Multi-species network inference improves gene regulatory network reconstruction for early embryonic development in Drosophila

TL;DR

Integrating gene expression data from multiple related Drosophila species enhances the accuracy of gene regulatory network reconstruction during early embryonic development, leveraging evolutionary conservation.

Contribution

This study demonstrates that multi-species data integration improves gene regulatory network inference compared to single-species approaches.

Findings

Multi-species networks outperform single-species networks in accuracy.

Predicted networks reflect known phylogenetic relationships.

Consensus networks reduce edge discrepancies across species.

Abstract

Gene regulatory network inference uses genome-wide transcriptome measurements in response to genetic, environmental or dynamic perturbations to predict causal regulatory influences between genes. We hypothesized that evolution also acts as a suitable network perturbation and that integration of data from multiple closely related species can lead to improved reconstruction of gene regulatory networks. To test this hypothesis, we predicted networks from temporal gene expression data for 3,610 genes measured during early embryonic development in six Drosophila species and compared predicted networks to gold standard networks of ChIP-chip and ChIP-seq interactions for developmental transcription factors in five species. We found that (i) the performance of single-species networks was independent of the species where the gold standard was measured; (ii) differences between predicted networks reflected the known phylogeny and differences in biology between the species; (iii) an integrative consensus network which minimized the total number of edge gains and losses with respect to all single-species networks performed better than any individual network. Our results show that in an evolutionarily conserved system, integration of data from comparable experiments in multiple species improves the inference of gene regulatory networks. They provide a basis for future studies on the numerous multi-species gene expression datasets for other biological processes available in the literature.