GREENER: Gene and Regulatory Elements Networks Involved in Rice Cortex and aerenchyma differentiation

The mechanisms of formation of aerenchyma are not known, in particular genes involved in its initiation. Their identification would make it possible to understand how this adaptive mechanism to submergence, present in many flowering plant species, is implemented. This would also open up the possibility of developing new submergence-tolerant cereal species. Rice is the perfect model to identify these genes and mechanisms that are naturally present in this species.

Date de début de projet

01/02/2021

Date de fin du projet

28/02/2024

Objectives

The main objective of the project is to identify and characterize transcription factors involved in root cortex and aerenchyma formation in rice using a systems biology approach.

Description

The mechanisms of formation of aerenchyma are not known, in particular genes involved in its initiation. Their identification would make it possible to understand how this adaptive mechanism to submergence, present in many flowering plant species, is implemented. This would also open up the possibility of developing new submergence-tolerant cereal species. Rice is the perfect model to identify these genes and mechanisms that are naturally present in this species. Functional analysis, by genome editing, is routinely performed; The formation of aerenchyma results probably from the cooperation between many regulators (TF, kinases, peptides) and effectors (enzymes, structural proteins) that are specifically expressed in the cortex. This complexity makes it difficult to identify by a simple transcriptomic approach the key regulator(s) in the formation of these aerenchyma as it is the gene network as a whole that is responsible for cortex transition to aerenchyma and not just a single gene justifying the development of an innovative system biology approach.

WP1: Machine learning, Visual Analytics and Network Bioscience Automation

General objectives: we propose to rationalize “root regulatory network biology” through a computational cycle composed of three building “blocks”: the inference (data-learn), interrogation (visual analytics-simulation) and intervention (design-test) with regulatory networks. Network inference: to learn hypotheses (i.e., networks) from observations (i.e., data). Network interrogation: to visualize and analyze the most important patterns in terms of structure and the multi-dimensional (tabular) experimental data associated to network elements, and to make predictions about the behavior of targeted system. Network intervention: design regulatory circuits and select functional experiments.

WP2: Engineering and deciphering transcriptional programs using CAS9 technologies

General objectives: Validate and deciphering candidates TF for root cell differentiation using protoplast, CAS9 derived technology and functional analysis of the most promising candidate in planta.  A synthetic gene network control system derived from CAS9 will be developed. This technology is based on the use of one or more scaffold RNAs, a dCas9 and binding-effector modules. TF candidates from WP1 will be tested for their ability alone or in combination to induce cellular differentiation of each tissue, by analyzing by ddRTPCR the induced or suppressed primary targets and these data will be added to the learning algorithms and through interactive annotation tools that have been developed (WP1). We will analyze the in planta function of 2-3 TF candidates, involved in the differentiation of the aerenchyma. Loss of function lines will be generated by CRISPR/CAS9, Over-expressor lines and promoter lines: GFP and promoter lines: TF: GPF will also be established to confirm the expression profile and mode of action of the non-cell-autonomous and cell-autonomous TF.

The set of tools, Network Bioscience Automation and synthetic control of gene networks will be sufficiently generic to be used to explore other gene networks in all model and cultivated plants, for example to explore and validate new metabolic pathways.

Partnership

University Lille (Professeur M. Elati) UMR Canther 9020 CNRS/UMR 1277 INSERM

Le List (luxembourg) Doctor M Ghoniem (Luxembourg Institute of Science and Technology) THE INTERACTIVE VISUALIZATION GROUP TEAM

Team

Développement adaptatif du riz et du Sorgho (DARS)

Fundings

ANR PRCI France-Luxembourg ;

requested funding 781k€

Rice, aerenchyma, cortex, flodding tolerance, cereals, system biology, gene network inference, CRISPR/CAS9, Machine learning, Visualy analytics