Role of cAMP and phosphodiesterase signaling in liver health and disease

Liver disease is a significant health problem worldwide with mortality reaching around 2 million deaths a year. Non-alcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD) are the major causes of chronic liver disease. Pathologically, NAFLD and ALD share similar patterns of hepatic disorders ranging from simple steatosis to steatohepatitis, fibrosis and cirrhosis. It is becoming increasingly important to identify new pharmacological targets, given that there is no FDA-approved therapy yet for either NAFLD or ALD. Since the evolution of liver diseases is a multifactorial process, several mechanisms involving parenchymal and non-parenchymal hepatic cells contribute to the initiation and progression of liver pathologies. Moreover, certain protective molecular pathways become repressed during liver injury including signaling pathways such as the cyclic adenosine monophosphate (cAMP) pathway. cAMP, a key second messenger molecule, regulates various cellular functions including lipid metabolism, inflammation, cell differentiation and injury by affecting gene/protein expression and function. This review addresses the current understanding of the role of cAMP metabolism and consequent cAMP signaling pathway(s) in the context of liver health and disease. The cAMP pathway is extremely sophisticated and complex with specific cellular functions dictated by numerous factors such abundance, localization and degradation by phosphodiesterases (PDEs). Furthermore, because of the distinct yet divergent roles of both of its effector molecules, the cAMP pathway is extensively targeted in liver injury to modify its role from physiological to therapeutic, depending on the hepatic condition. This review also examines the behavior of the cAMP-dependent pathway in NAFLD, ALD and in other liver diseases and focuses on PDE inhibition as an excellent therapeutic target in these conditions.     

A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach

A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.     

白细胞介素-6在酒精性肝病中的作用

酒精性肝病(alcoholic liver disease,ALD)是由于长期过量饮酒导致肝的内部组织发生炎症损伤的慢性肝病.乙醇及其衍生物在代谢过程中直接或间接诱导引起的肝炎症反应可能是ALD发病的重要机制.然而,该过程内在的细胞分子机制尚不明确.最新研究发现,白细胞介素-6(interleukin-6,IL-6)对...     

Understanding signaling in yeast: Insights from network analysis

Reconstruction of protein interaction networks that represent groups of proteins contributing to the same cellular function is a key step towards quantitative studies of signal transduction pathways. Here we present a novel approach to reconstruct a highly correlated protein interaction network and to identify previously unknown components of a signaling pathway through integration of protein-protein interaction data, gene expression data, and Gene Ontology annotations. A novel algorithm is designed to reconstruct a highly correlated protein interaction network which is composed of the candidate proteins for signal transduction mechanisms in yeast Saccharomyces cerevisiae. The high efficiency of the reconstruction process is proved by a Receiver Operating Characteristic curve analysis. Identification and scoring of the possible linear pathways enables reconstruction of specific sub-networks for glucose-induction signaling and high osmolarity MAPK signaling in S. cerevisiae. All of the known components of these pathways are identified together with several new "candidate" proteins, indicating the successful reconstructions of two model pathways involved in S. cerevisiae. The integrated approach is hence shown useful for (i) prediction of new signaling pathways, (ii) identification of unknown members of documented pathways, and (iii) identification of network modules consisting of a group of related components that often incorporate the same functional mechanism.     

Data-Derived Modeling Characterizes Plasticity of MAPK Signaling in Melanoma

The majority of melanomas have been shown to harbor somatic mutations in the RAS-RAF-MEK-MAPK and PI3K-AKT pathways, which play a major role in regulation of proliferation and survival. The prevalence of these mutations makes these kinase signal transduction pathways an attractive target for cancer therapy. However, tumors have generally shown adaptive resistance to treatment. This adaptation is achieved in melanoma through its ability to undergo neovascularization, migration and rearrangement of signaling pathways. To understand the dynamic, nonlinear behavior of signaling pathways in cancer, several computational modeling approaches have been suggested. Most of those models require that the pathway topology remains constant over the entire observation period. However, changes in topology might underlie adaptive behavior to drug treatment. To study signaling rearrangements, here we present a new approach based on Fuzzy Logic (FL) that predicts changes in network architecture over time. This adaptive modeling approach was used to investigate pathway dynamics in a newly acquired experimental dataset describing total and phosphorylated protein signaling over four days in A375 melanoma cell line exposed to different kinase inhibitors. First, a generalized strategy was established to implement a parameter-reduced FL model encoding non-linear activity of a signaling network in response to perturbation. Next, a literature-based topology was generated and parameters of the FL model were derived from the full experimental dataset. Subsequently, the temporal evolution of model performance was evaluated by leaving time-defined data points out of training. Emerging discrepancies between model predictions and experimental data at specific time points allowed the characterization of potential network rearrangement. We demonstrate that this adaptive FL modeling approach helps to enhance our mechanistic understanding of the molecular plasticity of melanoma.     

Carotenoids and fatty liver disease: Current knowledge and research gaps

Carotenoids form an important part of the human diet, consumption of which has been associated with many health benefits. With the growing global burden of liver disease, increasing attention has been paid on the possible beneficial role that carotenoids may play in the liver. This review focuses on carotenoid actions in non-alcoholic fatty liver disease (NAFLD), and alcoholic liver disease (ALD). Indeed, many human studies have suggested an association between decreased circulating levels of carotenoids and increased incidence of NAFLD and ALD. The literature describing supplementation of individual carotenoids in rodent models of NAFLD and ALD is reviewed, with particular attention paid to β-carotene and lycopene, but also including β-cryptoxanthin, lutein, zeaxanthin, and astaxanthin. The effect of beta-carotene oxygenase 1 and 2 knock-out mice on hepatic lipid metabolism is also discussed. In general, there is evidence to suggest that carotenoids have beneficial effects in animal models of both NAFLD and ALD. Mechanistically, these benefits may occur via three possible modes of action: 1) improved hepatic antioxidative status broadly attributed to carotenoids in general, 2) the generation of vitamin A from β-carotene and β-cryptoxanthin, leading to improved hepatic retinoid signaling, and 3) the generation of apocarotenoid metabolites from β-carotene and lycopene, that may regulate hepatic signaling pathways. Gaps in our knowledge regarding carotenoid mechanisms of action in the liver are highlighted throughout, and the review ends by emphasizing the importance of dose effects, mode of delivery, and mechanism of action as important areas for further study. This article is part of a Special Issue entitled Carotenoids recent advances in cell and molecular biology edited by Johannes von Lintig and Loredana Quadro.     

Dynamic cross-talk analysis among TNF-R,TLR-4 and IL-1R signalings in TNFα-induced inflammatory responses

Background

Development in systems biology research has accelerated in recent years, and the reconstructions for molecular networks can provide a global view to enable in-depth investigation on numerous system properties in biology. However, we still lack a systematic approach to reconstruct the dynamic protein-protein association networks at different time stages from high-throughput data to further analyze the possible cross-talks among different signaling/regulatory pathways.

Methods

In this study we integrated protein-protein interactions from different databases to construct the rough protein-protein association networks (PPANs) during TNFα-induced inflammation. Next, the gene expression profiles of TNFα-induced HUVEC and a stochastic dynamic model were used to rebuild the significant PPANs at different time stages, reflecting the development and progression of endothelium inflammatory responses. A new cross-talk ranking method was used to evaluate the potential core elements in the related signaling pathways of toll-like receptor 4 (TLR-4) as well as receptors for tumor necrosis factor (TNF-R) and interleukin-1 (IL-1R).

Results

The highly ranked cross-talks which are functionally relevant to the TNFα pathway were identified. A bow-tie structure was extracted from these cross-talk pathways, suggesting the robustness of network structure, the coordination of signal transduction and feedback control for efficient inflammatory responses to different stimuli. Further, several characteristics of signal transduction and feedback control were analyzed.

Conclusions

A systematic approach based on a stochastic dynamic model is proposed to generate insight into the underlying defense mechanisms of inflammation via the construction of corresponding signaling networks upon specific stimuli. In addition, this systematic approach can be applied to other signaling networks under different conditions in different species. The algorithm and method proposed in this study could expedite prospective systems biology research when better experimental techniques for protein expression detection and microarray data with multiple sampling points become available in the future.

     

Depdc5 deficiency exacerbates alcohol-induced hepatic steatosis via suppression of PPARα pathway

Alcohol-related liver disease (ALD), a condition caused by alcohol overconsumption, occurs in three stages of liver injury including steatosis, hepatitis, and cirrhosis. DEP domain-containing protein 5 (DEPDC5), a component of GAP activities towards Rags 1 (GATOR1) complex, is a repressor of amino acid-sensing branch of the mammalian target of rapamycin complex 1 (mTORC1) pathway. In the current study, we found that aberrant activation of mTORC1 was likely attributed to the reduction of DEPDC5 in the livers of ethanol-fed mice or ALD patients. To further define the in vivo role of DEPDC5 in ALD development, we generated Depdc5 hepatocyte-specific knockout mouse model (Depdc5-LKO) in which mTORC1 pathway was constitutively activated through loss of the inhibitory effect of GATOR1. Hepatic Depdc5 ablation leads to mild hepatomegaly and liver injury and protects against diet-induced liver steatosis. In contrast, ethanol-fed Depdc5-LKO mice developed severe hepatic steatosis and inflammation. Pharmacological intervention with Torin 1 suppressed mTORC1 activity and remarkably ameliorated ethanol-induced hepatic steatosis and inflammation in both control and Depdc5-LKO mice. The pathological effect of sustained mTORC1 activity in ALD may be attributed to the suppression of peroxisome proliferator activated receptor α (PPARα), the master regulator of fatty acid oxidation in hepatocytes, because fenofibrate (PPARα agonist) treatment reverses ethanol-induced liver steatosis and inflammation in Depdc5-LKO mice. These findings provide novel insights into the in vivo role of hepatic DEPDC5 in the development of ALD.Subject terms: Alcoholic liver disease, Experimental models of disease     

A Three Stage Integrative Pathway Search (<Emphasis Type="Italic">TIPS</Emphasis><Superscript>©</Superscript>) framework to identify toxicity relevant genes and pathways

Background

The ability to obtain profiles of gene expressions, proteins and metabolites with the advent of high throughput technologies has advanced the study of pathway and network reconstruction. Genome-wide network reconstruction requires either interaction measurements or large amount of perturbation data, often not available for mammalian cell systems. To overcome these shortcomings, we developed a Three Stage Integrative Pathway Search (TIPS^©) approach to reconstruct context-specific active pathways involved in conferring a specific phenotype, from limited amount of perturbation data. The approach was tested on human liver cells to identify pathways that confer cytotoxicity.

Results

This paper presents a systems approach that integrates gene expression and cytotoxicity profiles to identify a network of pathways involved in free fatty acid (FFA) and tumor necrosis factor-α (TNF-α) induced cytotoxicity in human hepatoblastoma cells (HepG2/C3A). Cytotoxicity relevant genes were first identified and then used to reconstruct a network using Bayesian network (BN) analysis. BN inference was used subsequently to predict the effects of perturbing a gene on the other genes in the network and on the cytotoxicity. These predictions were subsequently confirmed through the published literature and further experiments.

Conclusion

The TIPS^© approach is able to reconstruct active pathways that confer a particular phenotype by integrating gene expression and phenotypic profiles. A web-based version of TIPS^© that performs the analysis described herein can be accessed at http://www.egr.msu.edu/tips.

     

Time course of gene expression profiling in the liver of experimental mice infected with Echinococcus multilocularis

Background

Alveolar echinococcosis (AE) is a severe chronic parasitic disease which behaves like a slow-growing liver cancer. Clinical observations suggest that the parasite, Echinococcus multilocularis (E. multilocularis) influences liver homeostasis and hepatic cell metabolism. However, this has never been analyzed during the time course of infection in the common model of secondary echinococcosis in experimental mice.

Methodology/Principal Findings

Gene expression profiles were assessed using DNA microarray analysis, 1, 2, 3 and 6 months after injection of E. multilocularis metacestode in the liver of susceptible mice. Data were collected at different time points to monitor the dynamic behavior of gene expression. 557 differentially expressed genes were identified at one or more time points, including 351 up-regulated and 228 down-regulated genes. Time-course analysis indicated, at the initial stage of E. multilocularis infection (month 1–2), that most of up-regulated pathways were related to immune processes and cell trafficking such as chemokine-, mitogen-activated protein kinase (MAPK) signaling, and down-regulated pathways were related to xenobiotic metabolism; at the middle stage (month 3), MAPK signaling pathway was maintained and peroxisome proliferator-activated receptor (PPAR) signaling pathway emerged; at the late stage (month 6), most of up-regulated pathways were related to PPAR signaling pathway, complement and coagulation cascades, while down-regulated pathways were related to metabolism of xenobiotics by cytochrome P450. Quantitative RT-PCR analysis of a random selection of 19 genes confirmed the reliability of the microarray data. Immunohistochemistry analysis showed that proliferating cell nuclear antigen (PCNA) was increased in the liver of E. multilocularis infected mice from 2 months to 6 months.

Conclusions

E. multilocularis metacestode definitely exerts a deep influence on liver homeostasis, by modifying a number of gene expression and metabolic pathways. It especially promotes hepatic cell proliferation, as evidenced by the increased PCNA constantly found in all the experimental time-points we studied and by an increased gene expression of key metabolic pathways.     

A Kalman-Filter Based Approach to Identification of Time-Varying Gene Regulatory Networks

Motivation

Conventional identification methods for gene regulatory networks (GRNs) have overwhelmingly adopted static topology models, which remains unchanged over time to represent the underlying molecular interactions of a biological system. However, GRNs are dynamic in response to physiological and environmental changes. Although there is a rich literature in modeling static or temporally invariant networks, how to systematically recover these temporally changing networks remains a major and significant pressing challenge. The purpose of this study is to suggest a two-step strategy that recovers time-varying GRNs.

Results

It is suggested in this paper to utilize a switching auto-regressive model to describe the dynamics of time-varying GRNs, and a two-step strategy is proposed to recover the structure of time-varying GRNs. In the first step, the change points are detected by a Kalman-filter based method. The observed time series are divided into several segments using these detection results; and each time series segment belonging to two successive demarcating change points is associated with an individual static regulatory network. In the second step, conditional network structure identification methods are used to reconstruct the topology for each time interval. This two-step strategy efficiently decouples the change point detection problem and the topology inference problem. Simulation results show that the proposed strategy can detect the change points precisely and recover each individual topology structure effectively. Moreover, computation results with the developmental data of Drosophila Melanogaster show that the proposed change point detection procedure is also able to work effectively in real world applications and the change point estimation accuracy exceeds other existing approaches, which means the suggested strategy may also be helpful in solving actual GRN reconstruction problem.     

Cross Tissue Trait-Pathway Network Reveals the Importance of Oxidative Stress and Inflammation Pathways in Obesity-Induced Diabetes in Mouse

Complex disorders often involve dysfunctions in multiple tissue organs. Elucidating the communication among them is important to understanding disease pathophysiology. In this study we integrate multiple tissue gene expression and quantitative trait measurements of an obesity-induced diabetes mouse model, with databases of molecular interaction networks, to construct a cross tissue trait-pathway network. The animals belong to two strains of mice (BTBR or B6), of two obesity status (obese or lean), and at two different ages (4 weeks and 10 weeks). Only 10 week obese BTBR animals are diabetic. The expression data was first utilized to determine the state of every pathway in each tissue, which is subsequently utilized to construct a pathway co-expression network and to define trait-relevant and trait-linking pathways. Among the six tissues profiled, the adipose contains the largest number of trait-linking pathways. Among the eight traits measured, the body weight and plasma insulin level possess the most number of relevant and linking pathways. Topological analysis of the trait-pathway network revealed that the glycolysis/gluconeogenesis pathway in liver and the insulin signaling pathway in muscle are of top importance to the information flow in the network, with the highest degrees and betweenness centralities. Interestingly, pathways related to metabolism and oxidative stress actively interact with many other pathways in all animals, whereas, among the 10 week animals, the inflammation pathways were preferentially interactive in the diabetic ones only. In summary, our method offers a systems approach to delineate disease trait relevant intra- and cross tissue pathway interactions, and provides insights to the molecular basis of the obesity-induced diabetes.     

Synergy analysis reveals association between insulin signaling and desmoplakin expression in palmitate treated HepG2 cells

The regulation of complex cellular activities in palmitate treated HepG2 cells, and the ensuing cytotoxic phenotype, involves cooperative interactions between genes. While previous approaches have largely focused on identifying individual target genes, elucidating interacting genes has thus far remained elusive. We applied the concept of information synergy to reconstruct a "gene-cooperativity" network for palmititate-induced cytotoxicity in liver cells. Our approach integrated gene expression data with metabolic profiles to select a subset of genes for network reconstruction. Subsequent analysis of the network revealed insulin signaling as the most significantly enriched pathway, and desmoplakin (DSP) as its top neighbor. We determined that palmitate significantly reduces DSP expression, and treatment with insulin restores the lost expression of DSP. Insulin resistance is a common pathological feature of fatty liver and related ailments, whereas loss of DSP has been noted in liver carcinoma. Reduced DSP expression can lead to loss of cell-cell adhesion via desmosomes, and disrupt the keratin intermediate filament network. Our findings suggest that DSP expression may be perturbed by palmitate and, along with insulin resistance, may play a role in palmitate induced cytotoxicity, and serve as potential targets for further studies on non-alcoholic fatty liver disease (NAFLD).     

Zinc prevention and treatment of alcoholic liver disease

Alcoholic liver disease (ALD) is associated with decreases in zinc (Zn) and its major binding protein, metallothionein (MT), in the liver. Studies using animal models have shown that Zn supplementation prevents alcohol-induced liver injury under both acute and chronic alcohol exposure conditions. There are hepatic and extrahepatic actions of Zn in the prevention of alcoholic liver injury. Zn supplementation attenuates ethanol-induced hepatic Zn depletion and suppresses ethanol-elevated cytochrome P450 2E1 (CYP2E1) activity, but increases the activity of alcohol dehydrogenase in the liver; an action that is likely responsible for Zn suppression of alcohol-induced oxidative stress. Zn also enhances glutathione-related antioxidant capacity in the liver. At the cellular level, Zn inhibits alcohol-induced hepatic apoptosis partially through suppression of the Fas/FasL-mediated pathway. Zn supplementation preserves intestinal integrity and prevents endotoxemia, leading to inhibition of endotoxin-induced tumor necrosis factor-alpha (TNF-alpha) production in the liver. Zn also directly inhibits the signaling pathway involved in endotoxin-induced TNF-alpha production. These hepatic and extrahepatic effects of Zn are independent of MT. However, low levels of MT in the liver sensitize the organ to alcohol-induced injury, and elevation of MT enhances the endogenous Zn reservoir and makes Zn available when oxidative stress is imposed. Zn has a high potential to be developed as an effective agent in the prevention and treatment of ALD.     

Layered Signaling Regulatory Networks Analysis of Gene Expression Involved in Malignant Tumorigenesis of Non-Resolving Ulcerative Colitis via Integration of Cross-Study Microarray Profiles

Background

Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/Principal Findings

In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results

Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions

The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.     

Identification of Potential Pathway Mediation Targets in Toll-like Receptor Signaling

Recent advances in reconstruction and analytical methods for signaling networks have spurred the development of large-scale models that incorporate fully functional and biologically relevant features. An extended reconstruction of the human Toll-like receptor signaling network is presented herein. This reconstruction contains an extensive complement of kinases, phosphatases, and other associated proteins that mediate the signaling cascade along with a delineation of their associated chemical reactions. A computational framework based on the methods of large-scale convex analysis was developed and applied to this network to characterize input–output relationships. The input–output relationships enabled significant modularization of the network into ten pathways. The analysis identified potential candidates for inhibitory mediation of TLR signaling with respect to their specificity and potency. Subsequently, we were able to identify eight novel inhibition targets through constraint-based modeling methods. The results of this study are expected to yield meaningful avenues for further research in the task of mediating the Toll-like receptor signaling network and its effects.     

Discovering pathways by orienting edges in protein interaction networks

Modern experimental technology enables the identification of the sensory proteins that interact with the cells' environment or various pathogens. Expression and knockdown studies can determine the downstream effects of these interactions. However, when attempting to reconstruct the signaling networks and pathways between these sources and targets, one faces a substantial challenge. Although pathways are directed, high-throughput protein interaction data are undirected. In order to utilize the available data, we need methods that can orient protein interaction edges and discover high-confidence pathways that explain the observed experimental outcomes. We formalize the orientation problem in weighted protein interaction graphs as an optimization problem and present three approximation algorithms based on either weighted Boolean satisfiability solvers or probabilistic assignments. We use these algorithms to identify pathways in yeast. Our approach recovers twice as many known signaling cascades as a recent unoriented signaling pathway prediction technique and over 13 times as many as an existing network orientation algorithm. The discovered paths match several known signaling pathways and suggest new mechanisms that are not currently present in signaling databases. For some pathways, including the pheromone signaling pathway and the high-osmolarity glycerol pathway, our method suggests interesting and novel components that extend current annotations.