共查询到20条相似文献,搜索用时 15 毫秒
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Mathematical models are extensively employed to understand physicochemical processes in biological systems. In the absence of detailed mechanistic knowledge, models are often based on network inference methods, which in turn rely upon perturbations to nodes by biochemical means. We have discovered a potential pitfall of the approach underpinning such methods when applied to signaling networks. We first show experimentally, and then explain mathematically, how even in the simplest signaling systems, perturbation methods may lead to paradoxical conclusions: for any given pair of two components X and Y, and depending upon the specific intervention on Y, either an activation or a repression of X could be inferred. This effect is of a different nature from incomplete network identification due to underdetermined data and is a phenomenon intrinsic to perturbations. Our experiments are performed in an in vitro minimal system, thus isolating the effect and showing that it cannot be explained by feedbacks due to unknown intermediates. Moreover, our in vitro system utilizes proteins from a pathway in mammalian (and other eukaryotic) cells that play a central role in proliferation, gene expression, differentiation, mitosis, cell survival, and apoptosis. This pathway is the perturbation target of contemporary therapies for various types of cancers. The results presented here show that the simplistic view of intracellular signaling networks being made up of activation and repression links is seriously misleading, and call for a fundamental rethinking of signaling network analysis and inference methods. 相似文献
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Calcium ion (Ca2+) is one of the very important ubiquitous intracellular second messenger molecules involved in many signal transduction pathways in plants. The cytosolic free Ca2+ concentration ([Ca2+]cyt) have been found to increased in response to many physiological stimuli such as light, touch, pathogenic elicitor, plant hormones and abiotic stresses including high salinity, cold and drought. This Ca2+ spikes normally result from two opposing reactions, Ca2+ influx through channels or Ca2+ efflux through pumps. The removal of Ca2+ from the cytosol against its electrochemical gradient to either the apoplast or to intracellular organelles requires energized ‘active’ transport. Ca2+-ATPases and H+/Ca2+ antiporters are the key proteins catalyzing this movement. The increased level of Ca2+ is recognised by some Ca2+-sensors or calcium-binding proteins, which can activate many calcium dependent protein kinases. These kinases regulate the function of many genes including stress responsive genes, resulted in the phenotypic response of stress tolerance. Calcium signaling is also involved in the regulation of cell cycle progression in response to abiotic stress. The regulation of gene expression by cellular calcium is also crucial for plant defense against various stresses. However, the number of genes known to respond to specific transient calcium signals is limited. This review article describes several aspects of calcium signaling such as Ca2+ requiremant and its role in plants, Ca2+ transporters, Ca2+-ATPases, H+/ Ca2+-antiporter, Ca2+-signature, Ca2+-memory and various Ca2+-binding proteins (with and without EF hand).Key Words: Calcium binding proteins, Ca2+ channel, Ca2+-dependent protein kinases, Ca2+/H+ antiport, calcium memory, calcium sensors, calcium signatures, Ca2+-transporters, EF hand motifs, plant signal transduction 相似文献
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Aminoacyl-tRNA synthetases (ARSs) and ARS-interacting multifunctional proteins (AIMPs) exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM), the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN), 124 cancer-associated druggable target genes (DTGs) and 404 protein-protein interactors (PPIs) of ARSs using NCI's cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA) were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS), and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc). Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM. 相似文献
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One of the goals of systems biology is to reverse engineer in a comprehensive fashion the arrow diagrams of signal transduction systems. An important tool for ordering pathway components is genetic epistasis analysis, and here we present a strategy termed Alternative Inputs (AIs) to perform systematic epistasis analysis. An alternative input is defined as any genetic manipulation that can activate the signaling pathway instead of the natural input. We introduced the concept of an “AIs-Deletions matrix” that summarizes the outputs of all combinations of alternative inputs and deletions. We developed the theory and algorithms to construct a pairwise relationship graph from the AIs-Deletions matrix capturing both functional ordering (upstream, downstream) and logical relationships (AND, OR), and then interpreting these relationships into a standard arrow diagram. As a proof-of-principle, we applied this methodology to a subset of genes involved in yeast mating signaling. This experimental pilot study highlights the robustness of the approach and important technical challenges. In summary, this research formalizes and extends classical epistasis analysis from linear pathways to more complex networks, facilitating computational analysis and reconstruction of signaling arrow diagrams. 相似文献
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Junil Kim Drieke Vandamme Jeong-Rae Kim Amaya Garcia Munoz Walter Kolch Kwang-Hyun Cho 《PLoS computational biology》2014,10(7)
Biological systems are known to be both robust and evolvable to internal and external perturbations, but what causes these apparently contradictory properties? We used Boolean network modeling and attractor landscape analysis to investigate the evolvability and robustness of the human signaling network. Our results show that the human signaling network can be divided into an evolvable core where perturbations change the attractor landscape in state space, and a robust neighbor where perturbations have no effect on the attractor landscape. Using chemical inhibition and overexpression of nodes, we validated that perturbations affect the evolvable core more strongly than the robust neighbor. We also found that the evolvable core has a distinct network structure, which is enriched in feedback loops, and features a higher degree of scale-freeness and longer path lengths connecting the nodes. In addition, the genes with high evolvability scores are associated with evolvability-related properties such as rapid evolvability, low species broadness, and immunity whereas the genes with high robustness scores are associated with robustness-related properties such as slow evolvability, high species broadness, and oncogenes. Intriguingly, US Food and Drug Administration-approved drug targets have high evolvability scores whereas experimental drug targets have high robustness scores. 相似文献
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Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies – producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled “ENA”, accessible on CRAN (http://cran.r-project.org/web/packages/ENA/). 相似文献
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Andrea M. Pereira Cicerone Tudor Philippe-Alexandre Pouille Shashank Shekhar Johannes S. Kanger Vinod Subramaniam Enrique Martín-Blanco 《PloS one》2014,9(7)
Cells display versatile responses to mechanical inputs and recent studies have identified the mitogen-activated protein kinase (MAPK) cascades mediating the biological effects observed upon mechanical stimulation. Although, MAPK pathways can act insulated from each other, several mechanisms facilitate the crosstalk between the components of these cascades. Yet, the combinatorial complexity of potential molecular interactions between these elements have prevented the understanding of their concerted functions. To analyze the plasticity of the MAPK signaling network in response to mechanical stress we performed a non-saturating epistatic screen in resting and stretched conditions employing as readout a JNK responsive dJun-FRET biosensor. By knocking down MAPKs, and JNK pathway regulators, singly or in pairs in Drosophila S2R+ cells, we have uncovered unexpected regulatory links between JNK cascade kinases, Rho GTPases, MAPKs and the JNK phosphatase Puc. These relationships have been integrated in a system network model at equilibrium accounting for all experimentally validated interactions. This model allows predicting the global reaction of the network to its modulation in response to mechanical stress. It also highlights its context-dependent sensitivity. 相似文献
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The task of gene regulatory network reconstruction from high-throughput data is receiving increasing attention in recent years. As a consequence, many inference methods for solving this task have been proposed in the literature. It has been recently observed, however, that no single inference method performs optimally across all datasets. It has also been shown that the integration of predictions from multiple inference methods is more robust and shows high performance across diverse datasets. Inspired by this research, in this paper, we propose a machine learning solution which learns to combine predictions from multiple inference methods. While this approach adds additional complexity to the inference process, we expect it would also carry substantial benefits. These would come from the automatic adaptation to patterns on the outputs of individual inference methods, so that it is possible to identify regulatory interactions more reliably when these patterns occur. This article demonstrates the benefits (in terms of accuracy of the reconstructed networks) of the proposed method, which exploits an iterative, semi-supervised ensemble-based algorithm. The algorithm learns to combine the interactions predicted by many different inference methods in the multi-view learning setting. The empirical evaluation of the proposed algorithm on a prokaryotic model organism (E. coli) and on a eukaryotic model organism (S. cerevisiae) clearly shows improved performance over the state of the art methods. The results indicate that gene regulatory network reconstruction for the real datasets is more difficult for S. cerevisiae than for E. coli. The software, all the datasets used in the experiments and all the results are available for download at the following link: http://figshare.com/articles/Semi_supervised_Multi_View_Learning_for_Gene_Network_Reconstruction/1604827. 相似文献
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