Investigating drought resistance using metabolic phenotyping · Investigating drought resistance using metabolic phenotyping ... 285 x 2 hybrids grown in several fields ... 25 26

DROPS – Eucarpia - Recent progress in drought tolerance: from genetics to modelling - June 8 & 9 2015 - Montpellier

Investigating drought resistance using metabolic phenotyping

Yves Gibon Fruit Biology and Pathology unit Bordeaux Metabolome Platform

INRA & Bordeaux University


Metabolomics & metabolic phenotyping

Leaf metabolism and yield

Ear metabolism and yield

Conclusion and outlook

Int roduct ion and out line

The power of QTL-assisted breeding has recently been questioned Metabolic traits are seen as highly condensed information that is intermediary between gene expression and agronomical traits


M et abolomics are scar y

4 metabolic traits able to predict your imminent death: alpha-1-acid glycoprotein albumin very-low-density lipoprotein particle size citrate


M et abolomics for healt hier fr ies

Acrylamide, a potential carcinogen, is formed during the fabrication of potato chips

GC-TOF revealed that genotypes with high fructose and glucose levels in potato tubers give low quality chips

At high temperatures, reducing sugars react with amino groups of proteins yielding acrylamide (Maillard reaction)


M et abolic t rait s as power ful biomar ker s

in M aize

285 x 2 hybrids grown in several fields 130 metabolites measured at an early vegetative stage 56k SNPs Both groups provide prediction accuracies of up to 80% for agronomical

traits using ridge regression–best linear unbiased predictions (RR-BLUP) Single metabolites correlate only weakly


H igh t hroughput ver sus high densit y

Microplate automation

Orbitrap

LCMS targeted

GCMS

NMR

LCMS untargeted

Sample number

Analyte number

LC-UV/visible

at equal costs


H igh Throughput M et abolic Phenot yping

Experiment Data

Harvest Sample grinding <-60°C

Aliquot weighing

<-60°C Capping & decapping of tubes

Extraction Pipetting

Mixing Incubating Timing...

Reading Raw data

processing

Microplate readers Robotic stations

Tube (de)capper

LIMS

Cryogenic sample grinding & weighing

Scintillation vials Storage tubes Microplates

Optimisation and Standardisation of consumables


Bordeaux H it -M e plat for m

3 grinding mills, 1 cryo-weighing robot, 2 capping/decapping machines 3 fully equipped 96-format pipetting robots, 11+1 microplate readers, a dedicated LIMS…

Currently: #400,000 analyses / year (e.g. 10 analytes in 40,000 samples) #20 key metabolites: low costs (#2€/sample), low needs (20 mg FW)

#60 enzyme activities: low costs (0.1-1€/enzyme/sample), low needs (20-100 mg FW)


Finding genes behind met abolic t rait s

One metabolic trait is associated to hundreds of QTLs

Nested association mapping population #5,000 genotypes One experiment = 12,000 samples 12 metabolic traits measured

Nitrate

Glutamate Protein

Chlorophyll

Citrate

Malate

Hexoses Sucrose

Starch

Fumarate

Amino acids

C storage

C export

N storage C4 metabolism

Growth

Photosynthesis


Finding genes behind met abolic t rait s

Levels of central metabolites in C and N metabolism are determined by genetic variation in key genes involved in carbon capture (carbonic anhydrase) and movement (malate transporters)

Manipulation of these loci by breeding or transformation and then optimization in a wide range of environments could lead to even more efficient yield

RMIP = resample model inclusion probability


Leaf met abolism and plant per for mance

• Ideal metabolic screen?

– In a greenhouse or a growth chamber

– Younger developmental stages

– Quick and easy sampling

– Cheap and fast analysis

– Highly accurate prediction of agronomical traits


Predict agronomical t rait s from met abolic

dat a?

• Leaf samples taken during vegetative growth phase at PhenoArch

• Analysis of the metabolic traits mapped with the NAM population (Zhang et al., Plant Physiol., in press)

• Integration with grain yield data obtained in 15 fields within DROPS

?


• 13 metabolic traits in 60 hybrids grown in an automated greenhouse (2011, PhenoArch, Montpellier), <1€ per profile

• Questions:

– What optimal sampling: downsizing to #20 mg fresh weight

– What time of the day: end of day versus end of night

– Can we distinguish the treatments?

• Results

– No significant differences between End of Day & End of Night: Arabidopsis and Maize are two different things!

– The data was a little bit messy: inappropriate sampling with harvester effect

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Baptiste/FrançoisBaptiste/Olivier

BenoitCorrine

Corrine/Emilie

Yves

Baptiste/François

Baptiste/OlivierBenoitCorrine

Corrine/EmilieYves

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EDEN

ED

ENTreatment Harvester Time of day


The experiment was repeated in 2012: o256 maize hybrids

oWell watered or in water-deficit

o Leaf samples harvested during the day

o2 biological replicates, 2 technical replicates

o13 metabolic traits in >1000 samples

There was a strong year effect but the technical and biological error was much better

Opt imisat ion of a met abolic screen

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2011


11 metabolic variables measured in leaves of well watered plants

Partial Least Squares regression (PLS) to predict yield (29 field trials)

A training set and a validation set, each with 124 hybrids

Predict ion of yield is promising

The model was able to predict up to 24% (R2) of the grain yield

-1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1

axis 1

axis

2

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V11 Yield

Circle of Correlations


Predict ion of yield losses not so good

The model was able to predict up to 5% of yield loss in the field

11 metabolic variables expressed as ratios between well watered and water deficit

PLS regression

A training set and a validation set, each with 124 hybrids

Too many correlated variables: get back to step 1 -1 -0.5 0 0.5 1

-1

-0.5

0

0.5

1

axis 1

axis

2

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V2

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V11

Yield response

Circle of Correlations


Search for fur t her met abolic mar ker s

Untargeted metabolomics performed on a subset of 30 maize hybrids leaf samples 3 replicates, 1H-NMR and LC-QTOF-MS, >150€ / profile

PCA performed with all 2868 variables

PCA scores plot PCA loadings plot

PC1 (14%)

PC

2 (

10

%)


Search for fur t her met abolic mar ker s

Untargeted metabolomics performed on a subset of 30 maize hybrids leaf samples (3 replicates; 1H-NMR and LC-QTOF-MS)

PCA performed with the 30 top variables found significant for genotype as factor

PC

2 (

19

%)

PC1 (27%)

PCA scores plot PCA loadings plot

Develop new targeted assays


Seed abor t ion

• Maize grain yield is sensitive to drought occurring at anthesis

• What provokes abortion? – Sugar starvation / signalling hypothesis: low invertase activity

leads to impaired carbon import, energy supply, osmotic adjustment and ultimately to abortion (Zinselmeier et al., 1999; Kakumanu et al., 2012)

– Inadequate timing hypothesis: tip silks emerge too late to enable fertilisation (Otegui et al., 1994; Oury et al., accepted)

• Strategy

– Detailed time-course study using the B73 reference hybrid (Olivier Turc)

– Hypothesis testing in a small panel of 7 genotypes


Seed abor t ion

2. Interesting observations:

• Gradient in metabolic composition between the basis and the tip of

the ear

• Hexoses are the most abundant osmolytes and thus major players

of cell turgor and expansion

• Hexose concentrations are only slightly influenced by water stress

1. Vincent Oury et al. (in preparation): seed abortion is not provoked by carbon

starvation, but by inadequate timing of silk emergence

Well Watered

Water Deficit

Glucose

Fructose

Sucrose

Amino acids

Glutamate

0100200300400500600700

Base Tip Base Tip Base Tip Base Tip Base Tip Base Tip Base Tip Base Tip

WD WW WD WW WD WW WD WW

Husks Emergence Silks Emergence SilksEmergence+5d

SilksEmergence+7d

mo

smo

l.l-1


Seed abor t ion

2. Interesting observations:

• Gradient in metabolic composition between the basis and the tip of

the ear

• Hexoses are the most abundant osmolytes and thus major players

of cell turgor and expansion

• Hexose concentrations are only slightly influenced by water stress

Are metabolic gradients heritable?

Are they associated with abortion?

1. Vincent Oury et al. (in preparation): seed abortion is not provoked by carbon

starvation, but by inadequate timing of silk emergence

Genotype 2 Genotype 1


Seed abor t ion

• Water table experiment in Bologna (2012) • 7 genotypes, control conditions and water stress

• Ears collected right after silk emergence and dissected

into tip and basis, then ovules and rachis (190

samples)

• Biochemical analysis

• Metabolites: major sugars, starch, organic acids, amino acids,

protein content

• Sugar enzymes: sucrose synthase, neutral invertase, acid

invertase, cell wall invertase, sucrose phosphate synthetase


Dat a over view

-1.0 -0.5 0.0 0.5 1.0

-1.0

-0.5

0.0

0.5

1.0

Variables factor map (PCA)

Dim 1 (29.23%)

Dim

2 (

17

.63

%)

Amino.Acids

Fumarate

Glucose

GlutamateFructose

Hexoses

Malate

Sucrose

Protein

Nitrate

Starch

Sucrose.Synthase..E.

Neutral.Invertase

Acid.Invertase

Sucrose.Phosphate.Synthase

Cell.Wall.Invertase

-5 0 5

-6-4

-20

24


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Dim

2 (

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.63

%)

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333334

OvuleRachis

OvuleRachis

Higher hexoses in the rachis Higher enzyme activities and N-compounds in the ovule

Rachis Ovule


Dat a over view

-1.0 -0.5 0.0 0.5 1.0

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1.0


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Dim

2 (

17

.63

%)

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Fumarate

Glucose

GlutamateFructose

Hexoses

Malate

Sucrose

Protein

Nitrate

Starch


Neutral.Invertase

Acid.Invertase


Cell.Wall.Invertase

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2 (

17

.63

%)

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R1

R4

R1R4

More of everything in ears of stressed plants

Drought

Control

The 2nd dimension is more relevant, so let’s mix ovules and rachis


Dat a over view

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2 (

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.02

%)

Amino.Acids

Fumarate

Glucose

Glutamate

Fructose

Malate

Sucrose

ProteinNitrate

Starch


Neutral.InvertaseAcid.Invertase


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2 (

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.02

%)

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Tip

BaseTip

Now we can distinguish samples based on their position in the ear:

Drought effect still visible but still on the second PC


Cor relat ions bet ween met abolic var iables

and yield

High N in the basis under stress = low yield

Hexoses and sucrose show opposite trends


Tip sugar s cor relat e w it h yield

y = 20,505x - 856,97 R² = 0,4793

y = 6,2213x - 4,5067 R² = 0,0549

0

200

400

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1400

50 70 90 110

y = -13,887x + 1670,4 R² = 0,0994

y = -13,203x + 1546,4 R² = 0,5104

0

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1400

40 50 60 70 80 90

Fructose (µmol/gFW) Sucrose (µmol/gFW)

Yield Yield

Hexoses are positively correlated to yield in well watered plants Sucrose is negatively correlated to yield in stressed plants

Sucrose metabolism


Car bon st ar vat ion not t he cause,

but…

Maintaining high hexose to sucrose ratios in the tip may lead to higher yield under drought

Phenotyping ear metabolism not too bad

What next?

Repeat and extend the study to a whole panel of hybrids

Develop a mechanistic model connecting sucrose metabolism and silk growth via the Lockhart equation

Y P

t

[Hexoses] Y P

t

[Hexoses]

Control Drought

Even little less turgor may result in decreased silk growth rate


Conclusions and out look

• Leaf metabolism in the greenhouse versus yield in the field

– Combinations of metabolic traits could be used to predict yield and yield loss

– Predicting drought resistance will be more difficult

– Further metabolic variables needed

• Ear metabolism and seed abortion

– Carbon starvation may not be the cause but there could be a link between C metabolism and yield

– Should we extend the study?


A ck now ledgement s

• Bordeaux Platform: Duyên Prodhomme, Patricia Ballias, Annick Moing, Mickael Maucourt, Stéphane Bernillon, Nadia Lamari and colleagues

• PhenoArch Experiment: François Tardieu, Claude Welcker, LLorenz Cabrera, Emilie Millet and colleagues at LEPSE-Montpellier

• Bologna experiment: Roberto Tuberosa, Maria Angela Cane and colleagues at UNIBO; Olivier Turc, Vincent Oury and Claude Welcker

• Field data: all participants and in particular Björn Usadel and Claude Welcker

Documents

Investigating drought resistance using metabolic phenotyping · Investigating drought resistance using metabolic phenotyping ... 285 x 2 hybrids grown in several fields ... 25 26