Abiotic stresses are the main limitation to plant growth and yield. Drought stress (water deficit) is probably the most important of these and the most widely encountered by plants, either in the field or in nature. It remains a major goal for the beginning of the 21st century to better understand the regulation of stress responses and how this relates to plant growth.
There is tremendous variation for stress response among Arabidopsis accessions found in the wild ("ecotypes"), and part of this variation actually corresponds to adaptation to specific environmental conditions. The difficulty is that -in most populations- these traits segregate continuously as quantitative traits and cannot be easily targeted with classical genetics' tools, simply because they are controlled by many genetic factors. Using RIL (Recombinant Inbred Line) sets developped from crosses between well-chosen accessions, we are locating the genes that control these quantitative traits through QTL (Quantitative Trait Loci) mapping. Then, using essentially a positional cloning approach (aided by candidate gene information along the way), we are cloning the genes that explain these QTL, revealing the molecular basis of quantitative variation. To help in the candidate gene approach, we are also studying mutants in genes of unknown function that have been detected in silico as responsive to drought stress.
To understand the responses to drought stress, we are investing in high-throughput phenotyping displays for the following quantitative traits
This film shows the movement of the plants on our 'Phenoscope' (below), 4 times faster than reality.
Real-speed and high definition movie is here (70 Mo).
- Measuring shoot growth in vivo under different water deficit treatments.
Either to confirm QTL effects detected in vitro (below) or to screen for variation specific to in vivo conditions (plants grown on real soil) among accessions, RILs or mutants, we have developped our own high-throughput phenotyping platform (Phenoscope) that can handle and treat hundreds of plants simultaneously in controlled conditions. We now have 2 robots like the one shown below, capable of moving ~1,500 individual plants throughout the growth chamber following a 4-hours cycle, while automatically adjusting soil water content (according to different established treatment programs) and taking pictures of the plants for further automatic leaf area analysis.
- Measuring shoot growth in vitro under different osmotic conditions.
Shoot growth is an obvious quantitative trait integrating many sources of variation to determine the size of the photosynthetic organ.
We have established in vitro protocols to study the effect of osmotic stress treatments (applied with mannitol or PEG) on leaf expansion at the very young stage (10 days-old plants). Plants are growing under precisely controlled plant densities and environmental conditions until we scan the plates and measure individual plants leaf area using a series of macros under Optimas image analysis software.
As illustrated below, we find tremendous variation among accessions and RILs for shoot growth and shoot growth response to osmotic stress. Currently, we are cloning several QTL for these traits originating from different RIL populations.
- Measuring root elongation in vitro under different osmotic conditions.
Root system development is an important factor to explain plant tolerance to drought stress. Lateral root initiation and elongation are both highly controlled steps, especially in response to the water status of the soil and the plant.
Again, we are working in vitro on young plants (scanned after 10 days of vertical growth) to measure the effects of osmotic stress (mannitol) on root architecture. Most interesting traits for us are the number of lateral roots and total length of the lateral root system, but also primary root length, branching density, and root hair elongation.
As illustrated below, accessions themselves vary greatly in the reduction of their root growth in response to osmotic stress. We also observe intensive transgression in many RIL sets, allowing us to detect QTL with large-enough effects to be followed at the fine-mapping stage.