Aggregated gene co-expression networks predict transcription factor regulatory landscapes in grapevine.
J Exp Bot
; 74(21): 6522-6540, 2023 11 21.
Article
em En
| MEDLINE
| ID: mdl-37668374
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Fatores de Transcrição
/
Redes Reguladoras de Genes
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article