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The yin and yang of meta- phenomics. Hendrik Poorter , Frank Gilmer & Uli Schurr JPPC, FZJ, DE. JPPC: Jülich Plant Phenotyping Centre. 1. Quality control before data enter the database 2. Extracting biological knowledge from the database. Two topics:. 1. Environmental control. - PowerPoint PPT Presentation
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Hendrik Poorter,Frank Gilmer& Uli SchurrJPPC, FZJ, DE
The yin and yang of meta-phenomics
JPPC: Jülich Plant Phenotyping Centre
1. Quality control before data enter the database
2. Extracting biological knowledge from the database
1. Environmental control
2. Data collection
3. Deduction of information
4. Deduction of knowledge
Two topics:
1. Quality control:
Quality targets for experiments:• Setup SOPs (standard operation procedures).
• SOPs for – reproducible – growth environments.
• SOPs for – reproducible - plant analysis.
• Collect information (history, phenotype) for every
individual in a plant information database.
• Reliable, reproducible, transparent
The biologist’s perspective:plant size variability is an issue
80 100 120 140 160 180 200 220 2400
2
4
6
8
# of
obs
erva
tions
Water loss (rel. units) Plant variability across labs:Massonnet et al. (2010) Plant Phys.
What variability can one expect?
0.1 0.3 0.5 0.7 0.9 1.10
10
20
30
P75 = 0.46P50 = 0.30P25 = 0.21
%
slnW
n = 600 Mean = 1.41
n = 800
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.00
5
10
15
20
25
30
Freq
uenc
y (%
)
W700 / W 350
A meta-analysis of the effect of elevated CO2:350 experiments with 800 mean values
for 350 species
Poorter & Navas (2003) New Phytol.
600 estimates of variability in plant size(standard deviation ln-transformed weight)
Poorter & Garnier (1996) J. Exp. Bot.
Could the variation in growth response to elevated CO2 be explained simply by plant-to-plant variability?
SDlnW# of
plants
P20 0.21 4
P50 0.31 8
P80 0.51 12
1. Assume a true W700/W350 of 1.41
2. Draw at random 4, 8 or 12 plants
from a population with 3 variabilities:
B
W700/W350 ?
90.000 simulations
Yes, all observed variation in growth response could just happen to be caused by sampling too few individuals from too variable experimental populations:
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.00
5
10
15
20
25
30
Freq
uenc
y (%
)
Weight Ratio
Poorter & Navas (2003) New Phytol.
Conclusion 1:
- Quality control in your procedures is an issue
- Biological variation is an equally important issue, and growth chambers are NOT solving this problem
2. Extracting biological knowledge from the database:
At the phenotypic level, there is – for plants –a lack of information structured in a database:
TAIR, PLEXdb, Genevestigator,Drastic, CSB,DB, Germinate
LedaGlopnetTRY
(TurboVeg)
? (Floral DB)
Chloroplast 2010, Germinate
How do plants respond to their environment?
Investigator A:Arabidopsis
Trait xlow light 20 units
high light 40 units
Investigator B:Brassica
Trait xlow light 60 unitshigh light 60 units
The 2 experiments may actually tell the same thing:
The classical dose-response curve:
Nutrient supply
Yiel
dMitscherlich (1909)
The example ofSLA vs Light:
Light intensity
Irradiance
PFDPPFD
PAR
PFR
µmol m-2 s-1
mol m-2 day-1
W m-2
luxcal cm-2 s-1
langley min-1
lumen foot-2
MJft-c
SLA
SLW
SLM
LSM
LSW
LMAg m-2
mg cm-2
µg cm-2
µg mm-2
m2 kg-1
dm2 g-1
mm2 mg-1 cm2 mg-1
Ma
SLA: leaf area / leaf dry mass
How can we achieve a clear picture from fragmented data?:
SLA(m2 kg-1)
Daily Photon Irradiance(mol m-2 day-1)
A literature analysis of >1100 data points (mean values) from >150 experiments on >300 species:
DPI (mol m-2 day-1)
SLA
(m2 k
g-1)
0 10 20 30 40 500
20
40
60
80
100
DPI (mol m-2 day-1)
Rice & Bazzaz (1989) Ingestad et al. (1994) Sharew et al. (1996) Evans & Poorter (2001)
SLA
(m2 kg-1 )
Four different experiments show that interspecific variation in SLA is large:
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
After scaling SLA relative to the (interpolated) value at a reference light intensity of 8 mol m-2 day-1:
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 10 20 30 40 500.0
0.5
1.0
1.5
2.0
DPI (mol m-2 day-1)
SLA
(sca
led)
>1000 data points from >150 experiments on >300 species:
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
10 20 30 40 500.25
0.5
1
2
4
DPI (mol m-2 day-1)
SLA
(sca
led)
Median and the interquartile range for 7 light classes:
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 10 20 30 40 500.25
0.5
1
2
4
SLA
(sca
led)
DPI (mol m-2 day-1)P10
P90
P50
10th and 90th percentiles give norm values, by which you can compare new experiments: The red line is an example of an outlying experiment
0 10 20 30 40 500.25
0.50.5
11
22
44
88
P90
Ann. Bot. (1986)
SLA
(sca
led)
DPI (mol m-2 day-1)
P10
Terminalia ivorensis
1. Light quantity (DPI) 2. Light quality (R/FR) 3. UV-B 4. CO2
5. O3
6. Nutrient availability (N, P, G) 7. Drought stress 8. Waterlogging 9. Submergence10.Temperature11. Salinity12. Soil compaction
Stress box
Can we follow the same approach for other environmental factors?
SLA responses to light, gases, and nutrients:
0 10 20 30 400.5
1
1.5
2a
SLA
(sca
led)
DPI (mol m-2 day-1)0.2 0.4 0.6 0.8 1.0 1.2
b
R / FR0 5 10 15 20
c
UV-B (kJ m-2 day-1)
0 200 400 600 800 10000.5
1
1.5
2d
SLA
(sca
led)
CO2 (µmol mol-1)0 20 40 60 80 100
e
O3 (nmol mol-1)0.2 0.4 0.6 0.8 1.0 1.2
f
Nutrient availability (rel. scale)
1000 50 30
700 150 600
SLA responses to water, temperature, salinity and soil compaction:
0.0 0.2 0.4 0.6 0.8 1.00.5
1
1.5
2 g
SLA
(sca
led)
Water availability (rel. scale)0.2 0.4 0.6 0.8 1.0
h
Waterlogging0.0 0.2 0.4 0.6 0.8 1.0
i
Submergence
5 10 15 20 250.5
1
1.5
2j
SLA
(sca
led)
Temperature (°C)0.0 0.2 0.4 0.6 0.8 1.0
l
k
Salinity (rel. to seawater)1.1 1.2 1.3 1.4 1.5 1.6
Soil Compaction (g cm-3)
300 90
70
300 200 70
An overall non-linear equation to describe the response:
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 10 20 30 400
20
40
60
80
100
0 20 40 60 800.2
0.5
1.5
4 S
LA (s
caled
)
Poorter et al., Fig. 1
DPI (mol m-2 day-1)
3202 640201 ...)(log DPISLAsc
r2 = 0.72; PI = 2.92
Plasticity index: highest divided by lowest fitted value across a predefined range
Range PI
Irradiance 1 – 50 mol m-2 day-1 2.92CO2 200 – 1200 µmol mol-1 1.41Salinity 0 – 100 % seawater 1.23Waterlogging - – + 1.12Compaction 1.0 – 1.6 g cm-3 1.05
R : FR 0.2 – 1.2 mol mol-1 1.00
UV-B 1 – 20 kJ m-2 day-1 1.00
O3 5 – 100 nmol mol-1 1.00
Nutrients 0.05 – 1 rel. units 1.13Water 0.05 – 1 rel. units 1.25
Submergence - – + 1.95Temperature 5 – 35 oC 2.24
- Species family / name- woody / herbaceous- deciduous / evergreen- shrub / tree- annual / perennial- N2 fixing- C3 / C4 / CAM
Species traits
- Glasshouse, Growth chamber, OTC, Garden- Light (DPI)- Temperature (24h-average)- Substrate (hydroponics / soil, pot volume)
Growth environment
- Shade / Sun- Dry / Wet- Cold / Warm- Non-saline / Saline
Environmental niche
Are there differences between subgroups?
An example: tropical species are more plastic than boreal species
5 10 15 20 250.5
1
2 low-temp species intermediate high-temp species
SL
A (s
caled
)
Temperature (°C)
Growth chamber
Glasshouse
OTC, shade house
Functional groups
Most experiments with herbs were in growth chambers,most with trees were outside in shade houses:
SLA is just one trait, can we do the same for other traits?:
Env. Factor SLA Trait 2 Trait 3 Trait 4 .... Trait n1 2 3 4 5 6 ... 12
0 10 20 30 400.5
1
2
LM
F (s
caled
)
DPI (mol m-2 day-1)
PI = 1.22n = 400
Yes, for example the % allocation of biomass to leaves as dependent on light intensity:
- Yield- RGR, ULR, LAR- SLA- LMF, SMF, RMF, (HI)
Growth box (> 4)
- PHOT actual- PHOT capacity (/m2, /g, /N)- gs,Transpiration, - ci/ca
- J / Vmax
- RESP leaf, stem, root, fruit (/g)
Gas exchange (> 3)
- [C], [N], [P] leaf, stem, root, fruit- Starch, Fructan- Nitrate- Sol. Sugars- Lignin- (Hemi-)Cellulose- Protein, Org. N- Org. acids- Minerals, Ash- Sol. Phenolics- Tannin- Construction costs- Delta 13C
Chem. comp. (> 5)
- leaf size- plant height- leaf thickness- leaf density (or FW / DW)- vol / % epidermis, mesophyll air spaces, sclerenchyma- cell size
Morphology / anatomy (> 3)
- Rubisco capacity- PEP carboxylase- SBPase- AGPase- NR- etc
Enzyme box (> 4)
- link to mRNA
Do the same for these plant traits:
Conclusions:
► Is able to summarise data across many experiments
► Yields quantitative response curves
► As well as normal limits
This meta-phenomics approach :
► Is applicable to (almost all) environmental factors
► Is applicable to all plant traits
► Will be very useful for modeling (global change,
limiting factors)
Frank Gilmer, FZJ
Uli Schurr, FZJ
Thanks to:
for more info see: - J. Exp. Bot. (2010) 61: 2043-2055 - www.metaphenomics.org
Ismael ArandaOwen AtkinCorine de GrootYulong FengJurg FranzaringKeith FunnellYaskara HayashidaVaughan HurryKen Krauss
Dina RhonzinaFrancesco RipulloneCatherine RoumetPeter RyserDylan SchwilkSusanne TittmannJan Henk VenemaRafael Villar
Dina RhonzinaFrancesco RipulloneCatherine RoumetPeter RyserDylan SchwilkSusanne TittmannJan Henk VenemaRafael Villar
Gerard Bönisch, MPI-Jena
Benjamin Bruns, FZJ