Imaging and Modeling Growth and plasticity in plant Ovules – IMAGO
The clonal propagation of seeds without sexual reproduction (apomixis) bears enormous promises for sustainable agriculture and food security. Introducing apomixis to agronomically important plants could revolutionize breeding practices by enabling fixation of hybrid traits-of-interest including heterosis. Apomixis occurs naturally in many plant species but not in major crop plants, hence motivating intense research efforts to understand the underlying mechanisms. Apomixis is an alteration of sexual reproduction at different steps. The first deviation occurs at the somatic-to-reproductive transition, establishing germ cell initials or SMC (Spore Mother Cells). SMC differentiation is contemporary to the growth and patterning of the ovule primordium.
Recent findings from the partners’ groups suggest that disturbances in cell growth and division during ovule morphogenesis are associated with apomictic-like ovule development. Yet, the precise relation of early ovule morphogenesis with SMC fate and its plasticity between apomictic and sexual reproductive development has not been explored so far. This question can now be addressed thanks to the advent of non-invasive, high-resolution 3D imaging and image processing techniques, combined with functional approaches relying on genetic and toxicological perturbations. In addition, with its relatively simple structure, consisting of only ~100 cells at maturity, the ovule primordium offers an attractive system to create digital tissue models capturing informative patterning rules.
By combining imaging and modeling approaches, IMAGO aims at elucidating the relation of ovule primordium architecture to SMC fate. In this regards, IMAGO is contributing to ongoing, successful international efforts aiming at modeling plant growth control. Furthermore, it offers a methodologically and biologically timely contribution to the long-term goal of translating apomixis in crops. We use Arabidopsis, as a primary model to establish experimental and conceptual baselines. Next, we validate the robustness of our findings in maize, a major sexually reproducing crop, and in Paspalum, an apomictic grass with quantitative aposporous phenotypes.
We formulate three objectives to:
1) Define 3D growth patterns in the ovule primordium using high-resolution, 3D imaging of cellular markers, and identify quantitative correlations between growth parameters and SMC fate.
2) Elucidate key patterning rules predicting SMC differentiation in relation to ovule primordium architecture. For this, 2D and 3D digital tissue models of ovule growth are generated at cellular resolution. 2D models dynamically test candidate growth parameters (based on 1), their interactions and hierarchy. The 3D model focuses on testing the influence of the apical dome’s topology.
3) Validate these rules in vivo following three complementary approaches: (i) analysis of known developmental mutants whose ovules display ectopic SMCs, or, conversely, that fail differentiating any SMC; (ii) evaluation of perturbations of growth and division on SMC fate using genetic, pharmacological approaches, and (iii) validate the robustness of the conceptual model by analyzing ovule development in sexual and apomictic grasses (Maize and Paspalum, respectively).
IMAGO is based on a multidisciplinary collaboration between experts in plant reproductive development, image processing and computational modeling.
Madame Daphné AUTRAN (INSTITUT de RECHERCHE POUR LE DEVELOPEMENT / UMR DIADE)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
IRD / UMR DIADE INSTITUT de RECHERCHE POUR LE DEVELOPEMENT / UMR DIADE
UZH UNIVERSITY of ZURICH / Department of Plant and Microbial Biology
INRIA Institut National de Recherche en Informatique et Automatique
ENS Lyon / RDP Ecole Nationale Supérieure de Lyon - Laboratoire RDP
Help of the ANR 243,594 euros
Beginning and duration of the scientific project: January 2017 - 36 Months