Transcription factors and hormonal regulatory networks in root nodule symbiosis: a systems biology approach

Substantial efforts are being made to generate large "omics" data sets, which provides a great opportunity to unravel the biological mechanisms of complex traits using bioinformatics and machine learning tools. In this context, networks have rapidly become an attractive approach to manage, display and contextualize these large data sets in order to obtain a system level and molecular understanding of biological key processes [1]. As a proof of concept, we used a "targeted" bioinformatics approach, which has been developed in our lab [2], to reanalyze classical transcriptomics experiments of rhizobia-induced nodule development in Medicago truncatula. This "targeted" knowledge-based approach involves k-means clustering of data, followed by construction and analysis of Gene Co-expression Network (GCN), based on a priori selection of genes or modules, which act as "seeds" or "baits" to identify novel components using the so called "guilt by association" principle. Starting from k-means clustering of all genes encoding transcription factors (TF), we used different specific modules (e.g. nodulation stage-specific DEG, hormonal pathways, known nodule regulatory pathways) to challenge the TF regulatory networks and identify known and novel relationships amongst specific TF and functional gene modules. We also applied the SWItchMiner (SWIM) software [3], that identifies the so-called "switch genes" with a crucial topological role in the network, to the selected transcriptomics experiments to evaluate the capacity of this computational tool to correctly identify the main hub genes acting in nodule legume rhizobial symbiosis and nodule development. References 1. Serin EA, Nijveen H, Hilhorst HW, Ligterink W (2016) Front. Plant Sci., 7, 444. 2. Testone G, Baldoni E, Iannelli MA, Nicolodi C, Di Giacomo E, Pietrini F, Mele G, Giannino D, Frugis G (2019) Plants (Basel),12, E531. 3. Paci P, Colombo T, Fiscon G, Gurtner A, Pavesi G, Farina L (2017) Sci Rep., 7, 44797