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

TL;DR

This study predicts the polarity of chemical synapses in C. elegans using gene expression data, revealing an excitatory-inhibitory ratio similar to real-world networks, and provides an open-source prediction tool.

Contribution

It introduces a novel gene expression-based method for predicting synaptic polarity in C. elegans connectome, filling a data gap in connection sign annotation.

Findings

Predicted polarity for over two-thirds of synapses.

Found an excitatory-inhibitory ratio of approximately 4:1.

Developed an open-source tool for polarity prediction.

Abstract

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.