4.6 Article

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

Journal

PLOS COMPUTATIONAL BIOLOGY
Volume 16, Issue 12, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1007974

Keywords

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Funding

  1. Hungarian National Research, Development and Innovation Office [K131458, K116525]
  2. Higher Education Institutional Excellence Programme of the Ministry for Innovation and Technology in Hungary within the framework of the Molecular Biology thematic programme of the Semmelweis University
  3. Thematic Excellence Programme (Temateruleti Kivalosagi Program) of the Ministry for Innovation and Technology in Hungary (Nemzeti Kutatasi, Fejlesztesies Innovacios Alap) [2020-4.1.1.-TKP2020]
  4. Semmelweis University
  5. Human Capacities Grant Management Office in Hungary(Emberi Eroforrasok Miniszteriuma) [NTPNFTO-18-B-0179, NTP-NFTO-19-B-0264]
  6. Semmelweis University [EFOP-3.6.3VEKOP-16-2017-00009]

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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. Author summary The fundamental way neurons communicate is by activating or inhibiting each other via synapses. The balance between the two is crucial for the optimal functioning of a nervous system. However, whole-brain synaptic polarity information is unavailable for any species and experimental validation is challenging. The roundworm Caenorhabditis elegans possesses a fully mapped connectome with an emerging gene expression profile of its 302 neurons. Based on the consideration that the polarity of a synapse can be determined by the neurotransmitter(s) expressed in the presynaptic neuron and the receptors expressed in the postsynaptic neuron, we conceptualized and created a tool that predicts synaptic polarities based on connectivity and gene expression information. Using currently available datasets we propose for the first time that the ratio of excitatory and inhibitory synapses in a partial connectome of C. elegans is around 4 to 1 which is in line with the balance observed in many natural systems. Our method opens a way to include spatial and temporal dynamics of synaptic polarity that would add a new dimension of plasticity in the excitatory:inhibitory balance. Our tool is freely available to be used on any network accompanied by any expression atlas.

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