Doctorate thesis of Montpellier University

Tuesday december 13 at 14h pm, Amphi Philippe Lamour

Statistical inference of the gene regulatory networks in Arabidopsis thaliana under rising atmospheric CO2 levels

Doctoral school : GAIA – Biodiversité, Agriculture, Alimentation, Environnement, Terre, Eau
Spéciality : BIDAP – Biologie, Interactions, Diversité Adaptative des Plantes
University : Université Montpellier
Reasearch unit : IPSiM –  Institut for Plant Sciences of Montpellier

Team: Sirene

Jury :

Andréa RAU, Chargée de recherche, INRAE Jouy en Josas                          Rapportrice
Étienne DELANNOY, Chargé de recherche, INRAE Paris Saclay                    Rapporteur
Céline MASCLAUX-DAUBRESSE, Directrice de recherche, INRAE Versailles-Grignon  Examinatrice
Nathalie VIALANEIX, Directrice de recherche, INRAE Toulouse                  Examinatrice
Philippe NACRY, Directeur de recherche, INRAE Montpellier                     Examinateur
Antoine MARTIN, Chargé de recherche, CNRS Montpellier Directeur de thèse
Sophie LÈBRE, Maître de conférences, IMAG – Université Paul Valéry Montpellier 3 Co-Encadrante
Vincent SEGURA, Chargé de recherche, INRAE Montpellier                            Invité

Abstract:

Human activity is causing an elevation of CO2 levels in the atmosphere, that are expected to rise from 420 ppm to approximately 1000 ppm by the end of the century. C3 plants, a major part of cultivated crops, are particularly affected by the rise of CO2 levels. Even though a stimulation of biomass production is expected under elevated CO2 (eCO2), this gain is met with a marked depletion of the plant mineral composition and an especially strong decline in nitrogen (N) content. This poses a major threat to crop quality and human nutrition, that we propose to start addressing through systems biology approaches. Promising hypotheses to explain this decline invoke a disruption of signalling pathways associated to N uptake and assimilation, motivating the investigation at the genomic scale of gene expression reprogramming in the roots of the model plant Arabidopsis thaliana under eCO2. To uncover the unknown regulators orchestrating such networks, we developed statistical methods for Gene Regulatory Network (GRN) inference. Modelling transcriptional dependencies from gene expression data can be performed by regression-based techniques, a challenging task hindered by high dimension and the scarcity of ground truth networks. We propose two novel approaches : (i) an extension of a Random Forest-based method, GENIE3, via permutation procedures assessing the significance of regulatory interactions that we include within a complete suite for GRN inference, and (ii) two integrative GRN inference methods based on sparse linear regression with stability selection and Random Forests, integrating Transcription Factor Binding Sites (TFBSs) with gene expression. We benchmark those methods against experimental gold standards, and show that they improve the biological relevance of inferred GRNs in Arabidopsis thaliana. We applied the first inference approach to a combinatorial transcriptomic dataset of root tissues under contrasted CO2 levels and nutritional conditions, and the second to the roots of plants exposed to a gradient of CO2 concentrations. The inferred GRNs provided candidate genes for the control of this response, and we demonstrate that some of them regulate growth stimulation under eCO2 without penalizing shoot nutrient content. Overall, our results indicate that key nitrate and iron nutrition genes and their known regulators are misregulated by rising CO2, and that pathways associated to high affinity nitrate transport systems are especially unfavorably altered. The last objective of this work was to leverage natural genetic variability to identify genes controlling the ionome response to eCO2. We confirmed a mineral content decline in three populations of Arabidopsis at different geographic scales, and showed that the variability in this response can be explained by genetic determinants in the world-wide panel via linear mixed models. We put forward another set of candidate genes, highly associated to iron, N and zinc depletion in the shoots under eCO2 that pave the way for designing plants with sustainable nutritional value for the near future.