-responsive FKBP52-dependent pathway

Tryptophan catabolism is increasingly recognized as a key and druggable molecular mechanism active in cancer, immune, and glioneural cells and involved in the modulation of antitumor immunity, autoimmunity and glioneural function. In addition to the pivotal rate limiting enzyme indoleamine-2,3-dioxygenase, expression of tryptophan-2,3-dioxygenase (TDO) has recently been described as an alternative pathway responsible for constitutive tryptophan degradation in malignant gliomas and other types of cancer. In addition, TDO has been implicated as a key regulator of neurotoxicity involved in neurodegenerative diseases and ageing. The pathways regulating TDO expression, however, are largely unknown. Here, a siRNA-based transcription factor profiling in human glioblastoma cells revealed that the expression of human TDO is suppressed by endogenous glucocorticoid signaling. Similarly, treatment of glioblastoma cells with the synthetic glucocorticoid dexamethasone led to a reduction of TDO expression and activity in vitro and in vivo. TDO inhibition was dependent on the immunophilin FKBP52, whose FK1 domain physically interacted with the glucocorticoid receptor as demonstrated by bimolecular fluorescence complementation and in situ proximity ligation assays. Accordingly, gene expression profile analyses revealed negative correlation of FKBP52 and TDO in glial and neural tumors and in normal brain. Knockdown of FKBP52 and treatment with the FK-binding immunosuppressant FK506 enhanced TDO expression and activity in glioblastoma cells. In summary, we identify a novel steroid-responsive FKBP52-dependent pathway suppressing the expression and activity of TDO, a central and rate-limiting enzyme in tryptophan metabolism, in human gliomas.

Logical modelling of Drosophila signalling pathways

A limited number of signalling pathways are involved in the specification of cell fate during the development of all animals. Several of these pathways were originally identified in Drosophila. To clarify their roles, and possible cross-talk, we have built a logical model for the nine key signalling pathways recurrently used in metazoan development. In each case, we considered the associated ligands, receptors, signal transducers, modulators, and transcription factors reported in the literature. Implemented using the logical modelling software GINsim, the resulting models qualitatively recapitulate the main characteristics of each pathway, in wild type as well as in various mutant situations (e.g. loss-of-function or gain-of-function). These models constitute pluggable modules that can be used to assemble comprehensive models of complex developmental processes. Moreover, these models of Drosophila pathways could serve as scaffolds for more complicated models of orthologous mammalian pathways. Comprehensive model annotations and GINsim files are provided for each of the nine considered pathways.

Unbiased RNAi screen for hepcidin regulators links hepcidin suppression to proliferative Ras/RAF and nutrient-dependent mTOR signaling

The hepatic hormone hepcidin is a key regulator of systemic iron metabolism. Its expression is largely regulated by 2 signaling pathways: the "iron-regulated" bone morphogenetic protein (BMP) and the inflammatory JAK-STAT pathways. To obtain broader insights into cellular processes that modulate hepcidin transcription and to provide a resource to identify novel genetic modifiers of systemic iron homeostasis, we designed an RNA interference (RNAi) screen that monitors hepcidin promoter activity after the knockdown of 19 599 genes in hepatocarcinoma cells. Interestingly, many of the putative hepcidin activators play roles in signal transduction, inflammation, or transcription, and affect hepcidin transcription through BMP-responsive elements. Furthermore, our work sheds light on new components of the transcriptional machinery that maintain steady-state levels of hepcidin expression and its responses to the BMP- and interleukin-6-triggered signals. Notably, we discover hepcidin suppression mediated via components of Ras/RAF MAPK and mTOR signaling, linking hepcidin transcriptional control to the pathways that respond to mitogen stimulation and nutrient status. Thus using a combination of RNAi screening, reverse phase protein arrays, and small molecules testing, we identify links between the control of systemic iron homeostasis and critical liver processes such as regeneration, response to injury, carcinogenesis, and nutrient metabolism.

Systems genetics of hepatocellular damage in vivo and in vitro: identification of a critical network on chromosome 11 in mouse

Quantitative trait locus (QTL) mapping is a powerful method to find modifier loci that influence disease risk and progression without prior knowledge of underlying genetic mechanisms. The aim of this study is to identify gene loci that contribute to individual differences in liver fibrosis following chronic liver damage. For this purpose, we carried out a mapping study across a panel of 21 BXD recombinant inbred strains using primary hepatocytes challenged with transforming growth factor (TGF)-β for 48 h. We identified a 6 Mb interval on chromosome 11 that is a major modifier of TGF-β-induced hepatocyte injury. Corresponding in vivo genetic analysis of fibrosis after chronic hepatotoxic injury by carbon tetrachloride (CCl4 ip for 6 wk) highlighted the same locus. Expression QTL (eQTL) analysis in liver tissues in the BXD family identified six polymorphisms in this region that are associated with strong cis eQTLs and that correlate well with gene expression in liver after both 6 wk CCl4 treatment and acute ethanol damage of the liver. Within this interval we rank two genes containing coding sequence variants as strong candidates that may modulate the severity of liver fibrosis: 1) the extracellular proteinase inhibitor gene Expi (also known as Wdnm1 or Wfdc18) and 2) musashi RNA-binding protein 2 (Msi2). The powerful combination of experimental, genetics, and bioinformatics methods, as well as combined in vitro and in vivo approaches can be used to define QTLs, genes, and even candidate sequence variants linked to hepatotoxicity and fibrosis.

Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks

A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models.Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input-output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous.We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to logical and eventually to logic-based ODE models. Importantly, systems and network properties determined in the rougher representation are conserved during these transformations.

Cellular ERK phospho-form profiles with conserved preference for a switch-like pattern

ERK is a member of the MAPK pathway with essential functions in cell proliferation, differentiation, and survival. Complete ERK activation by the kinase MEK requires dual phosphorylation at T and Y within the activation motif TEY. We show that exposure of primary mouse hepatocytes to hepatocyte growth factor (HGF) results in phosphorylation at the activation motif, but not of other residues nearby. To determine the relative abundances of unphosphorylated ERK and the three ERK phospho-forms pT, pY, and pTpY, we employed an extended one-source peptide/phosphopeptide standard method in combination with nanoUPLC-MS. This method enabled us to determine the abundances of phospho-forms with a relative variability of ≤5% (SD). We observed a switch-like preference of ERK phospho-form abundances toward the active, doubly phosphorylated and the inactive, unphosphorylated form. Interestingly, ERK phospho-form profiles were similar upon growth factor and cytokine stimulation. A screening of several murine and human cell systems revealed that the balance between TY- and pTpY-ERK is conserved while the abundances of pT- and pY-ERK are more variable within cell types. We show that the phospho-form profiles do not change by blocking MEK activity suggesting that cellular phosphatases determine the ERK phospho-form distribution. This study provides novel quantitative insights into multisite phosphorylation.

Heterogeneous kinetics of AKT signaling in individual cells are accounted for by variable protein concentration

In most solid cancers, cells harboring oncogenic mutations represent only a sub-fraction of the entire population. Within this sub-fraction the expression level of mutated proteins can vary significantly due to cellular variability limiting the efficiency of targeted therapy. To address the causes of the heterogeneity, we performed a systematic analysis of one of the most frequently mutated pathways in cancer cells, the phosphatidylinositol 3 kinase (PI3K) signaling pathway. Among others PI3K signaling is activated by the hepatocyte growth factor (HGF) that regulates proliferation of hepatocytes during liver regeneration but also fosters tumor cell proliferation. HGF-mediated responses of PI3K signaling were monitored both at the single cell and cell population level in primary mouse hepatocytes and in the hepatoma cell line Hepa1_6. Interestingly, we observed that the HGF-mediated AKT responses at the level of individual cells is rather heterogeneous. However, the overall average behavior of the single cells strongly resembled the dynamics of AKT activation determined at the cell population level. To gain insights into the molecular cause for the observed heterogeneous behavior of individual cells, we employed dynamic mathematical modeling in a stochastic framework. Our analysis demonstrated that intrinsic noise was not sufficient to explain the observed kinetic behavior, but rather the importance of extrinsic noise has to be considered. Thus, distinct from gene expression in the examined signaling pathway fluctuations of the reaction rates has only a minor impact whereas variability in the concentration of the various signaling components even in a clonal cell population is a key determinant for the kinetic behavior.