Research & Projects
Genetic origins of the variability in water status maintenance / (an)isohydry
Maintaining adequate tissue water content through efficient controls of water supplies and losses is a key requirement for crop performance and plant survival in dry environments. However, while a wide inter- and intra-specific variability had been reported for these traits, their genetic links remained highly debated. Our results on a progeny of 200 grapevine genotypes showed that the variability in the stomatal pores closure (to limit water losses at the leaf surface) only plays a partial role in the genetic variations of water status maintenance. We showed that the latter is also linked to the genetic control of the soil-to-leaf water supply, and revealed a new and important role for the stress hormone abscicic acid in these genetic variations (Coupel-Ledru et al., J Exp Bot 2014; Coupel-Ledru et al., Plant Physiol 2017).
Role of night-time transpiration in the variations of water-use efficiency
Breeding crops with more biomass produced per drop of water transpired is a key challenge in the context of climate change. However, the tight coupling between transpiration and carbon assimilation during the day makes it challenging to decrease water loss without altering photosynthesis and reducing crop yield. We tested an alternative hypothesis, i.e. whether reducing transpiration at night when photosynthesis is inactive could substantially reduce water loss without altering growth. By combining physiological measurements on thousands of grapevine plants and QTL detection, we showed that night-time transpiration is under a strong genetic control depending both on stomatal closure and cuticular losses, and is partly uncoupled from daytime transpiration. We thus identified genomic regions where selection could be operated to reduce transpiration at night and maintain growth (Coupel-Ledru et al., PNAS 2016). These results paved the way to breeding crops with this underexploited trait for higher water-use efficiency.
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Picture (c) F Pantin
Multi-scale high-throughput phenotyping in the orchard combined with GWAS to explore the genetic determinants of water use, light interception and tree architecture
In trees in particular, the complexity of the canopy (vigour, self-shading, etc.) drives local variations of the microclimate and local acclimation processes leading to a strong variability in intra-plant photosynthesis and water losses. Although such variations have been reported, it has never been explored to which extent the genetic control of vegetative architecture may impact overall tree responses to water deficit. We demonstrated the feasibility of combining innovative high-throughput phenotyping (T-LiDAR, airborne imagery) and GWAS and thus unravelled a major bottleneck for phenotyping thousands of adult trees in field conditions (Coupel-Ledru et al., Horticulture Research 2019). We explored the genetic links between vegetative architecture, light interception and canopy water use and showed that multiple allelic combinations exist for all studied traits within this collection (Coupel-Ledru et al., New Phytologist 2022). This opens promising avenues to jointly optimize tree architecture, light interception and water use in breeding strategies. Genotypes carrying favourable alleles depending on environmental scenarios and production objectives could thus be targeted.
How do atmospheric CO2 elevation and drought interact to ultimately impact on plant growth and water use?
Whilst research has led an understanding of plant responses to each environmental cue separately (drought, temperature, CO2), much less is understood about the effects of their interaction on plant physiology and development. This lack of insight results in uncertainties in predictions of the impact of climate change on agricultural production. Exposure of C3 plants to elevated CO2 alters the balance between carbon gain through greater photosynthesis and leaf water loss through lower stomatal conductance, which has been predicted to ameliorate the impacts of
drought. However, longer-term responses to CO2 may result in greater leaf growth, hence greater water use and increased risk of drought stress. The likelihood of positive or negative outcomes will most probably depend on the timing and intensity of drought during the development of the crop.
In the STOCOdrought project (EPPN2020), we explore how plant responses to long-term exposure to elevated CO2 are altered by
different scenarios of drought.
Leaf burning in a grapevine diversity panel exposed to a record heatwave:
a strong genetic control opens promising breeding avenue
Extreme events associated with climate change increasingly threaten agriculture. Experimenting on a grapevine diversity panel suddenly exposed to a record heatwave in South France, we observed varietal responses ranging from complete tolerance to severe burning. We uncovered a handful of genomic regions associated with extreme heat tolerance, showing that we may leverage genetic diversity for breeding perennial fruit crops capable of withstanding heatwaves (Coupel-Ledru et al., 2024 New Phytologist). We are now investigating the physiological bases of this genetic variability (Laurine Chir's PhD project).
Phenotyping water/carbon related traits in to screen variations across hundreds of varieties in diverse environments
Phenotyping hundreds of genotypes in the vineyard is a key requirement to studying the genetic variability and determinants of water and carbon trade-offs and their response to climate change conditions. However, this is often hampered because conventional methods for measuring water and carbon related traits are typically expensive, destructive, and not usable at high-throughput. To unravel this bottleneck, we develop and test new high-throughput phenotyping methods based on the use of NIRS (Near InfraRed Spectroscopy) and leaf chlorophyll fluorescence. In this view, we have deployed intensive measurements within a panel of 279 varieties in the frame of the ANR G²WAS. Our strategy aims at combining conventional low-throughput measurements of traits such as photosynthesis, stomatal conductance and carbohydrates content, with fast leaf measurements using poro/fluorimetry and NIRS, then use these datasets to build cutting-edge statistical models to predict the traits of interest from NIRS and poro/fluorimetry, based on Partial Least Squares regressions (Eva Coindre's PhD project).