Dissecting gene regulatory networks in Cichorium endivia leaves: fundamental biology in leafy crops

Cichorium endivia is a leafy crop belonging to the Cichorieae tribe of the Compositae/Asteraceae which leaves represent an excellent source of health benefiting plant nutrients and are widely consumed as fresh, minimally processed and cooked food. Similar to lettuce, their leaves display a wide range of morphology diversity, ranging from smooth (escaroles) to extremely curly leaves (endives). Due to the striking morphological diversity of the leaves in escarole and endive, C. endivia represents a good model to study the relationship between leaf shape and function. We used the transcriptome of C. endivia (cv 'Domari'), previously obtained in our lab by de novo assembly, to construct a bona fide leaf database of transcription factors (TFs), and RNA-seq data from leaves of field-grown plants of four commercial cultivars (two curled-leaved endives and two broad-leaved escaroles escaroles) to explore the regulatory networks acting in leaves. We used k-means cluster analysis to find transcriptional patterns in the data on the basis of similarity or distance, and found eight major expression clusters of TF genes in C. endivia leaves, two of which highly anticorrelated. To provide a global overview of co-expression patterns of the C. endivia TF genes, we used a total of 23 RNA-seq samples and pairwise correlation analysis to construct a targeted Gene Co-expression Network (GCN), which topological indexes such as node degree, closeness centrality and betwenness centrality were analyzed to identify key components (hubs) in the network. We then used a guide-gene approach to challenge the obtained TFs expression clusters and GCN with specific functional modules to assess how these modules enter into the GCN networks and relate to the TF clusters identified. This allowed us to predict the regulatory networks controlling photosynthesis and senescence in C. endivia leaves and genetic hubs that are at the boundaries between shape and function.