2/26/2014
Challenges to construct a sunflower ideotype with improved
performance in water-limiting conditions
What physiological processes are affected during
drought stress in the field?
What genetic network controls those physiological
processes?
Development of a biomarker for plant water status
What is the natural variation available for breeding in
these networks and what can evolution teach us?
How does it relate to oil yield?
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
Field transcriptomics
Field transcriptomics
Starting from the field ground truth
Starting from the field ground truth
Pools
Objectives
Identify drought-regulated genes in a field environment
in a reference hybrid
Validate sampling methods
Plants
85
31
125
Affymetrix chips
• Temperature
Many questions !!!
• Osmotic stress
• Water deprivation
Irrigated
Non - irrigated
PAG XXII, Genomics-assisted breeding
AgriGO
San Diego, January 12 2014
• ABA stimulus
PAG XXII, Genomics-assisted breeding
Field transcriptomics
San Diego, January 12 2014
Plant water status
Starting from the field ground truth
Many questions:
Evaporative demand
H2O transpiration (E)
What is the real stress intensity in the field?
What physiological processes are affected during
drought stress in the field?
Available water or FTSW
Fraction of Transpirable Soil Water
=
1
Total Transpirable Soil Water
A=E
At pre-dawn
Leaf water potential ΨPD
reflects the available water
What genetic network control those physiological
processes?
Water available
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
PAG XXII, Genomics-assisted breeding
H2O absorption (A)
San Diego, January 12 2014
1
2/26/2014
Glasshouse experiments
Plant water stress
Building models in controlled conditions
Evaporative demand 
1
Fraction of Transpirable Soil Water
(FTSW)
H2O transpiration (E)
Pre-dawn Leaf Water Potential
ΨPD
Water constraint
=
Fraction of Transpirable Soil Water
FSTW 
A<E
60%
Leaf
growth
50%
Stomatal
closure
40%
Leaf water potential
ΨPD 
30%
Water
Stress
20%
Turgescence
Water available 
PAG XXII, Genomics-assisted breeding
H2O absorption (A)
San Diego, January 12 2014
10%
0
PAG XXII, Genomics-assisted breeding
+
Permanent Wilting Point
(PWP)
San Diego,
January 12 2014
Glasshouse experiments
Glasshouse experiments
Building models in controlled conditions
Building models in controlled conditions
PHENOTYPIC PARAMETERS
Integrated stress 1-FSTW (ITW)
Fixed Duration Stress
Fraction of Transpirable Soil Water
(FSTW)
1
8 genotypes
hybrids / inbred lines
tolerant /sensitive
Pre-flowering stage
Plant height (PHe)
Transpiration (ET)
60%
50%
96 samples
Transcriptomics (Affymetrix)
 Morpho-physiological phenotyping
40%
Osmotic potential (OP)
Relative water content (RWC)
Photosynthesis
30%
20%
Specific Leaf Area (SLA)
Water Use Efficiency (13C)
Fixed Intensity
Stress
Collar diameter
10%
0
Time
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
Glasshouse experiments
A gene-phenotype network for drought response
Building models in controlled conditions
Building models in controlled conditions
Dimensionnality problem
Highly correlated phenotypic traits
Sparse Partial Least Squares
using mixOmics R package
Fixed intensity stress
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
PAG XXII, Genomics-assisted breeding
Fixed duration stress
Both
San Diego, January 12 2014
2
2/26/2014
A gene-phenotype network for drought response
A gene-phenotype network for drought response
Linking field and controlled conditions data
Rengel et al. 2012 PLoS ONE
Some answers in controlled conditions:
Adjustment of osmotic potential is early and associated to transpiration
ABA induced genes are very important but do not show GxE interaction
We identified genes highly correlated to
- physiological processes: transpiration, osmotic potential, …
- stress intensity
Confirms field experiments
Fixed intensity stress
Fixed duration stress
PAG XXII, Genomics-assisted breeding
Can we build a biomarker to estimate the water status of
the crop?
Both
San Diego, January 12 2014
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
Construction of a water status biomarker
Construction of a water status biomarker
Selecting genes
Controlled conditions experiment design
Range of
Fixed Intensity
Stress
Fraction of Transpirable Soil Water
(FSTW)
1
4 genotypes
hybrids / inbred lines
tolerant /sensitive
60%
2 developmental stages
50%
40%
Selected genes
No genotypic effect
30%
20%
10%
Fixed intensity stress
Fixed duration stress
PAG XXII, Genomics-assisted breeding
Both
San Diego, January 12 2014
0
Time
PAG XXII, Genomics-assisted breeding
Construction of a water status biomarker
Construction of a water status biomarker
Fitting a GLM in controlled conditions
Validating the model in field conditions
Water status =
Estimated
Drought Stress Intensity
+ a1 * dCt1
+ a2 * dCt2
+ a3 * dCt3
+k
HaT13l002207
HaT13l002636
HaT13l005199
Both transcriptomics
tubulin
GBF3
XTR7 concanavalin
 3 experimental platforms
INRA, Syngenta, Soltis
Different soil depths and compositions
Different irrigations
R² = 0.78
RMSEc = 0.64
 3 genotypes
Melody, Inedi, XRQ
Inbred lines
XRQ PSC8
Hybrids
Inedi Melody
Observed
Pre-Dawn Leaf Water Potential
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
San Diego, January 12 2014
 During 4 weeks after flowering
and ΨPD
 24h survey
Every 3 hours
Diurnal variation of gene expression
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
3
2/26/2014
Construction of a water status biomarker
Use of a water status biomarker
Validating the model in field conditions
Characterization of the environment in field trials
Estimation of drought stress intensity
in natural/field conditions
Validation on field data
 Pros
• Cost effective (4 genes by qPCR)
• High throughput
• Sampling time between 10:00 and 17:00
Estimated
Drought Stress Intensity
R² = 0.61
RMSEc = 0.67
 Cons
• Results are not immediate
Auzeville
Fleurance
Samatan
 Study traits subjected to GxE interactions
Genetics  removes environment effect on trait
Diseases  interaction with drought (Phoma macdonaldii in
sunflower)
Observed
Pre-Dawn Leaf Water Potential
 Dynamic description of the environment
Helps fitting of crop models
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
Many thanks to our collaborators
Use of a water status biomarker
Integration in a crop madel
Transcriptomics
Transcriptomics
Estimation of FTSW
Estimation of FTSW
Stress integration
module
G. Marchand
Publications: Rengel et al., 2012 PLoS ONE,
Marchand et al., 2013 Plant Cell & Environment,
Marchand et al. 2013 pre-published in arXiv
Genetics
Climate
Soil
Architecture
Phenotypic
traits
YIELD
Genetically
variable
B. Mayjonade
P126
D. Rengel
Phenology
Biomass allocation
Genetics: P. Vincourt, S. Muños, B. Mangin
Leaf expansion
Bioinformatics: J. Gouzy, S. Carrère
Leaf Transpiration
Crop
management
Agronomist Ecophysiologist: P. Casadebaig, P. Maury, Ph. Burger, Ph. Debaecke
Statisticians: I. González and S. Déjean: the mixOmics package (CRAN)
Sunflower biotech and breeders: Biogemma, Caussade, Maisadour, RAGT 2N, Soltis,
Syngenta
Schematic representation of sunflower crop model Sunflo (Casadebaig et al., 2010)
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
PAG XXII, Genomics-assisted breeding
San Diego, January 12 2014
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ΨPD
SUNRISE PROJECT
www.sunrise-project.fr
2012-2019
Night
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Looking for post-docs
And PhD students
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ΨPD or ΨPD’ measurement
Contact:
[email protected]
ΨPD
Ψ Leaf water potential measurement
Leaf harvest for transcriptomic
Night
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San Diego, January 12 2014
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2/26/2014
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