Abstract
Representing large biological data as networks is becoming increasingly adopted for predicting gene function while elucidating the multifaceted organization of life processes. In grapevine (Vitis vinifera L.), network analyses have been mostly adopted to contribute to the understanding of the regulatory mechanisms that control berry composition. Whereas, some studies have used gene co-expression networks to find common pathways and putative targets for transcription factors related to development and metabolism, others have defined networks of primary and secondary metabolites for characterizing the main metabolic differences between cultivars throughout fruit ripening. Lately, proteomic-related networks and those integrating genome-wide analyses of promoter regulatory elements have also been generated. The integration of all these data in multilayered networks allows building complex maps of molecular regulation and interaction. This perspective article describes the currently available network data and related resources for grapevine. With the aim of illustrating data integration approaches into network construction and analysis in grapevine, we searched for berry-specific regulators of the phenylpropanoid pathway. We generated a composite network consisting of overlaying maps of co-expression between structural and transcription factor genes, integrated with the presence of promoter cis-binding elements, microRNAs, and long non-coding RNAs (lncRNA). This approach revealed new uncharacterized transcription factors together with several microRNAs potentially regulating different steps of the phenylpropanoid pathway, and one particular lncRNA compromising the expression of nine stilbene synthase (STS) genes located in chromosome 10. Application of network-based approaches into multi-omics data will continue providing supplementary resources to address important questions regarding grapevine fruit quality and composition.
Highlights
Specialty section: This article was submitted to Technical Advances in Plant Science, a section of the journal Frontiers in Plant Science
This approach revealed new uncharacterized transcription factors together with several microRNAs potentially regulating different steps of the phenylpropanoid pathway, and one particular long non-coding RNAs (lncRNA) compromising the expression of nine stilbene synthase (STS) genes located in chromosome 10
Genomic data generated from genome sequencing projects are commonly used to ascribe molecular function and biological processes based on sequence similarity, while transcriptomics and metabolomics data typically provide a global “snapshot” of gene expression and metabolite dynamics in various biological contexts
Summary
Identification of primary metabolic switches during berry development to suggest timing of regulation in carbohydrate metabolism. Several studies have focused on condition-independent GCNs (encompassing different cultivars, tissues, developmental stages, stress and vineyard management treatments) as it provides a more convenient and representative (albeit “static”) relationship overview (Table 1) This approach has been useful for ascribing the most representative biological functions of the 134 grapevine R2R3-MYB transcription factors based on their top 100 co-expressed genes (Wong et al, 2016), where VviMYB13 (close homolog of VviMYB14 and VviMYB15) was identified as an additional STILBENE SYNTHASE regulator acting in a tissue- and/or stress-dynamic manner. MiRNA and siRNA-mediated gene regulatory networks have been constructed from high-throughput small RNA and degradome sequencing and computational target prediction methods (Zhang et al, 2012; Belli Kullan et al., 2015) These networks (not relying in abundance or expression levels) revealed novel modules such as miR156/miR172 regulatory circuits and VviTAS3/4 regulatory cascades, which are implicated in regulating plant growth and development and in the control of flavonoid biosynthesis, respectively. Cluster I contained genes mainly involved in the regulation of anthocyanin accumulation such as five flavonoid-3′,5′-hydroxylases (F3′5′H), two anthocyanin-o-methyltransferases (AOMT1-2), the UDP-
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