1. WP5 _ Enhancing the knowledge of genetics
underlying novel traits and providing phenotyping methods
Results achieved since the beginning of the project and plans for 2013

2. Main objectives of the WP5
• Develop tools for novel trait determination
• Investigate novel and complex traits
• Make novel trait analysis high throughput and
applicable for the research community and the
breeding industry

3. 3 tasks
• Task 5.1 Improve Monilinia resistance in peach
• Task 5.2 Assess fruit quality Jurriaan
• Apple
• Peach Remo
• Task 5.3 Select traits important for climate change
adaptation Evelyne

4. Results achieved since the beginning of the project
Task 1. improve resistance to Monilinia in peach
• Protocols for infection tests in lab
• Test of artificial infections in orchard
• Microscopy analysis of infection
• Biochemical analyses of fruit surface
• Tests of fungicide activity of some compounds
• Spore survey in the orchard

5. Results achieved since the beginning of the project
Task 1. improve resistance to Monilinia in peach
Spray Monilinia laxa
suspension
Infection enhancement
by humidity increasing:
fruit covering
One week incubation
Susceptibility score:
% infected fruits
Setup of high-throughput orchard
brown-rot phenotyping protocol
CB2 CB3
inoculation
covering
no yes
no C I
paper CB2 IB2
plastic CB3 IB3

6. Results achieved since the beginning of the project
Task 1. improve resistance to Monilinia in peach
Spray Monilinia laxa
suspension
Infection enhancement
by humidity increasing:
fruit covering
One week incubation
Susceptibility score:
% infected fruits
Setup of high-throughput orchard
brown-rot phenotyping protocol
CB2 CB3
IB2 treatment allowed to
distinguish between
tolerant and susceptible
accessions

7. cell collapse ( )
fungal colonization ( )
Monilinia disease progress :
8 vs. 48 hour after inoculation
Zephir 8hpiZephir 48hpi
E. Lady 8hpiE. Lady 48hpi
Bolinha 8hpiBolinha 48hpi
Bolinha: no fungal impact
on the analysed samples

8. Results achieved since the beginning of the project
Task 1. improve resistance to Monilinia in peach
Biochemical analyses of fruit surface
40 60 80 100 120 140 160
050100150
Jours après floraison
Massed'unfruit(g)
A
I
II
III
40 60 80 100 120 140 160
050100150
Jours après floraison
Demi-circonférence(mm)
B
40 60 80
0.00.51.01.52.0
Jours ap
IndicedeDifférenced'Absorbance
C
Fruit growth for 2 cultivars
Days after bloom
Fruitmass(g)
020406080100
25-apr 16-may 30-may 20-jun 4-jul 18-jul maturity
Infectionprobability(%)
* ** *
SG
ZE
Infection probability for 2 cultivars
I II III

9. Results achieved since the beginning of the project
Task 1. improve resistance to Monilinia in peach
Cuticular conductance
Biochemical analyses
• surface compounds
• waxes
• cutins
• epiderm and flesh phenolics
40 60 80 100 120 140 160
05101520
total cuticular wax quantities (mg/dm²)
DAB
waxaccumulationmg/dm²
SG
ZE
Total cuticular waxes accumulation
Wax(mg/dm²)
Days after bloom
0 50 100 150 200 250
2004006008001000
masse du fruit (g)
conductancedel'épiderme(cm/h)
SG
ZE
Fruit mass (g)
Cuticularconductance(cm/h)
Cuticular conductance

10. Results achieved since the beginning of the project
Task 1. improve resistance to Monilinia in peach
Correlations between
infection probability and
surface compounds
-1-0.75-0.5-0.2500.250.50.751
P
Pic64.6_307
Pic65.7_312
-1 -0.75 -0.5 -0.25
Proportiond'infection
Acideoléanolique
Acideursolique
Oleanolic acid
Ursolic acid
pcoumaroyl
Pic52.5_312
Pic54.6_308
Pic64.6_307
Pic65.7_312
inhibitory candidates
020406080100
25-apr 16-may 30-may 20-jun 4-jul 18-jul maturity
Infectionprobability(%)
* ** *
SG
ZE
Infection probability
I II I
I
I
mg/dm²
01234
Oleanolic acid mg/dm²
0246810
Ursolic acid
Airedepic/dm²/10^5
051015202530
Pic52.5_312
Jours après floraison
Airedepic/dm²/10^5
051015
Pic64.6_307
40 60 80 100 120 140 160
051015202530
Jours après floraison
Airedepic/dm²/10^5
Pic65.7_312
40 60 80 100 120 140 160
0510152025
Airedepic/dm²/10^5
p-coumaroyl derivative
SG
ZE
days after bloom
Evolution of fruit surface compounds
along fruit growth
Days after bloom

11. Task5.2 Assess fruit quality
State of progress

12. Research topic in task 5.2
• Texture/maturation/quality tools evaluation
– Compare, use and evaluate common used methods (colour cards, pm, ss, tta,
AWETA, DA)
– Acoustic method for crispiness (FC, NZ)
– Juice/juiciness analysis by punching and study tissue structure (NZ)
– New methods for Fibre content (ML)
• Gene expression based methods for analysing fruit texture trait
– Ethylene pathway dedicated approach to unravel ripening and texture trait
biomarkers (DLO)
– Differential cultivars for harvest time, storage quality, shelf-life and meatiness
(JP)
• Storage stress test to select and early predict storage performance of new
lines
– Standard measurements and inspection (DLO)
– Gene expression analysis (JP, DLO)

13. Texture/maturation/quality tools evaluation
Quality measurements (n=20)
• At harvest
• After 1 week regime + 1 week shelf life (18°C, 75% RH)
• After 1 month regime + 1 week shelf life (18°C, 75% RH)
• After 2 months regime + 1 week shelf life (18°C, 75% RH)
Monitored quality parameters:
• Fruit weight
• Ethylene production (at harvest)
• Firmness (FTA automated penetrometer, destructive)
• Firmness (AWETA acoustic, non-destructive)
• Ground colour (colour chart)
• Chlorophyll status (DA-meter, non-destructive)
• External and internal disorders (expert visual evaluation)

14. SensorSense ETD 300 characteristics
• Detection limit 300 ppt (0.3 ppb)
• Upper limit 5 ppm (this one up to
100 ppm)
• Measurement every 5 sec
• Accuracy: <1% of 0.3 ppb (largest
value)
• Very specific for ethylene
• Calibration yearly

15. 0 20 40 60 80
0
2
4
6
8
10
12
14
16
18
20
22
24
26
40
45
50
55
60
65
70
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0
2
4
6
8
10
12
14
16
18
20
22
24
26
40
45
50
55
60
65
70
0 20 40 60 80
0
2
4
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8
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0 20 40 60 80
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20
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24
26
40
45
50
55
60
65
70
TA-XTplus - AED
4 mm flat head probe
5 Kg loading cell
100 mm/min
Acquisition at 500 pps
Compression strain: 90%

16. Approach Plant & Food Research
• 6 cultivars
• 10 trained panellists
• 14 sensory texture attributes
• 3 fruit per cultivar
• 3 storage times (scheduled according to
softening rate)
• Air storage only
• 1 harvest (using starch and skin colour

17. Approaches for improved phenotyping technologies
– optimising existing technologies
• Destructive
• Tensile
• Single edge notch beam
• Rheometer (frequency sweep)
• Juicer Kinetics
• Microphone
• Nondestructive
• AWETA
• Sinclair

18. Juiciness potential assoc. with larger cells,
higher density and more apoplastic fluid
Scifresh
Sciros
C
.Pink
R
.G
ala
Sciearly
C
ox
0.75
0.80
0.85
0.90
0.95
Corticaldensity(g/mL)
Scifresh Sciros RGala C.Pink
0
20000
40000
60000
80000
100000
Cellaream
2
Scifresh
Sciros
C
.Pink
R
oyalgala
0.05
0.06
0.07
0.08
Apoplasticfluid(g/gFW)

19. List of tools
Tas
k
Sub
-
tas
k Sub-task name Trait or step
Destructive
/ non Protocol / tool
Tested (1 or 2
years) Species
Conclusions
on protocols
available Feasibility Reliability
High-
throuput
2 2.1 Maturity
assessment
Index of
differential
absorbance IAD
1 apple not yet* y y y
Starch D Y 2 " y y y y
Solible solids D Y 2 " y y y y
Titratable acids D Y 2 " y y y y
Streif Index D Y 2 " y y y y
Ethylene
production
N Y 1 " y y y y/n
Colour
determination
N Y 2 " y y y y
Chlorophyll ? status
(DA-meter)
N Y 1 " y y Not sure to
what
quality trait
it links
y

20. List of tools
Tas
k
Su
b-
tas
k Sub-task name
Trait or
step Sub-trait
Protocol /
tool
Destructi
ve / non
Tested (1
or 2
years) Species
Conclusions
on
protocols
available
Feasibilit
y
Reliabilit
y
High-
throuput
2.2 Fruit texture
assessment
Physical
paramet
ers
Firmness analysis by penetro
measurements
Texture
Analyser
D 1 apple y y y y
hand-held
penetromet
er
D x " x x <y x
Firmness analysis by confined
compression test
D x " x x x x
Firmness analysis by non-
destructive acoustic
resonance devices
AWETA AFS N 1 " y y y y
Sensor deform at impact sinclair y y y/n y
Firmness analysis combined
with crispness analysis
acoustic-
mechanical
N x " x x x x
Juice press/spin test D x " x x x x
Color determination N 1 " y y y y
Bioche
mical
paramet
ers
Soluble solids D 1 " y y y y/n
Titratable acidity D 1 " y y y y/n
Flavour x " x x x x
Molecular characters of
texture
1(sample
s frozen)
" n ? ? ?

21. Water contribution/microstructure on texture (tissue mechanical
properties): Evaluation of NMR relaxometry as a screening tool
Achieved
•Water content & compartmentalization were accessed by relaxometry but were
not directly related to mechanical properties
•Freezing and thawing samples exacerbate relations between mechanical
properties & relaxometric variables
On going
•Relate relaxometry & mechanical data with free sugar and cell wall compositions
•New assays to evaluate relaxometry of frozen samples vs
mechanical/histological/chemical variables
New methods for quality screening assessment
WP5.2
INRA-Nantes Biopolymers, Interactions, Assembly

22. Dietary fiber content and nature : development of fast screening method
by Mid IR analysis of alcohol insoluble tissue material (AIM)
Achieved
•AIM, protein and Mid-IR of 29 genotypes
On going
•DF (AOAC method TDF), cell wall sugar analysis, ash and starch contents
•Chemiometric analyses
New methods for quality screening assessment
WP5.2
INRA-Nantes Biopolymers, Interactions, Assembly

23. Identification of genes and networks controlling major apple quality traits
Materials:
10 varieties (Golden, Gala, Elstar, Greensleave, Cox, Boskoop, Ariane, Jonagold, Fuji,
Granny Smith)
4 contrasted hybrids
4 time points: Harvest, 1 month post-harvest, 2 MPH, 4 MPH
2 years of data: 2011&2012
Texture traits:
Expert sensory panel (1 to 5 values): Fiber, Grain, Mealiness, Softness, Crunchiness,
Juiciness, Acidity, Sugar
Physical and Chemical measurements:
Acidity, Sugar, Ethylene, Penetrometry, Compression
Results:
2011: Phenotypic data completed (contrasted traits, differents kinetics of trait evolution)
2012: Finishing the 4MPH measurements
RNA purification completed soon (112 samples)
Transcriptomic results with the AryANE chip in June 2013
Bioanalayses and Network buildings during summer
WP5-3
To be completed: material exchange with DLO and PFR (comparison with RNAseq)
functional validation of the candidates

25. R scripts developed for data treatment
24 « .pair » files
Raw data
12 « .txt » files
Norm. data
normalized intensities per sample,
ratios, p.value, localFDR
(Limma & fdrtool packages)
RG_plot.png
MA_plot.png
PCAS
Clusters
Dendrograms
...
complet-lists.txt
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Annotations.txt

26. MapMan
Dongxia Yao et al Genomics Volume 98, Issue 1 2011 47 - 55

27. Storage Stress Test Tool
• Success cultivar very much dependent on storage
behaviour
• Use commercial pick and commercial storage CA,
DCS, mechanical/SF
• Stress test and supporting tools to analyse this
behaviour
– Does it suffer in-cooling or CA stress
– Is it Low Temp Sensitive
– Firmness during storage
– Sensitive to storage disorders

28. Experimental setup
Five commercially grown cultivars
1. Cox O.P.
2. Elstar
3. Golden Delicious
4. Jonagold
5. Kanzi
Harvested
• a week before
• at commercial harvest date (for long term ULO-storage)
• 1 week after
regime T (°C) %O2 %CO2
1 -1 21 0
2 -1 1 5
3 10 21 0
4 10 1 5

29. Overview of storage stress test (Y1)
Cultivar
Max
storage
period
(optimal
conditio
ns)
Low Temp
Sensitivity flesh
browning
Texture issues
Low O2/high CO2
tolerance
Disorder sensitivity
practic
e
test practice test practice test practice test
Cox 5-6 m +++
Can develop
mealiness
before
softening
-
Bitter pit,
brown core,
Softening
Kanzi 12 m +++
Very firm,
tough peel
?
bitter pit,
lenticel
breakdown
Jonagold 9 m - Greasiness ++
Scald, flesh
browning
Golden 8 m - ++ Scald
Elstar 7 m +
Rapid softening
after storage
+
Softening,
skin spots

30. Overview of storage stress test (Y1)
Cultivar
Max
storage
period
(optimal
conditio
ns)
Low Temp
Sensitivity flesh
browning
Texture issues
Low O2/high CO2
tolerance
Disorder sensitivity
practic
e
test practice test practice test practice test
Cox 5-6 m +++
+++
(-1°C + CA,
late harvest,
≥1 month)
Can develop
mealiness
before
softening
Mealiness ,
rapid
softening
(air, ≥1
month)
- -
Bitter pit,
brown core,
Softening
-
Kanzi 12 m +++
++++
(-1°C + CA,
late harvest,
2 months)
Very firm,
tough peel
Very firm
indeed
?
++
(-1°C + CA,
last harvest,
≥1 month)
bitter pit,
lenticel
breakdown
-
Jonagold 9 m - - Greasiness
Greasiness
(10°C, air, ≥1
month)
++
+
(-1°C + CA,
3 harvest, ≥1
month)
Scald, flesh
browning
-
Golden 8 m - -
Greasiness
(10°C, air, ≥1
month)
++
++
(-1°C + CA,
1+3 harvest,
≥1 month)
Scald -
Elstar 7 m + -
Rapid softening
after storage
Confirms
practice
+ -
Softening,
skin spots
Skin spots
(all treatm)

31. Samples for microarray
Cultivar
Gen
etic
bac
kgro
und
Max
storage
period
(optimal
conditions)
Texture issues
Low Temp
Sensitivity flesh
browning
Low O2/high CO2
tolerance
Disorder
sensitivity
practice test practice test practice test
Cox 5-6 m
Can develop
mealiness before
softening
+ +++ ++ - -
Bitter pit, brown
core, Softening
Golden 8 m +/- - - ++ + Scald
Kanzi 12 m
Very firm, tough
peel
- +++ + ++(?) ++
bitter pit,
lenticel
breakdown

32. Task5.2 Assess fruit quality (peach)
State of progress

33. Cultivar Texture Type Pulp
Institution
Cultivar Texture Type Pulp
Institution
UMIL IRTA ARO UMIL IRTA ARO
Oro A NM Pc Y √ BO 05030142 SH P G √
BO 94007020 NM Pc Y √ BO 05030149 SH P G √
Dixired M P Y √ BO 05030081 SH P G √
Iride NM Pc W √ Elegant lady M P G √
Alice Col NM Pc Y √ IFF 331 SH P B √
Ambra M N Y √ √ Sweet dream M √
Big Top SM N Y √ √ Dulcebo SM P G √
Honey Kist SM N Y √ SRG M N G √
Redhaven M P Y √ Honey Royale SM √
Rich Lady SM P Y √ Nectaross M √
Vista Rich SM P Y √ BO 10120182 NM √
Ghiaccio SH P W √ √ Bolero M P G √
Glohaven M P Y √ BO 89010005 M N B √
Durado NM Pc Y √ Fei Cheng NM Pc B √
Alipersie M P Y √ Maria delizia M P B √
BO 00020006 NM Pc Y √ Dulciva M N G √
Swelling M P W √ Summer snow SM P W √
September Snow SM P W √ Fairlaine SM N Y √
1881 SM P W √ Hermoza SM P W √

34. Plant material & Experimental Design
Harvest
ColdStorage(21dat4ºC)
14d
1d
3d
21+1d
21+3d
0d
21d
21+5d
5d
Postharvest trial
2d
21+2d
Classification
M1 M2 M3
2011, 2012: M1, M2, M3

35. Summary (I)
Trait Sub-trait Technique Years Comments
Physical
parameters Firmness analysis by
penetro measurements
Texture Analyser 1&2
Differentiate between fruit with different
texture types through storage
hand-held
penetrometer
1&2
In some cases, differentiate between fruit with
different texture types through storage
Firmness analysis by
confined compression test
Texture Analyser 1
No additional information if compared to other
physical firmness techniques
Firmness analysis by non-
destructive acoustic sensor
AWETA AFS 1&2
Differentiate between stone fruit type but not
different textures
Compression to
penetration ratio
Texture Analyser 2 Interesting results in Y1 but not in Y2
Juice press & spin test Expressible juice 1&2
Differenciates fruit with different textures
through storage
Color determination &
Portable
spectrophotometer
Minolta CR2600d 1&2
No suitable for differentiating fruit with
different textures
NIR
Bruker MPA Multi
Purpose
FT-NIR Analyzer
0 Data currently being analyzed

36. Summary (II)
Trait Sub-trait Technique Years Comments
Biochemical
parameters
Soluble solids
Standard
1&2
No relationship with texture
Titratable acidity 1&2
Ethylene production 1&2
Not directly related to fruit texture, some
interesting results that need to be further
confirmed
Antioxidant capacity FRAP assay 1&2 No direct relationship with fruit texture

37. Summary (III)
Trait Sub-trait Protocol Years tested Comments
Fruit structure
and imaging
TRS
Instrument developed by
Politecnico of Milano
1&2
Discriminate 3 out of 4
texture type (M, Sm
and SH)
Nuclear Magnetic
resonance
Esaote Airis II
field intensity of 0,3 Tesla
1
No difference in
texture (abandoned)
Echography
Multimage Aloka ssd-500
scanning frequency of
3,5-5,0-7,5 MHz
1
No difference in
texture (abandoned)
Computerized
tomography
Stratec Medizintechnik
XCT Research SA+
1&2
Gene expression RNA seq Illumina HiSeq 2000 2
Data currently being
analysed
Sensory
evaluation
Firmness perception
3 texture attributes and
likeness
2
No capability to
distinguish between
texture types but
differentiate through
storage

38. Phenotyping tools
 Standard quality (TSS, TTA)
 Firmness (Penetrometry, Texture Analyser and Acoustic Firmness sensor)
 Expressible juice
 Objective colour (L*, a* and b*; Spectrophotometer 360-740nm)
 Fruit ethylene production
 Sensory Analysis (Consumer tests)
 Biochemical analysis (Antioxidants, MDA…)
Fmax
F(5% Def.)
P&D ratio
F
Stiffness
Frecuency
Impact Force

39. Phenotyping tools
 Time Resolve Reflectance Spectroscopy (TRS)
 Computerised tomography (CT)
 Near Infrared Spectroscopy (NIR)
 Echography
 Nuclear Magnetic resonance(NMR)

40.  Gene expression analysis
Transcriptomics
cytokinesis cell stretching
time
Fase I Fase II Fase III Fase IV
t0
t1
t2
t3

41. Results
Fruit firmness: penetration
Days at 20ºC
0 1 2 3 4 5 6
Firmness(Kg)
0
1
2
3
4
5
6
7
Days at 20ºC
0 1 2 3 4 5 6
Firmness(Kg)
0
1
2
3
4
5
6
7
Firmness(Kg)
0
1
2
3
4
5
6
7
Firmness(Kg)
0
1
2
3
4
5
6
7
Ambra (M)Firmness(Kg)
0
1
2
3
4
5
6
7
8
After CS
Before CS
Firmness(Kg)
0
2
4
6
8
Big Top (SM)
Honey Royale (SM) Nectaross (M)
Rome Star (M) Sweet Dream (SM)
S
M

42. -> No clear differences among
different texture types
-> Values from IRTA much lower
than those from ARO (Impact of
Agroclimatic conditions?)
Am
braB
ig
Top
H
oney
R
oyale
N
ectaross
R
om
e
Star
Sw
eetD
reamO
ded
Sw
elling
1881
Septem
berSnow
Fairlane
(N
ectarine)
H
erm
oza
Sum
m
erSnow
P/Dratio
0
1
2
3
4
IRTA ARO
Slow melting
Melting
Results
Fruit firmness: deformation
*
*
*

43. a a
0
10
20
30
40
50
60
70
80
0 1 2 3 4 5
ExpressibleJuice(%)
Day at 20°C
Melting
Non Melting
Slow Melting
a
a
a
b b
b
b
bab
c
0
10
20
30
40
50
60
70
80
0 1 2 3 4 5
ExpressibleJuice(%)
Day at 20°C
Melting
Non Melting
Stony Hard
Slow Melting
a
a
a a
aab
bc
ab
b
b
c b
c
b
c
2012
2011
Results
Expressible juice

44. Mie theory
b
s a 
  )('
Stony Hard
Slow Melting
Melting
Results
TRS

45. Mie theory
b
s a 
  )('
Stony Hard
Slow Melting
Melting
Results
TRS

46. Mie theory
b
s a 
  )('
Stony Hard
Slow Melting
Melting
Results
TRS

47. Mie theory
b
s a 
  )('
Stony Hard
Slow Melting
Melting
Results
TRS

48. Days at 20ºC
0 1 2 3 4 5
AcousticFirmness
0
10
20
30
40
50
Days at 20ºC
0 1 2 3 4 5
AcousticFirmness
0
10
20
30
40
50
AcousticFirmness
0
5
10
15
20
25
30
AcousticFirmness
0
5
10
15
20
25
30
AcousticFirmness
0
5
10
15
20
25
30
35
40
AcousticFirmness
0
5
10
15
20
25
30
35
40
After CS
Before CS
Ambra Big Top
Honey Royale Nectaross
Rome Star Sweet Dream
Acoustic firmness changes did not reveal
different softening behaviors among the
different cultivars investigated
Significant differences in the fruit acoustic
firmness were observed between different
stone fruit types (Nectarine vs Peach)
Results
Acoustic firmness
Before cold storage
After cold storage (21 d 4°C)
(M)
(M)
(M) (SM)
(SM)
(SM)

49. Results
CT

50. Results
CT

51. Big Top Oro A
Redhaven Alipersie
Rich Lady
IFF 331
BO 94007020
Ghiaccio
SM
M
NM
SH

52. 0
50
100
150
200
250
300
Canning peach Peach Nectarine
Densitygcm-3
Texture within peach group
Preliminary results
average density
0
50
100
150
200
250
300
SM M SH
Densitygcm-3

53. NGS raw data
Quality trimming and filtering (erne-filter)
Alignment to reference genome (Bowtie2)
Hits raw count
T1 T2
M SM SH NM M SM SH NM
T1
M – Redhaven – Rep 1 1 0 0 0 0 0 0 0
M – Bolero – Rep 2 1 0 0 0 0 0 0 0
SM - Big Top – Rep 1 0 1 0 0 0 0 0 0
SM - Rich Lady – Rep 2 0 1 0 0 0 0 0 0
SH - IFF 331 – Rep 1 0 0 1 0 0 0 0 0
SH - BO05030081 – Rep 2 0 0 1 0 0 0 0 0
NM - Oro A – Rep 1 0 0 0 1 0 0 0 0
NM - BO010120182 – Rep 2 0 0 0 1 0 0 0 0
T2
M – Redhaven – Rep 1 0 0 0 0 1 0 0 0
M – Bolero – Rep 2 0 0 0 0 1 0 0 0
SM - Big Top – Rep 1 0 0 0 0 0 1 0 0
SM - Rich Lady – Rep 2 0 0 0 0 0 1 0 0
SH - IFF 331 – Rep 1 0 0 0 0 0 0 1 0
SH - BO05030081 – Rep 2 0 0 0 0 0 0 1 0
NM - Oro A – Rep 1 0 0 0 0 0 0 0 1
NM - BO010120182 – Rep 2 0 0 0 0 0 0 0 1
Differential expression analysis
(R, Limma and EdgeR)
Results
Transcriptomic

54. For each type, the two time
points group together.
Only SH flesh type groups
tightly together
NM groups together at least
in one axis, while M and SM
samples group separately in
both dimensions
Results
Transcriptomic

55. Conclusions and prespectives
 It is feasible to differentiate between fruit with different
textures using time-course postharvest experiments with
certain techniques.

56. Task5.3 Adaptative traits
State of progress
INRA Montpellier and Bordeaux
UNIBO

57. Objectives
• Is it a genetic adaptation to ongoing climatic changes?
• Setting protocols easy to perform on populations for genetic studies and
to be duplicated in different sites
• Chilling and heating requirements
– Changes in temperature (during winter and spring) influences tree
phenology
– Can we phenotype for selecting cultivars with desired chilling and heating
requirement ?
• Water scarcity
– Identification of physiological parameters, potential candidates for
phenotyping tolerance and/or resilience to root water stress

58. Planned actions: CR et HR
apple and peach
• In apple: 3 cultivars (Golden Delicious, Gala, Granny Smith)
• In peach: 9 cultivars (Fantasia, Ferjalou Jalousia, Flavorcrest,
Mayglo, Redhaven, Summergrand, Summer Lady, Sunred, Tasty
Free).
chosen from bibliographical data on their respective temperature
requirements (contrasting CR from 200 to 1000 CH).
• Common methods performed in autumn 2011 and 2012 to
estimate dates of dormancy release for floral and vegetative buds
• Samples of shoots collected from October year n to february year
n+1 (collection every week at each site from december to february)
• Prospect other tests, search for new descriptors

59.  Two biological tests:
 forcing of ‘one-bud cuttings’ (vegetative buds)
 forcing of floral primordia (Tabuenca’s test) from paradormancy period (summer in
year n-1) to ecodormancy period (winter year n)
 Characterization of genetic and annual influences both in Southern France and
Southern Brazil (bilateral Project Capes Cofecub)
Observation of ‘green-tip’ stage:
average time and percentage of budburst
VEGETATIVE BUD FORCING
FLORAL PRIMORDIA
FORCING
(within floral bud)
Weighting of primordia before (in orchard )
and after forcing (fresh and dry weights)
Results: CR et HR apple

60. Results CR in apple

61. 1 – Apple:
Soil water restriction characterised by FTSW (Fraction of Total Soil Water)
- Bologne: Same protocol than in 2011: Leaf T° and fluorescence
- Mtp: with a volumetric control of water in the soil (Volumetric Humidity
assessment experiment);
* with mild root water restriction, applied during 3 weeks at 50% FTSW
followed by 3 weeks at 20% FTSW, at morphological
Measurements: (leaf area, shoot length and number of nodes) and eco-
physiological (stomatal conductance) levels,
• the ability to resume growth after a severe root water restriction, ie
provoking apex growth arrest, and possibly death, for most genotypes,
applied during 3, 4 and 5 weeks (resilience).
2 – Peach: 2 cvs grafted on a F1 rootstock progeny
2012 was the first year of water stress (field experiment)
OBJECTIVES FOR WATER SCARCITY

62. 2. Identification of a cluster of physiological parameters,
potential candidates for phenotyping
Scatterplot (DATASHEET FATTORI pca STRESSED.sta 10v*17c)
F2FLUOTL = -8.8167E-16+0.6582*x
7S
23S
26S35S
37S
38S
40S
41S
48S
54S
70S
96S
117S
121S
125S
gsS
stkS
-4 -3 -2 -1 0 1 2 3 4
F2ALL
-3
-2
-1
0
1
2
3
F2FLUOTL
F2ALL:F2FLUOTL:
r = 0.8153; p = 0.00007;
y = -8.4004E-16 + 0.6582*x
Scatterplot (DATASHEET FATTORI pca STRESSED.sta 10v*17c)
F1FLUOTL = 6.1837E-16+0.6678*x
7S
23S
26S
35S
37S
38S
40S
41S
48S54S
70S96S
117S
121S
125S
gsS
stkS
-6 -4 -2 0 2 4 6 8 10
F1ALL
-4
-3
-2
-1
0
1
2
3
4
5
F1FLUOTL
F1ALL:F1FLUOTL:
r = 0.9387; p = 0.00000002;
y = 4.3311E-16 + 0.6678*x
PCA ALL vs. PCA FLUO_TL (2011)

63. 2. Identification of a cluster of physiological parameters, potential candidates for
phenotyping
PCA ALL vs. PCA FLUO_TL (2012)
Scatterplot: F1_ALL vs. F1_FLUO_TL (Casewise MD deletion)
F1_FLUO_TL = 0.0000 + .61268 * F1_ALL
Correlation: r = .91001
7
23
26
35
37
38
40
41
48
54
57
70
96
106
111
117
121
125
gs
stk
-8 -6 -4 -2 0 2 4
F1_ALL
-5
-4
-3
-2
-1
0
1
2
3
4
F1_FLUO_TL
95% confidence
Scatterplot: F2_ALL vs. F2_FLUO_TL (Casewise MD deletion)
F2_FLUO_TL = 0.0000 + .86070 * F2_ALL
Correlation: r = .92362
7
23
26
35
37
3840
41
48
54
57
70
96
106
111
117
121
125
gs
stk
-3 -2 -1 0 1 2 3 4
F2_ALL
-2
-1
0
1
2
3
4
F2_FLUO_TL
95% confidence

64. Projection of the cases on the factor-plane ( 1 x 2)
Cases with sum of cosine square >= 0.00
Labelling variable: trt
7s
23s
26s
35s
37s
38s40s
41s
48s
54s
57s
70s
96s
106s
111s
117s
121s
125s
GSs
STKs
-6 -5 -4 -3 -2 -1 0 1 2 3 4 5
Factor 1: 53.19%
-3
-2
-1
0
1
2
3
4
5
Factor2:31.55%
Projection of the cases on the factor-plane ( 1 x 2)
Cases with sum of cosine square >= 0.00
Labelling variable: trt
Active
7S
23S
26S35S
37S
38S
40S
41S
48S
54S
70S
96S
117S
121S
125S
gsS
stkS
-8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5
Factor 1: 65.47%
-5
-4
-3
-2
-1
0
1
2
3
4
Factor2:24.93%
41 Stk 38 26 35 54 48
41 Gs 106 96 111 54
Gs 23 96 125 70 40 7 117 37 121
37 38 Stk 121 70 40 26 48 125 35
117 57 7 23
3. A tentative discrimination
2012
2011

65. As expected gs of 50% FTSW WS
plants was higher than gs of 20%
FTSW WS plants, with a decrease of
ca. 80% and 35% compared to gs of
WW plants, respectively.
7
23
26
35
37
38
4041
48
54
57
7096
97
106
111
117
121
125
GS
SK
7
23
26
35
37
38
40 41
48
54
57
70
96
97
106
111
117
121
125
GS
SK
y = 0.1361x + 276.38
R² = 0.0464
ns
y = 0.09x + 102.6
R² = 0.1802
P=0.05
0
50
100
150
200
250
300
350
400
450
0 100 200 300 400 500 600
2012-gsinWSplants(mmol.m-2.s-1)
2012- gs in WW plants (mmol.m-2.s-1)
X12_gs_WS50
X12_gs_WS20
2012 results – Apple Mtp

66. BUT the ranking of genotypes relative to each other for the three traits in either WW or WS
between the two periods was different (example of the number of nodes).
This would suggest that apart from the proper effect of the water regime, a plant
development effect (growth dynamics varying along the growing season) and/or other
environmental factors likely different between the two measurement periods, affected the
development of the shoot.
Number of nodes developed during the period,
either 50% or 20%
Pearson coefficient / Rank
correlation (Kendall’s t)
WW - 50% FTSW vs WW - 20% FTSW R=0.32, ns; t=0.17, ns
WS - 50% FTSW vs WS - 20% FTSW R=0.21, ns; t=0.15, ns
2012 results – Apple Mtp

67. The combination leaf temperature and fluorescence
provides a very good compromise between rapid and
effective assessment of the drought resistance of a given
phenotype.
WS protocol seems to be effective, simple for Leaf T°
and fluorescence (and duplicable ??)
Summary-APPLE
Ranking was different between the two years and
between stress conditions.
Ongoing discussions and analyses

68. 0,0
5,0
10,0
15,0
20,0
25,0
0,0
20,0
40,0
60,0
80,0
100,0
120,0
140,0
1 2 3 4 5 6 7 8 9 10 11 12
temperature(°C)
Rainfall(mm)
month 2012
rainfall temperature
2012 Results on peach
Harvest date
Surprised: 19 and 23 July 2012
Summergrand: 31 July 2012
No rootstock effect on fruit growth
But, fruit weight impacted …
In the same year or from n-1 year effect?

69. Publications
• Water scarcity
– Unibo communication at ISHS symposium on
« orchard management »in South Africa, Dec. 2012
– Joint statistical analyses between Unibo & Mtp:
1 common publication planned in 2013
• CR:
– Statistical analyses of 2011 and 2012 results currently
carriet out and publication planned, at least for apple
results

70.  linked to other projects
 technical improvements on forcing tests, and application to a
segregant progeny (genetic determinism of chilling requirement
trait)
 Validation of chilling models previously selected in apple by
AFEF Team, based on the forcing test results obtained in the two
hemispheres
 Apply of ‘one-bud cuttings’ test Research of new alternative
methods as NIRS Technology, based on the forcing test results in
France (French Project Perpheclim ACCAF)
Main challenges for 2013 (CR, Apple)

71. Apple:
1 – Joint analyses between Bologna and Montpellier
2 - Same protocoles and observations in 2013, as in 2011 and 2012.
No resilience experiment.
 Need to better study the ranking of genotypes in the various
water conditions and years
3 –Comparison of 1YO shoots between plants in a greenhouse and
plants in nearby outside conditions in Montpellier
+ possibly, at least for some of the variables, comparison with
adult-fruiting trees of the same genotypes in the field.
Peach:
New experiment in 2013 in Bordeaux (2nd year) and in Bologna (1st
year)
Main challenges for 2013 (Water
scarcity)

72. Interactions WP5.3 and the rest of the
project
• Interactions with other WPs of the project:
– Apply of ‘one-bud cuttings’ test to an apple
segregant progeny (genetic determinism of chilling
requirement trait)
– Apply water restriction to an apple core collection
from WP4

73. Publications
• Communications in different meetings in 2012
and planned for 2013
• Publication plan has to be discussed during this
meeting

74. Main challenges for 2013
• Complete sample analyses
• Analyze all data acquired
• Make synthetic analyses between years
• Merge results between partners
• Give conclusions on methods and tools
• Complete D5.2 (due February 2013)

75. Interactions between WP5 and the
rest of the project
• From WP5 to WP2, WP3, WP4
– phenotyping tools for WP2, WP3 and WP4
Ex: Monilinia resistance on peach ; crispiness and water scarcity
on apple
– provide interesting genitors to WP2
• Interactions planned with the stakeholders of the project
Test the phenotyping protocoles when available

76. Action Plan for 2013
To be discussed during this meeting
Should include many exchanges between
partners via mels, phone calls, visio conf
or workshop meetings

77. WP5 workshops
This afternoon:
• Task 5.2 _ 15:30 to 18:30 _ sala delegacions
• Task 5.3 _ 17:00 to 18:30 _ room 2.06
Tomorrow morning:
8:30 to 10 : 3 tasks separately