Exploring Biological Networks with Cytoscape Software

Yeung, Natalie; Cline, Melissa; Kuchinsky, Allan; Smoot, Michael; Bader, Gary D.

doi:10.1002/0471250953.bi0813s23

Cited by 67 publications

(55 citation statements)

References 22 publications

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“…The structural similarity of the compounds was based on Estate Bit fingerprint descriptors (Hall and Kier, 1995). To construct a network similarity graph, a Tanimoto similarity cutoff of 0.7 was used for edge creation (with 0 representing dissimilar compounds and 1 representing identical compounds) and visualized using Cytoscape version 2.8.2 (Yeung et al, 2002). Data from three treatment concentrations, 10, 15, and 30 mM, were mapped, and a pie chart was painted at each node.…”

Section: Structural Models For Lipid-inducing Compoundsmentioning

confidence: 99%

Identification and Metabolite Profiling of Chemical Activators of Lipid Accumulation in Green Algae

Wase

Allen

et al. 2017

Plant Physiol.

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Microalgae are proposed as feedstock organisms useful for producing biofuels and coproducts. However, several limitations must be overcome before algae-based production is economically feasible. Among these is the ability to induce lipid accumulation and storage without affecting biomass yield. To overcome this barrier, a chemical genetics approach was employed in which 43,783 compounds were screened against Chlamydomonas reinhardtii, and 243 compounds were identified that increase triacylglyceride (TAG) accumulation without terminating growth. Identified compounds were classified by structural similarity, and 15 were selected for secondary analyses addressing impacts on growth fitness, photosynthetic pigments, and total cellular protein and starch concentrations. TAG accumulation was verified using gas chromatographymass spectrometry quantification of total fatty acids, and targeted TAG and galactolipid measurements were performed using liquid chromatography-multiple reaction monitoring/mass spectrometry. These results demonstrated that TAG accumulation does not necessarily proceed at the expense of galactolipid. Untargeted metabolite profiling provided important insights into pathway shifts due to five different compound treatments and verified the anabolic state of the cells with regard to the oxidative pentose phosphate pathway, Calvin cycle, tricarboxylic acid cycle, and amino acid biosynthetic pathways. Metabolite patterns were distinct from nitrogen starvation and other abiotic stresses commonly used to induce oil accumulation in algae. The efficacy of these compounds also was demonstrated in three other algal species. These lipid-inducing compounds offer a valuable set of tools for delving into the biochemical mechanisms of lipid accumulation in algae and a direct means to improve algal oil content independent of the severe growth limitations associated with nutrient deprivation.

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Section: Structural Models For Lipid-inducing Compoundsmentioning

confidence: 99%

Identification and Metabolite Profiling of Chemical Activators of Lipid Accumulation in Green Algae

Wase

Allen

et al. 2017

Plant Physiol.

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“…Protein lists were imported into PINA2 for analysis (31,52). Protein-protein interactions were uploaded from PINA2 into Cytoscape (53,54) to visualize network topology. Values of degree (D) and centrality (betweenness centrality, C B ; closeness centrality, C C ; and degree centrality, C D ) were extracted using Cytoscape's integrated network analysis module.…”

Section: Methodsmentioning

confidence: 99%

Reversal of dendritic phenotypes in 16p11.2 microduplication mouse model neurons by pharmacological targeting of a network hub

Blizinsky

Díaz-Castro

Forrest

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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The architecture of dendritic arbors contributes to neuronal connectivity in the brain. Conversely, abnormalities in dendrites have been reported in multiple mental disorders and are thought to contribute to pathogenesis. Rare copy number variations (CNVs) are genetic alterations that are associated with a wide range of mental disorders and are highly penetrant. The 16p11.2 microduplication is one of the CNVs most strongly associated with schizophrenia and autism, spanning multiple genes possibly involved in synaptic neurotransmission. However, disease-relevant cellular phenotypes of 16p11.2 microduplication and the driver gene(s) remain to be identified. We found increased dendritic arborization in isolated cortical pyramidal neurons from a mouse model of 16p11.2 duplication (dp/+). Network analysis identified MAPK3, which encodes ERK1 MAP kinase, as the most topologically important hub in protein-protein interaction networks within the 16p11.2 region and broader gene networks of schizophrenia-associated CNVs. Pharmacological targeting of ERK reversed dendritic alterations associated with dp/+ neurons, outlining a strategy for the analysis and reversal of cellular phenotypes in CNV-related psychiatric disorders.T he architecture of the dendritic arbors defines a pyramidal neuron's dendritic receptive field (1). Moreover, patterns of dendritic arborization are essential to the computational ability of the neuron (1). Generating and maintaining proper dendritic receptive fields is therefore crucial for neural circuit function that underlies complex behaviors. Conversely, alterations in dendrites occur in psychiatric disorders, including schizophrenia and autism spectrum disorder (ASD) (2).Psychiatric disorders have complex genetic architecture that is partly explained by rare variants with high penetrance (3, 4). Though incidences of these rare mutations are low (4-7), the accrued burden of rare variants on disease risk may account for a significant proportion of cases in linked disorders (8). The most well-characterized forms of rare variations are large genomic regional duplications or deletions known as copy number variations (CNVs). CNVs represent large genomic alterations, often encompassing multiple genes, which confer significant risk (odds ratio = 3-30) (9). The increased rare CNV burden is associated with numerous neurodevelopmental psychiatric conditions, including schizophrenia, ASD, and intellectual disability (5, 7, 10, 11). However, due to the large number of genes within CNVs, understanding the relationship between genotype and phenotypes and the rational identification of potential targets for reversing pathologically relevant phenotypes in CNV disorders has been challenging.Recently, much attention has been paid to recurrent microduplication or deletion at the 16p11.2 locus. The ∼600-kb 16p11.2 CNV region encompasses 29 known protein-coding genes and is a hot spot for chromosomal rearrangement (3); 16p11.2 CNVs have been linked to multiple disorders and phenotypes; CNVs of this region are associa...

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“…Graphml files (the main file exchange format used by the yEd editor) are supported by other network programs such as NodeXL http://www.codeplex.com/NodeXL, Sonivis http://www.sonivis.org/, GUESS http://guess.wikispot.org/GraphML, Pajek http://pajek.imfm.si/doku.php and NetworkX http://networkx.lanl.gov/ and the use of standard shaped nodes (glyphs) means that other generic network analysis tools such as Cytoscape [44] could also be used to draw mEPN diagrams. In particular we have been developing mEPN's compatibility with BioLayout Express 3D , a network analysis tool developed by us for the visualization and analysis networks derived from 'omics data [37,38].…”

Section: Discussionmentioning

confidence: 99%

The mEPN scheme: an intuitive and flexible graphical system for rendering biological pathways

et al. 2010

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BackgroundThere is general agreement amongst biologists about the need for good pathway diagrams and a need to formalize the way biological pathways are depicted. However, implementing and agreeing how best to do this is currently the subject of some debate.ResultsThe modified Edinburgh Pathway Notation (mEPN) scheme is founded on a notation system originally devised a number of years ago and through use has now been refined extensively. This process has been primarily driven by the author's attempts to produce process diagrams for a diverse range of biological pathways, particularly with respect to immune signaling in mammals. Here we provide a specification of the mEPN notation, its symbols, rules for its use and a comparison to the proposed Systems Biology Graphical Notation (SBGN) scheme.ConclusionsWe hope this work will contribute to the on-going community effort to develop a standard for depicting pathways and will provide a coherent guide to those planning to construct pathway diagrams of their biological systems of interest.

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Exploring Biological Networks with Cytoscape Software

Cited by 67 publications

References 22 publications

Identification and Metabolite Profiling of Chemical Activators of Lipid Accumulation in Green Algae

Identification and Metabolite Profiling of Chemical Activators of Lipid Accumulation in Green Algae

Reversal of dendritic phenotypes in 16p11.2 microduplication mouse model neurons by pharmacological targeting of a network hub

The mEPN scheme: an intuitive and flexible graphical system for rendering biological pathways

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