Synaptic polarity and sign-balance prediction using gene expression data in the Caenorhabditis elegans chemical synapse neuronal connectome network.

Autor: Fenyves BG; Department of Molecular Biology, Semmelweis University, Budapest, Hungary.; Department of Emergency Medicine, Semmelweis University, Budapest, Hungary., Szilágyi GS; Department of Molecular Biology, Semmelweis University, Budapest, Hungary., Vassy Z; Department of Molecular Biology, Semmelweis University, Budapest, Hungary., Sőti C; Department of Molecular Biology, Semmelweis University, Budapest, Hungary., Csermely P; Department of Molecular Biology, Semmelweis University, Budapest, Hungary.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2020 Dec 21; Vol. 16 (12), pp. e1007974. Date of Electronic Publication: 2020 Dec 21 (Print Publication: 2020).
DOI: 10.1371/journal.pcbi.1007974
Abstrakt: Graph theoretical analyses of nervous systems usually omit the aspect of connection polarity, due to data insufficiency. The chemical synapse network of Caenorhabditis elegans is a well-reconstructed directed network, but the signs of its connections are yet to be elucidated. Here, we present the gene expression-based sign prediction of the ionotropic chemical synapse connectome of C. elegans (3,638 connections and 20,589 synapses total), incorporating available presynaptic neurotransmitter and postsynaptic receptor gene expression data for three major neurotransmitter systems. We made predictions for more than two-thirds of these chemical synapses and observed an excitatory-inhibitory (E:I) ratio close to 4:1 which was found similar to that observed in many real-world networks. Our open source tool (http://EleganSign.linkgroup.hu) is simple but efficient in predicting polarities by integrating neuronal connectome and gene expression data.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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