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      The Multilayer Connectome of Caenorhabditis elegans

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          Abstract

          Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function and occurs primarily outside the synaptic connectome. We have mapped the putative monoamine connections, as well as a subset of neuropeptide connections, in C. elegans based on new and published gene expression data. The monoamine and neuropeptide networks exhibit distinct topological properties, with the monoamine network displaying a highly disassortative star-like structure with a rich-club of interconnected broadcasting hubs, and the neuropeptide network showing a more recurrent, highly clustered topology. Despite the low degree of overlap between the extrasynaptic (or wireless) and synaptic (or wired) connectomes, we find highly significant multilink motifs of interaction, pinpointing locations in the network where aminergic and neuropeptide signalling modulate synaptic activity. Thus, the C. elegans connectome can be mapped as a multiplex network with synaptic, gap junction, and neuromodulator layers representing alternative modes of interaction between neurons. This provides a new topological plan for understanding how aminergic and peptidergic modulation of behaviour is achieved by specific motifs and loci of integration between hard-wired synaptic or junctional circuits and extrasynaptic signals wirelessly broadcast from a small number of modulatory neurons.

          Author Summary

          Connectomics represents an effort to map brain structure at the level of individual neurons and their synaptic connections. However, neural circuits also depend on other types of signalling between neurons, such as extrasynaptic modulation by monoamines and peptides. Here we present a draft monoamine connectome, along with a partial neuropeptide connectome, for the nematode C. elegans, based on new and published expression data for biosynthetic genes and receptors. We describe the structural properties of these "wireless" networks, including their topological features and modes of interaction with the wired synaptic and gap junction connectomes. This multilayer connectome of C. elegans can serve as a prototype for understanding the multiplex networks comprising larger nervous systems, including the human brain.

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          Most cited references87

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            Emergence of scaling in random networks

            Systems as diverse as genetic networks or the world wide web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature is found to be a consequence of the two generic mechanisms that networks expand continuously by the addition of new vertices, and new vertices attach preferentially to already well connected sites. A model based on these two ingredients reproduces the observed stationary scale-free distributions, indicating that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
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              Collective dynamics of 'small-world' networks.

              Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                16 December 2016
                December 2016
                : 12
                : 12
                : e1005283
                Affiliations
                [1 ]Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, United Kingdom
                [2 ]HHMI Janelia Research Campus, Ashburn, VA, United States of America
                [3 ]Department of Biological Sciences, Columbia University, New York, NY, United States of America
                [4 ]Department of Psychiatry, University of Cambridge, Cambridge United Kingdom
                [5 ]ImmunoPsychiatry, Alternative Discovery & Development, GlaxoSmithKline R&D, Cambridge United Kingdom
                Oxford University, UNITED KINGDOM
                Author notes

                ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline; he holds stock in GSK. The authors have declared that no competing interests exist.

                • Conceptualization: WRS PEV ETB BB.

                • Data curation: BB CLB.

                • Formal analysis: BB CLB PEV.

                • Funding acquisition: WRS ETB.

                • Investigation: RB YLC EY BB.

                • Methodology: BB PEV CLB RB.

                • Project administration: WRS PEV.

                • Resources: RB.

                • Software: BB CLB.

                • Supervision: WRS PEV ETB.

                • Validation: BB CLB EY.

                • Visualization: BB WRS CLB.

                • Writing – original draft: BB WRS.

                • Writing – review & editing: RB PEV ETB YLC.

                Author information
                http://orcid.org/0000-0002-4360-5902
                http://orcid.org/0000-0001-6078-9312
                http://orcid.org/0000-0003-1977-0761
                Article
                PCOMPBIOL-D-16-01378
                10.1371/journal.pcbi.1005283
                5215746
                27984591
                eb959832-4f55-4cff-b1f1-335a1f90ecf9
                © 2016 Bentley et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 August 2016
                : 5 December 2016
                Page count
                Figures: 7, Tables: 6, Pages: 31
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: WT103784MA
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MC-A022-5PB91
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: 5T32DK007328-37
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/K020706/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award Recipient :
                We thank the MRC (grant MC-A022-5PB91 to WRS) and Wellcome Trust (grant WT103784MA to WRS) for funding. PEV was supported by a Bioinformatics Research Fellowship from the Medical Research Council (UK) (MR/K020706/1). YLC was supported by an EMBO Long-term Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
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                Computer and Information Sciences
                Neural Networks
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                Custom metadata
                vor-update-to-uncorrected-proof
                2017-01-04
                All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
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