细胞信号网络功能鲁棒的简并性拓扑特征

细胞信号网络对于外界环境的干扰表现出优良的鲁棒性,但是其维持功能鲁棒的内在机制尚未明确,本文研究了细胞信号网络功能鲁棒性的拓扑特征。选择布尔网络模型模拟细胞网络的动态行为,利用网络节点状态的扰动模拟外界环境干扰。基于演化策略探寻不同网络拓扑的功能并分析其在干扰环境下的鲁棒性,采用埃德尔曼提出的基于信息论的计算方法评估网络拓扑的简并度、冗余度和复杂度等拓扑属性,对比分析它们与功能鲁棒度的相关性及作用机理。结果显示,在网络模型的演化过程中,其拓扑简并度与功能鲁棒度显著正相关,相关性水平高于拓扑冗余度与鲁棒度的相关性。并且,随着鲁棒度的提升,网络的节点数和复杂度也随之升高,同样简并度与网络的节点数和复杂度的相关性高于拓扑冗余度与网络的节点数和复杂度的相关性。这说明增加的网络节点以简并的方式同时提高了网络拓扑的鲁棒度和复杂度。因此,细胞网络功能鲁棒性的拓扑特征是简并而不是冗余,简并为解决生物系统的复杂问题提供了有效手段,为人工系统的可靠性设计提供有益的借鉴。     

Boolean dynamics of Kauffman models with a scale-free network

We studied the Boolean dynamics of the "quenched" Kauffman models with a directed scale-free network, comparing with that of the original directed random Kauffman networks and that of the directed exponential-fluctuation networks. We have numerically investigated the distributions of the state cycle lengths and its changes as the network size N and the average degree k of nodes increase. In the relatively small network (N approximately 150), the median, the mean value and the standard deviation grow exponentially with N in the directed scale-free and the directed exponential-fluctuation networks with k=2, where the function forms of the distributions are given as an almost exponential. We have found that for the relatively large N approximately 10(3) the growth of the median of the distribution over the attractor lengths asymptotically changes from algebraic type to exponential one as the average degree k goes to k=2. The result supports the existence of the transition at k(c)=2 derived in the annealed model.     

Inferring Boolean network states from partial information

Networks of molecular interactions regulate key processes in living cells. Therefore, understanding their functionality is a high priority in advancing biological knowledge. Boolean networks are often used to describe cellular networks mathematically and are fitted to experimental datasets. The fitting often results in ambiguities since the interpretation of the measurements is not straightforward and since the data contain noise. In order to facilitate a more reliable mapping between datasets and Boolean networks, we develop an algorithm that infers network trajectories from a dataset distorted by noise. We analyze our algorithm theoretically and demonstrate its accuracy using simulation and microarray expression data.     

Learning restricted Boolean network model by time-series data

Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{e}}$, the normalized Hamming distance of state transition ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{st}}$, and the steady-state distribution distance μ^ssd. Results show that the proposed algorithm outperforms the others according to both ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{e}}$ and ${\mathit{\mu }}_{\mathrm{ham}}^{\mathrm{st}}$, whereas its performance according to μ^ssd is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.     

The signaling pathway of NADPH oxidase and its role in glomerular diseases

Abstract

Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (Nox), a major source of reactive oxygen species, is a critical mediator of redox signaling. It is well-documented that oxidative stress is associated with the development of glomerular diseases (GN). Hence, the Nox was also thought to be involved in the pathogenesis of GN. However, the expression of Nox in various GN was not consistent, the mechanisms by which the activity of the Nox enzymes in regulating renal cells remains unclear. Signaling pathways might be very important in the pathogenesis of GN. We performed this review to provide a relatively complete signaling pathways flowchart for Nox to the investigators who were interested in the role of Nox in the pathogenesis of GN. Here, we reviewed the signal transduction pathway of Nox and its role in the pathogenesis of GN.     

Neutral space analysis for a Boolean network model of the fission yeast cell cycle network

Background

Interactions between genes and their products give rise to complex circuits known as gene regulatory networks (GRN) that enable cells to process information and respond to external stimuli. Several important processes for life, depend of an accurate and context-specific regulation of gene expression, such as the cell cycle, which can be analyzed through its GRN, where deregulation can lead to cancer in animals or a directed regulation could be applied for biotechnological processes using yeast. An approach to study the robustness of GRN is through the neutral space. In this paper, we explore the neutral space of a Schizosaccharomyces pombe (fission yeast) cell cycle network through an evolution strategy to generate a neutral graph, composed of Boolean regulatory networks that share the same state sequences of the fission yeast cell cycle.

Results

Through simulations it was found that in the generated neutral graph, the functional networks that are not in the wildtype connected component have in general a Hamming distance more than 3 with the wildtype, and more than 10 between the other disconnected functional networks. Significant differences were found between the functional networks in the connected component of the wildtype network and the rest of the network, not only at a topological level, but also at the state space level, where significant differences in the distribution of the basin of attraction for the G₁ fixed point was found for deterministic updating schemes.

Conclusions

In general, functional networks in the wildtype network connected component, can mutate up to no more than 3 times, then they reach a point of no return where the networks leave the connected component of the wildtype. The proposed method to construct a neutral graph is general and can be used to explore the neutral space of other biologically interesting networks, and also formulate new biological hypotheses studying the functional networks in the wildtype network connected component.     

The signaling pathway of hypoxia inducible factor and its role in renal diseases

Abstract

It is well-documented that hypoxia inducible factor (HIF) is a key mediator of tissue and cellular adaptation to hypoxia. HIF-target genes are also involved in cellular apoptosis and profibrotic mechanisms. The role of HIF in diseases is not consistent. It is a risk factor for tumor progression, whereas it plays a protective role against ischemic hypofusion. For renal diseases, it is not always a risk or protective factor. Many factors are involved in the pathogenesis of renal diseases. It is reported that HIF not only increases hypoxia tolerance, but also regulates a lot of signaling pathways. In the past decades, a number of studies were also conducted to explore the association between HIF and the risk of renal diseases. However, the role of HIF in the development of renal diseases was not entirely clear. In this study, the signal transduction pathways of HIF and its role in the pathogenesis of renal diseases were reviewed.     

The network map of urotensin-II mediated signaling pathway in physiological and pathological conditions

Urotensin-II is a polypeptide ligand with neurohormone-like activity. It mediates downstream signaling pathways through G-protein-coupled receptor 14 (GPR14) also known as urotensin receptor (UTR). Urotensin-II is the most potent endogenous vasoconstrictor in mammals, promoting cardiovascular remodelling, cardiac fibrosis, and cardiomyocyte hypertrophy. It is also involved in other physiological and pathological activities, including neurosecretory effects, insulin resistance, atherosclerosis, kidney disease, and carcinogenic effects. Moreover, it is a notable player in the process of inflammatory injury, which leads to the development of inflammatory diseases. Urotensin-II/UTR expression stimulates the accumulation of monocytes and macrophages, which promote the adhesion molecules expression, chemokines activation and release of inflammatory cytokines at inflammatory injury sites. Therefore, urotensin-II turns out to be an important therapeutic target for the treatment options and management of associated diseases. The main downstream signaling pathways mediated through this urotensin-II /UTR system are RhoA/ROCK, MAPKs and PI3K/AKT. Due to the importance of urotensin-II systems in biomedicine, we consolidated a network map of urotensin-II /UTR signaling. The described signaling map comprises 33 activation/inhibition events, 31 catalysis events, 15 molecular associations, 40 gene regulation events, 60 types of protein expression, and 11 protein translocation events. The urotensin-II signaling pathway map is made freely accessible through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway:WP5158). The availability of comprehensive urotensin-II signaling in the public resource will help understand the regulation and function of this pathway in normal and pathological conditions. We believe this resource will provide a platform to the scientific community in facilitating the identification of novel therapeutic drug targets for diseases associated with urotensin-II signaling.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-022-00672-4.     

The interplay of Klotho with signaling pathway and microRNAs in cancers

Klotho (KL) gene has been accepted as an "aging suppressor" gene that encodes a single transmembrane protein in human known as Klotho which is commonly expressed in renal tubes. The interruption in the secretion of Klotho protein expedites aging whereas its high expression extends lifespan. The family of Klotho proteins has been reported to act as distinct receptors for endocrine fibroblast growth factors (FGFs), which manage multifarious metabolic processes. Further, the secreted Klotho is a hormonal factor that takes part in the ion channel organization. Numerous studies determined that this protein affects the function of a number of important signaling pathways, which may present an impact in tumorigenesis via the coordination of receptors located on them. This review article focuses on the effects of microRNAs on the performance of Klotho and how the interplay between Klotho and certain pathways like insulin-like growth factor, FGF, Wnt, and transforming growth factor β contribute to the biogenesis of cancer. The present study is also pointed at defining the molecular mechanisms of these interactions.     

The glucose signaling network in yeast

Background

Most cells possess a sophisticated mechanism for sensing glucose and responding to it appropriately. Glucose sensing and signaling in the budding yeast Saccharomyces cerevisiae represent an important paradigm for understanding how extracellular signals lead to changes in the gene expression program in eukaryotes.

Scope of review

This review focuses on the yeast glucose sensing and signaling pathways that operate in a highly regulated and cooperative manner to bring about glucose-induction of HXT gene expression.

Major conclusions

The yeast cells possess a family of glucose transporters (HXTs), with different kinetic properties. They employ three major glucose signaling pathways—Rgt2/Snf3, AMPK, and cAMP-PKA—to express only those transporters best suited for the amounts of glucose available. We discuss the current understanding of how these pathways are integrated into a regulatory network to ensure efficient uptake and utilization of glucose.

General significance

Elucidating the role of multiple glucose signals and pathways involved in glucose uptake and metabolism in yeast may reveal the molecular basis of glucose homeostasis in humans, especially under pathological conditions, such as hyperglycemia in diabetics and the elevated rate of glycolysis observed in many solid tumors.     

Sampling-rate-dependent probabilistic Boolean networks

External control of a genetic regulatory network is used for the purpose of avoiding undesirable states, such as those associated with a disease. To date, intervention has mainly focused on the external control of probabilistic Boolean networks via the associated discrete-time discrete-space Markov processes. Implementation of an intervention policy derived for probabilistic Boolean networks requires nearly continuous observation of the underlying biological system since precise application requires the observation of all transitions. In medical applications, as in many engineering problems, the process is sampled at discrete time intervals and a decision to intervene or not must be made at each sample point. In this work, sampling-rate-dependent probabilistic Boolean network is proposed as an extension of probabilistic Boolean network. The proposed framework is capable of capturing the sampling rate of the underlying system.     

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.     

Attractor analysis of asynchronous Boolean models of signal transduction networks

Prior work on the dynamics of Boolean networks, including analysis of the state space attractors and the basin of attraction of each attractor, has mainly focused on synchronous update of the nodes’ states. Although the simplicity of synchronous updating makes it very attractive, it fails to take into account the variety of time scales associated with different types of biological processes. Several different asynchronous update methods have been proposed to overcome this limitation, but there have not been any systematic comparisons of the dynamic behaviors displayed by the same system under different update methods. Here we fill this gap by combining theoretical analysis such as solution of scalar equations and Markov chain techniques, as well as numerical simulations to carry out a thorough comparative study on the dynamic behavior of a previously proposed Boolean model of a signal transduction network in plants. Prior evidence suggests that this network admits oscillations, but it is not known whether these oscillations are sustained. We perform an attractor analysis of this system using synchronous and three different asynchronous updating schemes both in the case of the unperturbed (wild-type) and perturbed (node-disrupted) systems. This analysis reveals that while the wild-type system possesses an update-independent fixed point, any oscillations eventually disappear unless strict constraints regarding the timing of certain processes and the initial state of the system are satisfied. Interestingly, in the case of disruption of a particular node all models lead to an extended attractor. Overall, our work provides a roadmap on how Boolean network modeling can be used as a predictive tool to uncover the dynamic patterns of a biological system under various internal and environmental perturbations.     

The network map of Elabela signaling pathway in physiological and pathological conditions

Elabela (ELA; also called Apela and Toddler) is one of the recently discovered ligand among the two endogenous peptide ligands (Apelin and Elabela) of the apelin receptor (APLNR, also known as APJ). Elabela-induced signaling plays a crucial role in diverse biological processes, including formation of the embryonic cardiovascular system and early placental development by reducing the chances of occurrence of preeclampsia during pregnancy. It also plays the major role in the renoprotection by reducing kidney injury and the inflammatory response and regulation of gene expression associated with heart failure and fibrosis. Elabela may be processed into different active peptides, each of which binds to APLNR and predominantly activates the signals through PI3K/AKT pathway. Owing to its biomedical importance, we developed a consolidated signaling map of Elabela, in accordance with the NetPath criteria. The presented Elabela signaling map comprises 12 activation/inhibition events, 15 catalysis events, 1 molecular association, 34 gene regulation events and 32 protein expression events. The Elabela signaling pathway map is freely made available through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway:WP5100).Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00640-4.     

Augmented sparse reconstruction of protein signaling networks

The problem of reconstructing and identifying intracellular protein signaling and biochemical networks is of critical importance in biology. We propose a mathematical approach called augmented sparse reconstruction for the identification of links among nodes of ordinary differential equation (ODE) networks, given a small set of observed trajectories with various initial conditions. As a test case, the method is applied to the epidermal growth factor receptor (EGFR) driven signaling cascade, a well-studied and clinically important signaling network. Our method builds a system of representation from a collection of trajectory integrals, selectively attenuating blocks of terms in the representation. The system of representation is then augmented with random vectors, and l₁ minimization is used to find sparse representations for the dynamical interactions of each node. After showing the performance of our method on a model of the EGFR protein network, we sketch briefly the potential future therapeutic applications of this approach.     

A comprehensive network map of IL-17A signaling pathway

Interleukin-17A (IL-17A) is one of the member of IL-17 family consisting of other five members (IL-17B to IL-17F). The Gamma delta (γδ) T cells and T helper 17 (Th17) cells are the major producers of IL-17A. Aberrant signaling by IL-17A has been implicated in the pathogenesis of several autoimmune diseases including idiopathic pulmonary fibrosis, acute lung injury, chronic airway diseases, and cancer. Activation of the IL-17A/IL-17 receptor A (IL-17RA) system regulates phosphoinositide 3-kinase/AKT serine/threonine kinase/mammalian target of rapamycin (PI3K/AKT/mTOR), mitogen-activated protein kinases (MAPKs) and activation of nuclear factor-κB (NF-κB) mediated signaling pathways. The IL-17RA activation orchestrates multiple downstream signaling cascades resulting in the release of pro-inflammatory cytokines such as interleukins (IL)-1β, IL-6, and IL-8, chemokines (C-X-C motif) and promotes neutrophil-mediated immune response. Considering the biomedical importance of IL-17A, we developed a pathway resource of signaling events mediated by IL-17A/IL-17RA in this study. The curation of literature data pertaining to the IL-17A system was performed manually by the NetPath criteria. Using data mined from the published literature, we describe an integrated pathway reaction map of IL-17A/IL-17RA consisting of 114 proteins and 68 reactions. That includes detailed information on IL-17A/IL-17RA mediated signaling events of 9 activation/inhibition events, 17 catalysis events, 3 molecular association events, 68 gene regulation events, 109 protein expression events, and 6 protein translocation events. The IL-17A signaling pathway map data is made freely accessible through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway: WP5242).Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-022-00686-y.