Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease.

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

Tolerance of repeated toxic injuries of murine livers is associated with steatosis and inflammation.

The human liver has a remarkable capacity to regenerate and thus compensate over decades for fibrosis caused by toxic chemicals, drugs, alcohol, or malnutrition. To date, no protective mechanisms have been identified that help the liver tolerate these repeated injuries. In this study, we revealed dysregulation of lipid metabolism and mild inflammation as protective mechanisms by studying longitudinal multi-omic measurements of liver fibrosis induced by repeated CCl4 injections in mice (n = 45). Based on comprehensive proteomics, transcriptomics, blood- and tissue-level profiling, we uncovered three phases of early disease development-initiation, progression, and tolerance. Using novel multi-omic network analysis, we identified multi-level mechanisms that are significantly dysregulated in the injury-tolerant response. Public data analysis shows that these profiles are altered in human liver diseases, including fibrosis and early cirrhosis stages. Our findings mark the beginning of the tolerance phase as the critical switching point in liver response to repetitive toxic doses. After fostering extracellular matrix accumulation as an acute response, we observe a deposition of tiny lipid droplets in hepatocytes only in the Tolerant phase. Our comprehensive study shows that lipid metabolism and mild inflammation may serve as biomarkers and are putative functional requirements to resist further disease progression.

Deciphering signal transduction networks in the liver by mechanistic mathematical modelling.

In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.

Automated Detection of Portal Fields and Central Veins in Whole-Slide Images of Liver Tissue.

Many physiological processes and pathological phenomena in the liver tissue are spatially heterogeneous. At a local scale, biomarkers can be quantified along the axis of the blood flow, from portal fields (PFs) to central veins (CVs), i.e., in zonated form. This requires detecting PFs and CVs. However, manually annotating these structures in multiple whole-slide images is a tedious task. We describe and evaluate a fully automated method, based on a convolutional neural network, for simultaneously detecting PFs and CVs in a single stained section. Trained on scans of hematoxylin and eosin-stained liver tissue, the detector performed well with an F1 score of 0.81 compared to annotation by a human expert. It does, however, not generalize well to previously unseen scans of steatotic liver tissue with an F1 score of 0.59. Automated PF and CV detection eliminates the bottleneck of manual annotation for subsequent automated analyses, as illustrated by two proof-of-concept applications: We computed lobulus sizes based on the detected PF and CV positions, where results agreed with published lobulus sizes. Moreover, we demonstrate the feasibility of zonated quantification of biomarkers detected in different stainings based on lobuli and zones obtained from the detected PF and CV positions. A negative control (hematoxylin and eosin) showed the expected homogeneity, a positive control (glutamine synthetase) was quantified to be strictly pericentral, and a plausible zonation for a heterogeneous F4/80 staining was obtained. Automated detection of PFs and CVs is one building block for automatically quantifying physiologically relevant heterogeneity of liver tissue biomarkers. Perspectively, a more robust and automated assessment of zonation from whole-slide images will be valuable for parameterizing spatially resolved models of liver metabolism and to provide diagnostic information.

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.

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.

T160­phosphorylated 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.

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.

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.

Hepatocellular carcinoma: a systems biology perspective.

Hepatocellular carcinomas (HCCs) have different etiology and heterogenic genomic alterations lead to high complexity. The molecular features of HCC have largely been studied by gene expression and proteome profiling focusing on the correlations between the expression of specific markers and clinical data. Integration of the increasing amounts of data in databases has facilitated the link of genomic and proteomic profiles of HCC to disease state and clinical outcome. Despite the current knowledge, specific molecular markers remain to be identified and new strategies are required to establish novel-targeted therapies. In the last years, mathematical models reconstructing gene and protein networks based on experimental data of HCC have been developed providing powerful tools to predict candidate interactions and potential targets for therapy. Furthermore, the combination of dynamic and logical mathematical models with quantitative data allows detailed mechanistic insights into system properties. To address effects at the organ level, mathematical models reconstructing the three-dimensional organization of liver lobules were developed. In the future, integration of different modeling approaches capturing the effects at the cellular up to the organ level is required to address the complex properties of HCC and to enable the discovery of new targets for HCC prevention or treatment.

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.

The liver-specific microRNA miR-122 controls systemic iron homeostasis in mice.

Systemic iron homeostasis is mainly controlled by the liver through synthesis of the peptide hormone hepcidin (encoded by Hamp), the key regulator of duodenal iron absorption and macrophage iron release. Here we show that the liver-specific microRNA miR-122 is important for regulating Hamp mRNA expression and tissue iron levels. Efficient and specific depletion of miR-122 by injection of a locked-nucleic-acid-modified (LNA-modified) anti-miR into WT mice caused systemic iron deficiency, characterized by reduced plasma and liver iron levels, mildly impaired hematopoiesis, and increased extramedullary erythropoiesis in the spleen. Moreover, miR-122 inhibition increased the amount of mRNA transcribed by genes that control systemic iron levels, such as hemochromatosis (Hfe), hemojuvelin (Hjv), bone morphogenetic protein receptor type 1A (Bmpr1a), and Hamp. Importantly, miR-122 directly targeted the 3' untranslated region of 2 mRNAs that encode activators of hepcidin expression, Hfe and Hjv. These data help to explain the increased Hamp mRNA levels and subsequent iron deficiency in mice with reduced miR-122 levels and establish a direct mechanistic link between miR-122 and the regulation of systemic iron metabolism.