Réseaux de gènes et éléments régulateurs impliqués dans la différenciation des tissus racinaires chez le riz – GREENER

A specific tissue transcriptome analysis for cortex tissues has been performed using Laser Capture Microdissection (LCM) technology to retrieve mRNAs, create and sequence RNAseq libraries before the beginning of the project. We have recently developed this technology and obtained mRNAs in large quantities and of optimal quality. Three developmental time points has been analyzed, meristematic, first sign of cortex differentiation, first sign of aerenchyma formation. Data will been curated with massive transcriptome data available in public databases to build high quality expression data needed to feed gene network reconstruction (WP1) A gene network will be reconstructed using curated data (WP1) and a database containing all these data, accessible through intuitive and dynamic query interfaces (WP1), will support the identification of the TFs that regulate the genes involved cortex formation and aerenchyma formation. These TF will be candidate TFs for the identity and differentiation of cortex tissue based on their influence (WP1). An interactive data visualization interface will help the biologist analyze and contrast different states of the network throughout the simulation experiments using Hive Plots to identify the most pertinent gene network potentially involved in aerenchyma formation.
The most promising candidate TFs issued from the WP1 simulation experiments, will then be validated alone, or in combination, in protoplasts (WP3), using a gene-network manipulation tool that allows an arbitrary number of genes (WP2) to be simultaneously overexpressed and under expressed. We recently demonstrated the functionality of the system using fluorescent reporter promoter (T. Mounier et al unpublished data). The validation will involve either the transcriptional demonstration of a direct regulation by one or more TFs on effectors genes, by Droplet RT-PCR, or by using molecular markers (vital dyes, anti-wall antibodies) for cortex differentiation. The validation or invalidation results will be re-injected into the gene network database in order to improve progressively the gene network. Finally, the function of 2-3 TF among the most interesting ones, will be analyzed in planta using CRISPR and dynamic multiphoton imaging technologies available from partner 3 in parallel to follow defects at cellular and tissue levels.
The proposed approach is innovative, with an original methodology to identify transcription factors through an inverse genetics approach, by developing a dynamic approach for modeling gene networks that allows the use of validation data in real time to improve this network, and finally by developing a fast, universal and high throughput system for validating and manipulating networks. Demonstration of the efficiency of this approach will come from the identification of new key factors for cortex and aerenchyma formation and differentiation in rice.

By comparing the influence of transcription factors between all conditions, two sub-networks specifically active in the cortex during its development were identified, sub-networks C2.1 and C2.2 which comprise 10 and 6 interconnected TFs respectively. Among the C2.1 network, two particularly interesting candidates. LOL1 is a regulator of cell death and a recent paper suggests that a loss of function mutation in this gene affects aerenchyma formation. The other candidat is an AP2 transcription factor, that play a key role in the response to anoxia.

CRISPR/KO mutants (two different crispr for each target) for all C2.1 pathway genes were generated, plants regenerated and mutants identified. For the two most interesting mutants, we segregated CAS9 to obtain homozygous stabilized mutants without transgene. We also developed and tested two different systems to phenotype the corresponding mutants. A miniaturized system (one week old seedling) and a system on adult roots (3 weeks). Two conditions, with an oxygenated medium and a deoxygenated medium (removal of dissolved oxygen using nitrogen flow and 0.1% dissolved agar). Controls were contrasting rice varieties and the reference variety used for mutant production (Kitaake). Vibratome sections were used to validate this system. The indicative conditions induce an earlier and more important formation of aerenchyma. The corresponding mutants will be analyzed using these two systems.

User requirements have been gathered and structured in the form of 6 user stories. A web application was developed to represent the cooperativity network and FT activity using different representation modes that can be interchanged according to the user's needs: Force-directed node-link diagrams (classical visualization), adjancency matrix, arc and radial plots. The matrix representation mode allows to easily group and represent TF clusters that have similar behaviors (modules) and to identify TFs that connect different clusters. These TFs are potentially Hubs that control the transition between two states (two stages). The data obtained in Task 1.1 also allowed us to demonstrate important changes in whole parts of cooperativity networks between two conditions. These hubs, identifiable thanks to the matrix views, are therefore interesting candidates that were not identifiable with a classical gene network representation diagram.

We will Extract candidate FTs that are not in the cooperativity network and from the gene network (the reconstructed networks are bipartite), identify FTs that control genes that have a predicted function in cell death. Then finalize the set of key regulators of cortical differentiation.

Data mining visualization tools allowing comparison between conditions and more uniquely, for each condition, using hive-plot will be developed to discover hub specific to transition stage from division to cell death initiation zone. These tools will facilitate the machine-user interaction by offering possibilities of modulation of the visualization outputs by playing on the influence, the level of connectivity of the transcription factors while offering the possibility of comparing the conditions (tissues/stages) between them.

The gene-network manipulation tool that allows an arbitrary number of genes (WP2) to be simultaneously overexpressed and under expressed is ready and will be tested to define the two most efficient combinations (activator/guide and rectifier/guide combination) and then validated in protoplast using an endogenous network.

Last, mutants for members of the C2.1 network will be analyzed for their root phenotype, in particular for differences in the level of aerenchyma formed, under normoxic or anoxic conditions. Further functional analysis (RNAscope expression profile, overexpressor etc...) will be initiated based on these phenotyping results.

Up to now, several papers are in preparation:
A first one to illustrate the visualisation tools developed for datamining of gene network reconstruction data and their comparison between developmental step.

A second one, illustrating the power of gene network reconstruction to identify key hub for aerenchyma formation is also underway.

Les inondations sont responsables d'environ 60 % de tous les dommages agricoles et des pertes de récoltes (FAO, 2015). La perspective du réchauffement climatique mondial affectera les zones cultivées et les conditions de croissance des cultures, ce qui aura des répercussions sur l'avenir de la sécurité alimentaire dans le monde. L'identification des gènes contrôlant la tolérance aux inondations est l'une des clés du développement de variétés de céréales tolérantes à la submersion, un problème qui touche un nombre croissant de zones cultivées dans le monde. Le riz présente des aérenchymes, c'est-à-dire des cavités remplies d'air qui relient les parties aériennes et les racines, leur permettant de maintenir leur respiration et leur croissance dans des conditions de submersion. La formation des aérenchymes est une réponse adaptative , mais le réseau de gènes contrôlant la formation des aérenchymes et du cortex est encore inconnu. L'objectif principal du projet est d'identifier et de caractériser les facteurs de transcription impliqués dans la différenciation des tissus du cortex et de l'aérenchyme chez le riz en utilisant une approche de biologie systémique. Ces travaux permettront d'identifier les gènes clés à l'origine de l'aérenchyme et de la tolérance aux inondations chez le riz. Ces gènes seront de futurs candidats pour la sélection de cultivars de tolérance aux inondations chez le riz mais aussi chez les céréales.