Topological analysis of mass-balanced signaling networks: a framework to obtain network properties including crosstalk

Signal transduction networks have only been studied at a small scale because large-scale reconstructions and suitable in silico analysis methods have not been available. Since reconstructions of large signaling networks are progressing well there is now a need to develop a framework for analysing structural properties of signaling networks. One such framework is presented here, one that is based on systemically independent pathways and a mass-balanced representation of signaling events. This approach was applied to a prototypic signaling network and it allowed for: (1) a systemic analysis of all possible input/output relationships, (2) a quantitative evaluation of network crosstalk, or the interconnectivity of systemically independent pathways, (3) a measure of the redundancy in the signaling network, (4) the participation of reactions in signaling pathways, and (5) the calculation of correlated reaction sets. These properties emerge from network structure and can only be derived and studied within a defined mathematical framework. The calculations presented are the first of their kind for a signaling network, while similar analysis has been extensively performed for prototypic and genome-scale metabolic networks. This approach does not yet account for dynamic concentration profiles. Due to the scalability of the stoichiometric formalism used, the results presented for the prototypic signaling network can be obtained for large signaling networks once their reconstruction is completed.     

Operating regimes of signaling cycles: statics, dynamics, and noise filtering

A ubiquitous building block of signaling pathways is a cycle of covalent modification (e.g., phosphorylation and dephosphorylation in MAPK cascades). Our paper explores the kind of information processing and filtering that can be accomplished by this simple biochemical circuit. Signaling cycles are particularly known for exhibiting a highly sigmoidal (ultrasensitive) input–output characteristic in a certain steady-state regime. Here, we systematically study the cycle's steady-state behavior and its response to time-varying stimuli. We demonstrate that the cycle can actually operate in four different regimes, each with its specific input–output characteristics. These results are obtained using the total quasi–steady-state approximation, which is more generally valid than the typically used Michaelis-Menten approximation for enzymatic reactions. We invoke experimental data that suggest the possibility of signaling cycles operating in one of the new regimes. We then consider the cycle's dynamic behavior, which has so far been relatively neglected. We demonstrate that the intrinsic architecture of the cycles makes them act—in all four regimes—as tunable low-pass filters, filtering out high-frequency fluctuations or noise in signals and environmental cues. Moreover, the cutoff frequency can be adjusted by the cell. Numerical simulations show that our analytical results hold well even for noise of large amplitude. We suggest that noise filtering and tunability make signaling cycles versatile components of more elaborate cell-signaling pathways.     

Discovery of predictive rule sets for chlorophyll-a dynamics in the Nakdong River (Korea) by means of the hybrid evolutionary algorithm HEA

This paper presents a hybrid evolutionary algorithm (HEA) to discover complex rule sets predicting the concentration of chlorophyll-a (Chl.a) based on the measured meteorological, hydrological and limnological variables in the hypertrophic Nakdong River. The HEA is designed: (1) to evolve the structure of rule sets by using genetic programming and (2) to optimise the random parameters in the rule sets by means of a genetic algorithm. Time-series of input–output data from 1995 to 1998 without and with time lags up to 7 days were used for training HEA. Independent input–output data for 1994 were used for testing HEA. HEA successfully discovered rule sets for multiple nonlinear relationships between physical, chemical variables and Chl.a, which proved to be predictive for unseen data as well as explanatory. The comparison of results by HEA and previously applied recurrent artificial neural networks to the same data with input–output time lags of 3 days revealed similar good performances of both methods. The sensitivity analysis for the best performing predictive rule set unraveled relationships between seasons, specific input variables and Chl.a which to some degree correspond with known properties of the Nakdong River. The statistics of numerous random runs of the HEA also allowed determining most relevant input variables without a priori knowledge.     

Bayesian Network Reconstruction Using Systems Genetics Data: Comparison of MCMC Methods

Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis–Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data.     

N-Terminal and C-Terminal Domains of Calmodulin Mediate FADD and TRADD Interaction

FADD (Fas–associated death domain) and TRADD (Tumor Necrosis Factor Receptor 1-associated death domain) proteins are important regulators of cell fate in mammalian cells. They are both involved in death receptors mediated signaling pathways and have been linked to the Toll-like receptor family and innate immunity. Here we identify and characterize by database search analysis, mutagenesis and calmodulin (CaM) pull-down assays a calcium-dependent CaM binding site in the α-helices 1–2 of TRADD death domain. We also show that oxidation of CaM methionines drastically reduces CaM affinity for FADD and TRADD suggesting that oxidation might regulate CaM-FADD and CaM-TRADD interactions. Finally, using Met-to-Leu CaM mutants and binding assays we show that both the N- and C-terminal domains of CaM are important for binding.     

A computational framework for the topological analysis and targeted disruption of signal transduction networks

In this article, optimization-based frameworks are introduced for elucidating the input-output structure of signaling networks and for pinpointing targeted disruptions leading to the silencing of undesirable outputs in therapeutic interventions. The frameworks are demonstrated on a large-scale reconstruction of a signaling network composed of nine signaling pathways implicated in prostate cancer. The Min-Input framework is used to exhaustively identify all input-output connections implied by the signaling network structure. Results reveal that there exist two distinct types of outputs in the signaling network that either can be elicited by many different input combinations or are highly specific requiring dedicated inputs. The Min-Interference framework is next used to precisely pinpoint key disruptions that negate undesirable outputs while leaving unaffected necessary ones. In addition to identifying disruptions of terminal steps, we also identify complex disruption combinations in upstream pathways that indirectly negate the targeted output by propagating their action through the signaling cascades. By comparing the obtained disruption targets with lists of drug molecules we find that many of these targets can be acted upon by existing drug compounds, whereas the remaining ones point at so-far unexplored targets. Overall the proposed computational frameworks can help elucidate input/output relationships of signaling networks and help to guide the systematic design of interference strategies.     

Exponential Signaling Gain at the Receptor Level Enhances Signal-to-Noise Ratio in Bacterial Chemotaxis

Cellular signaling systems show astonishing precision in their response to external stimuli despite strong fluctuations in the molecular components that determine pathway activity. To control the effects of noise on signaling most efficiently, living cells employ compensatory mechanisms that reach from simple negative feedback loops to robustly designed signaling architectures. Here, we report on a novel control mechanism that allows living cells to keep precision in their signaling characteristics – stationary pathway output, response amplitude, and relaxation time – in the presence of strong intracellular perturbations. The concept relies on the surprising fact that for systems showing perfect adaptation an exponential signal amplification at the receptor level suffices to eliminate slowly varying multiplicative noise. To show this mechanism at work in living systems, we quantified the response dynamics of the E. coli chemotaxis network after genetically perturbing the information flux between upstream and downstream signaling components. We give strong evidence that this signaling system results in dynamic invariance of the activated response regulator against multiplicative intracellular noise. We further demonstrate that for environmental conditions, for which precision in chemosensing is crucial, the invariant response behavior results in highest chemotactic efficiency. Our results resolve several puzzling features of the chemotaxis pathway that are widely conserved across prokaryotes but so far could not be attributed any functional role.     

TLR ligand-induced podosome disassembly in dendritic cells is ADAM17 dependent

Toll-like receptor (TLR) signaling induces a rapid reorganization of the actin cytoskeleton in cultured mouse dendritic cells (DC), leading to enhanced antigen endocytosis and a concomitant loss of filamentous actin–rich podosomes. We show that as podosomes are lost, TLR signaling induces prominent focal contacts and a transient reduction in DC migratory capacity in vitro. We further show that podosomes in mouse DC are foci of pronounced gelatinase activity, dependent on the enzyme membrane type I matrix metalloprotease (MT1-MMP), and that DC transiently lose the ability to degrade the extracellular matrix after TLR signaling. Surprisingly, MMP inhibitors block TLR signaling–induced podosome disassembly, although stimulated endocytosis is unaffected, which demonstrates that the two phenomena are not obligatorily coupled. Podosome disassembly caused by TLR signaling occurs normally in DC lacking MT1-MMP, and instead requires the tumor necrosis factor α–converting enzyme ADAM17 (a disintegrin and metalloprotease 17), which demonstrates a novel role for this “sheddase” in regulating an actin-based structure.     

Quantitative input–output dynamics of a c-di-GMP signal transduction cascade in Vibrio cholerae

Bacterial biofilms are multicellular communities that collectively overcome environmental threats and clinical treatments. To regulate the biofilm lifecycle, bacteria commonly transduce sensory information via the second messenger molecule cyclic diguanylate (c-di-GMP). Using experimental and modeling approaches, we quantitatively capture c-di-GMP signal transmission via the bifunctional polyamine receptor NspS-MbaA, from ligand binding to output, in the pathogen Vibrio cholerae. Upon binding of norspermidine or spermidine, NspS-MbaA synthesizes or degrades c-di-GMP, respectively, which, in turn, drives alterations specifically to biofilm gene expression. A long-standing question is how output specificity is achieved via c-di-GMP, a diffusible molecule that regulates dozens of effectors. We show that NspS-MbaA signals locally to specific effectors, sensitizing V. cholerae to polyamines. However, local signaling is not required for specificity, as changes to global cytoplasmic c-di-GMP levels can selectively regulate biofilm genes. This work establishes the input–output dynamics underlying c-di-GMP signaling, which could be useful for developing bacterial manipulation strategies.

Bacteria alternate between being free-swimming and existing in biofilm communities; commonly, the molecule c-di-GMP links sensory information to changes in biofilm behavior. This study delivers a quantitative understanding of c-di-GMP signaling in the global pathogen and biofilm former, Vibrio cholerae.     

Ror2 regulates branching,differentiation, and actin-cytoskeletal dynamics within the mammary epithelium

Wnt signaling encompasses β-catenin–dependent and –independent networks. How receptor context provides Wnt specificity in vivo to assimilate multiple concurrent Wnt inputs throughout development remains unclear. Here, we identified a refined expression pattern of Wnt/receptor combinations associated with the Wnt/β-catenin–independent pathway in mammary epithelial subpopulations. Moreover, we elucidated the function of the alternative Wnt receptor Ror2 in mammary development and provided evidence for coordination of this pathway with Wnt/β-catenin–dependent signaling in the mammary epithelium. Lentiviral short hairpin RNA (shRNA)-mediated depletion of Ror2 in vivo increased branching and altered the differentiation of the mammary epithelium. Microarray analyses identified distinct gene level alterations within the epithelial compartments in the absence of Ror2, with marked changes observed in genes associated with the actin cytoskeleton. Modeling of branching morphogenesis in vitro defined specific defects in cytoskeletal dynamics accompanied by Rho pathway alterations downstream of Ror2 loss. The current study presents a model of Wnt signaling coordination in vivo and assigns an important role for Ror2 in mammary development.     

Ubiquitination of ECSIT is crucial for the activation of p65/p50 NF-κBs in Toll-like receptor 4 signaling

Recent evidence shows that evolutionarily conserved signaling intermediate in Toll pathways (ECSIT) interacts with tumor necrosis factor receptor–associated factor 6 (TRAF6), is ubiquitinated, and contributes to bactericidal activity during Toll-like receptor (TLR) signaling. Here we report a new regulatory role for ECSIT in TLR4 signaling. On TLR4 stimulation, endogenous ECSIT formed a molecular complex with p65/p50 NF-κB proteins. Our biochemical studies showed that ECSIT specifically interacted with p65/p50 NF-κB proteins, which colocalized in the nucleus. Of interest, these effects were critically dependent on ubiquitination of the ECSIT lysine (K) 372 residue. K372A mutant ECSIT did not interact with p65/p50 NF-κB proteins and markedly attenuated nuclear colocalization. In addition, ECSIT-knockdown THP-1 cells could not activate NF-κB DNA-binding activities of p65 and p50, production of proinflammatory cytokines, or NF-κB–dependent gene expression in response to TLR4 stimulation. However, these activities were markedly restored by expressing the wild-type ECSIT protein but not the K372A mutant ECSIT protein. These data strongly suggest that the ubiquitination of ECSIT might have a role in the regulation of NF-κB activity in TLR4 signaling.     

SUMOylation Is Required for Optimal TRAF3 Signaling Capacity

TNF receptor–associated factors (TRAFs) are multifunctional adaptor proteins involved in temporal and spatial coordination of signals necessary for normal immune function. Here, we report that TRAF3, a TRAF family member with a key role in Toll-like and TNF family receptor signaling and suppressor of lymphomagenesis, is post-translationally modified by the small ubiquitin-related modifier (SUMO). Through yeast two-hybrid and co-immunoprecipitation assays we have identified Ubc9, the SUMO conjugating enzyme, as a novel TRAF3-interacting protein. We show that Ubc9-dependent SUMOylation of TRAF3 modulates optimal association with the CD40 receptor, thereby influencing TRAF3 degradation and non-canonical NF-κB activation upon CD40 triggering. Collectively, our findings describe a novel post-translational modification of a TRAF family member and reveal a link between SUMOylation and TRAF-mediated signal transduction.     

The Evolution and Origin of Animal Toll-Like Receptor Signaling Pathway Revealed by Network-Level Molecular Evolutionary Analyses

Genes carry out their biological functions through pathways in complex networks consisting of many interacting molecules. Studies on the effect of network architecture on the evolution of individual proteins will provide valuable information for understanding the origin and evolution as well as functional conservation of signaling pathways. However, the relationship between the network architecture and the individual protein sequence evolution is yet little known. In current study, we carried out network-level molecular evolution analysis on TLR (Toll-like receptor ) signaling pathway, which plays an important role in innate immunity in insects and mammals, and we found that: 1) The selection constraint of genes was negatively correlated with its position along TLR signaling pathway; 2) all genes in TLR signaling pathway were highly conserved and underwent strong purifying selection; 3) the distribution of selective pressure along the pathway was driven by differential nonsynonymous substitution levels; 4) The TLR signaling pathway might present in a common ancestor of sponges and eumetazoa, and evolve via the TLR, IKK, IκB and NF-κB genes underwent duplication events as well as adaptor molecular enlargement, and gene structure and conservation motif of NF-κB genes shifted in their evolutionary history. Our results will improve our understanding on the evolutionary history of animal TLR signaling pathway as well as the relationship between the network architecture and the sequences evolution of individual protein.     

Local Network Topology in Human Protein Interaction Data Predicts Functional Association

The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-β signaling pathway (P<10⁻⁵⁰). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.     

Ultrasensitive response motifs: basic amplifiers in molecular signalling networks

Multi-component signal transduction pathways and gene regulatory circuits underpin integrated cellular responses to perturbations. A recurring set of network motifs serve as the basic building blocks of these molecular signalling networks. This review focuses on ultrasensitive response motifs (URMs) that amplify small percentage changes in the input signal into larger percentage changes in the output response. URMs generally possess a sigmoid input–output relationship that is steeper than the Michaelis–Menten type of response and is often approximated by the Hill function. Six types of URMs can be commonly found in intracellular molecular networks and each has a distinct kinetic mechanism for signal amplification. These URMs are: (i) positive cooperative binding, (ii) homo-multimerization, (iii) multistep signalling, (iv) molecular titration, (v) zero-order covalent modification cycle and (vi) positive feedback. Multiple URMs can be combined to generate highly switch-like responses. Serving as basic signal amplifiers, these URMs are essential for molecular circuits to produce complex nonlinear dynamics, including multistability, robust adaptation and oscillation. These dynamic properties are in turn responsible for higher-level cellular behaviours, such as cell fate determination, homeostasis and biological rhythm.     

Clinical significance and biological functions of chemokine CXCL3 in head and neck squamous cell carcinoma

CXCL3 plays extensive roles in tumorigenesis in various types of human cancers through its roles in tumor cell differentiation, invasion, and migration. However, the mechanisms of CXCL3 in head and neck squamous cell carcinoma (HNSCC) remain unclear. In our study, multiple databases were used to explore the expression level, prognostic value, and related mechanisms of CXCL3 in human HNSCC through bioinformatic methods. We also performed further experiments in vivo and in vitro to evaluate the expression of CXCL3 in a human head and neck tissue microarray and the underlying effect mechanisms of CXCL3 on the tumor biology of HNSCC tumor cells. The result showed that the expression level of CXCL3 in patients with HNSCC was significantly higher as compared with that in normal tissues (P<0.05). Kaplan–Meier survival analysis demonstrated that patients with high CXCL3 expression had a lower overall survival rate (P=0.038). CXCL3 was further identified as an independent prognostic factor for HNSCC patients by Cox regression analysis, and GSEA exhibited that several signaling pathways including Apoptosis, Toll-like receptor, Nod-like receptor, Jak-STAT, and MAPK signaling pathways may be involved in the tumorigenesis of HNSCC. CAL27 cells overexpressing or HNSCC cells treated with exogenous CXCL3 exhibited enhanced cell malignant behaviors, whereas down-regulating CXCL3 expression resulted in decreased malignant behaviors in HSC4 cells. In addition, CXCL3 may affect the expression of several genes, including ERK1/2, Bcl-2, Bax, STAT3, and NF-κB. In summary, our bioinformatics and experiment findings effectively suggest the information of CXCL3 expression, roles, and the potential regulatory network in HNSCC.     

May–June Maximum Temperature Reconstruction from Mean Earlywood Density in North Central China and Its Linkages to the Summer Monsoon Activities

Cores of Pinus tabulaformis from Tianshui were subjected to densitometric analysis to obtain mean earlywood density data. Climate response analysis indicates that May–June maximum temperature is the main factor limiting the mean earlywood density (EWD) of Chinese pine trees in the Shimen Mountains. Based on the EWD chronology, we have reconstructed May–June maximum temperature 1666 to 2008 for Tianshui, north central China. The reconstruction explains 40.1% of the actual temperature variance during the common period 1953–2008. The temperature reconstruction is representative of temperature conditions over a large area to the southeast and northwest of the sampling site. Preliminary analysis of links between large-scale climatic variation and the temperature reconstruction shows that there is a relationship between extremes in spring temperature and anomalous atmospheric circulation in the region. It is thus revealed that the mean earlywood density chronology of Pinus tabulaformis has enough potential to reconstruct the temperature variability further into the past.     

A Coding IRAK2 Protein Variant Compromises Toll-like receptor (TLR) Signaling and Is Associated with Colorectal Cancer Survival

Within innate immune signaling pathways, interleukin-1 receptor-associated kinases (IRAKs) fulfill key roles downstream of multiple Toll-like receptors and the interleukin-1 receptor. Although human IRAK4 deficiency was shown to lead to severe immunodeficiency in response to pyogenic bacterial infection during childhood, little is known about the role of human IRAK2. We here identified a non-synonymous IRAK2 variant, rs35060588 (coding R214G), as hypofunctional in terms of NF-κB signaling and Toll-like receptor-mediated cytokine induction. This was due to reduced ubiquitination of TRAF6, a key step in signal transduction. IRAK2 rs35060588 occurs in 3–9% of individuals in different ethnic groups, and our studies suggested a genetic association of rs35060588 with colorectal cancer survival. This for the first time implicates human IRAK2 in a human disease and highlights the R214G IRAK2 variant as a potential novel and broadly applicable biomarker for disease or as a therapeutic intervention point.