Increased signaling entropy in cancer requires the scale-free property of proteininteraction networks

Teschendorff, Andrew E.; Banerji, Christopher R. S.; Severini, Simone; Kühn, Reimer; Sollich, Peter

doi:10.1038/srep09646

Cited by 50 publications

(61 citation statements)

References 40 publications

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“…Recent work has described cellular heterogeneity as network entropy — applied as a measure of signalling pathway promiscuity — and established that the level of network entropy provides an estimate of developmental potential 160,197 . In other words, the high entropy of a heterogeneous pluripotent stem cell population maintains a diverse range of pathways associated with more mature phenotypes in a poised state for activation.…”

Section: Epigenetic Stochasticitymentioning

confidence: 99%

Epigenetic modulators, modifiers and mediators in cancer aetiology and progression

2016

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This year is the tenth anniversary of the publication in this journal of a model suggesting the existence of ‘tumour progenitor genes’. These genes are epigenetically disrupted at the earliest stages of malignancies, even before mutations, and thus cause altered differentiation throughout tumour evolution. The past decade of discovery in cancer epigenetics has revealed a number of similarities between cancer genes and stem cell reprogramming genes, widespread mutations in epigenetic regulators, and the part played by chromatin structure in cellular plasticity in both development and cancer. In the light of these discoveries, we suggest here a framework for cancer epigenetics involving three types of genes: ‘epigenetic mediators’, corresponding to the tumour progenitor genes suggested earlier; ‘epigenetic modifiers’ of the mediators, which are frequently mutated in cancer; and ‘epigenetic modulators’ upstream of the modifiers, which are responsive to changes in the cellular environment and often linked to the nuclear architecture. We suggest that this classification is helpful in framing new diagnostic and therapeutic approaches to cancer.

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Section: Epigenetic Stochasticitymentioning

confidence: 99%

Epigenetic modulators, modifiers and mediators in cancer aetiology and progression

2016

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“…The highly connected nodes are called hubs, and networks following a power-law degree distribution are often called scale-free networks ( Figure ID). Cellular networks, genetic regulatory networks, and protein-protein interaction networks are biological examples of scale-free networks [54,55]. S-metric developed by Li and colleagues [56] is a useful method for explaining the differences between networks that have identical degree sequence, especially if they are scaling (i.e[ 4 _ T D $ D I F F ] ., there are bivariate relationships of power-law types, by which one attribute relates to another attribute raised to a power, called power-law or scaling exponent).…”

Section: Box 1 Network Typesmentioning

confidence: 99%

Disentangling Interactions in the Microbiome: A Network Perspective

Layeghifard

Hwang

Guttman

2017

Trends in Microbiology

588

431

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Microbiota are now widely recognized as being central players in the health of all organisms and ecosystems, and subsequently have been the subject of intense study. However, analyzing and converting microbiome data into meaningful biological insights remain very challenging. In this review, we highlight recent advances in network theory and their applicability to microbiome research. We discuss emerging graph theoretical concepts and approaches used in other research disciplines and demonstrate how they are well suited for enhancing our understanding of the higher-order interactions that occur within microbiomes. Network-based analytical approaches have the potential to help disentangle complex polymicrobial and microbe-host interactions, and thereby further the applicability of microbiome research to personalized medicine, public health, environmental and industrial applications, and agriculture.

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“…Metabolic networks in all organisms have been suggested to be scale-free networks [18], and scale-free network phenomena have been observed in many empirical studies [31–33]. Scale-free networks are extremely heterogeneous, and their topology being dominated by a few highly connected nodes that link the rest of the less connected nodes to the system [9].…”

Section: Discussionmentioning

confidence: 99%

Large-scale gene co-expression network as a source of functional annotation for cattle genes

et al. 2016

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BackgroundGenome sequencing and subsequent gene annotation of genomes has led to the elucidation of many genes, but in vertebrates the actual number of protein coding genes are very consistent across species (~20,000). Seven years after sequencing the cattle genome, there are still genes that have limited annotation and the function of many genes are still not understood, or partly understood at best. Based on the assumption that genes with similar patterns of expression across a vast array of tissues and experimental conditions are likely to encode proteins with related functions or participate within a given pathway, we constructed a genome-wide Cattle Gene Co-expression Network (CGCN) using 72 microarray datasets that contained a total of 1470 Affymetrix Genechip Bovine Genome Arrays that were retrieved from either NCBI GEO or EBI ArrayExpress.ResultsThe total of 16,607 probe sets, which represented 11,397 genes, with unique Entrez ID were consolidated into 32 co-expression modules that contained between 29 and 2569 probe sets. All of the identified modules showed strong functional enrichment for gene ontology (GO) terms and Reactome pathways. For example, modules with important biological functions such as response to virus, response to bacteria, energy metabolism, cell signaling and cell cycle have been identified. Moreover, gene co-expression networks using “guilt-by-association” principle have been used to predict the potential function of 132 genes with no functional annotation. Four unknown Hub genes were identified in modules highly enriched for GO terms related to leukocyte activation (LOC509513), RNA processing (LOC100848208), nucleic acid metabolic process (LOC100850151) and organic-acid metabolic process (MGC137211). Such highly connected genes should be investigated more closely as they likely to have key regulatory roles.ConclusionsWe have demonstrated that the CGCN and its corresponding regulons provides rich information for experimental biologists to design experiments, interpret experimental results, and develop novel hypothesis on gene function in this poorly annotated genome. The network is publicly accessible at http://www.animalgenome.org/cgi-bin/host/reecylab/d.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3176-2) contains supplementary material, which is available to authorized users.

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Increased signaling entropy in cancer requires the scale-free property of proteininteraction networks

Cited by 50 publications

References 40 publications

Epigenetic modulators, modifiers and mediators in cancer aetiology and progression

Epigenetic modulators, modifiers and mediators in cancer aetiology and progression

Disentangling Interactions in the Microbiome: A Network Perspective

Large-scale gene co-expression network as a source of functional annotation for cattle genes

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