Aggregated gene co-expression networks predict transcription factor regulatory landscapes in grapevine

Abstract Gene co-expression networks (GCNs) have not been extensively studied in non-model plants. However, the rapid accumulation of transcriptome datasets in certain species represents an opportunity to explore underutilized network aggregation approaches. In fact, aggregated GCNs (aggGCNs) highlight robust co-expression interactions and improve functional connectivity. We applied and evaluated two different aggregation methods on public grapevine RNA-Seq datasets from three different tissues (leaf, berry, and ‘all organs’). Our results show that co-occurrence-based aggregation generally yielded the best-performing networks. We applied aggGCNs to study several transcription factor gene families, showing their capacity for detecting both already-described and novel regulatory relationships between R2R3-MYBs, bHLH/MYC, and multiple specialized metabolic pathways. Specifically, transcription factor gene- and pathway-centered network analyses successfully ascertained the previously established role of VviMYBPA1 in controlling the accumulation of proanthocyanidins while providing insights into its novel role as a regulator of p-coumaroyl-CoA biosynthesis as well as the shikimate and aromatic amino acid pathways. This network was validated using DNA affinity purification sequencing data, demonstrating that co-expression networks of transcriptional activators can serve as a proxy of gene regulatory networks. This study presents an open repository to reproduce networks in other crops and a GCN application within the Vitviz platform, a user-friendly tool for exploring co-expression relationships.