Prioritizing Maize Metabolic Gene Regulators through Multi-Omic Network Integration.
bioRxiv
; 2024 Jul 24.
Article
em En
| MEDLINE
| ID: mdl-38464086
ABSTRACT
Elucidating gene regulatory networks is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. Here, we integrated 46 co-expression networks, 283 protein-DNA interaction (PDI) assays, and 16 million SNPs used to identify expression quantitative trait loci (eQTL) to construct TF-target networks. In total, we analyzed â¼4.6M interactions to generate four distinct types of TF-target networks co-expression, PDI, trans -eQTL, and cis -eQTL combined with PDIs. To functionally annotate TFs based on their target genes, we implemented three different network integration strategies. We evaluated the effectiveness of each strategy through TF loss-of function mutant inspection and random network analyses. The multi-network integration allowed us to identify transcriptional regulators of several biological processes. Using the topological properties of the fully integrated network, we identified potential functionally redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
BioRxiv
Ano de publicação:
2024
Tipo de documento:
Article