Aggregated gene co-expression networks for predicting transcription factor regulatory landscapes in a non-model plant species

Gene co-expression networks (GCNs) have not been extensively studied in non-model plants. However, the rapid accumulation of transcriptome datasets in these species represents an opportunity to explore underutilized network aggregation approaches that highlight robust co-expression interactions and improve functional connectivity. We applied and evaluated two different aggregation methods on public grapevine RNA- Seq datasets belonging to three different tissue conditions (leaf, berry and ‘all organs’). Our results show that co-occurrence-based aggregation generally yielded the best-performing networks. We applied GCNs to study several TF gene families, showing its capacity of detecting both already-described and novel regulatory relationships between R2R3-MYBs, bHLH/MYC and multiple secondary metabolism pathway reactions. Specifically, TF 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 and a GCN application within the Vitviz platform, a user-friendly tool for exploring co-expression relationships. ### Competing Interest Statement The authors have declared no competing interest. * GCN : Gene co-expression network. This term describes co-expression networks built at genomic scale. Therefore, they contain co-expression information between all the genes described in the genome. Aggregated network : GCN built using network co-occurrence aggregation. Briefly, this aggregation method consists on generating one network per SRA study, and counting across how many SRA studies the same co-expressions are detected. Single network : GCN built using data aggregation. Briefly, this aggregation method consists on merging the information contained on each SRA study in a unique count matrix and generating a network from the merge matrix count. Gene-centered network : List of the 420 genes (roughly 1% of the genes described in the PN40024 assembly) being most strongly co-expressed with a gene of interest. Pathway-centered network : Graphical representation of co-expressions between genes involved in a metabolic pathway and any other gene, including the transcription factors potentially involved in the regulation of that pathway. Interaction plot : Interactive representation of a pathway-centered network.