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The yin and yang of meta-phenomics HendrikPoorter, Frank Gilmer & Uli Schurr JPPC, FZJ, DE
1. Quality control before data enter the database2. Extracting biological knowledge from the database Two topics: 1. Environmental control 2. Data collection 3. Deduction of information 4. Deduction of knowledge
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 Plant variability across labs: Massonnetet al. (2010) Plant Phys.
What variability can one expect? 600 estimates of variability in plant size (standard deviation ln-transformed weight) Poorter & Garnier (1996) J. Exp. Bot. A meta-analysis of the effect of elevated CO2: 350 experiments with 800 mean values for 350 species Poorter & Navas (2003) New Phytol. n = 600 Mean = 1.41 n = 800
1. Assume a true W700/W350of 1.41 2. Draw at random 4, 8 or 12 plants from a population with 3 variabilities: Could the variation in growth response to elevated CO2 be explained simply by plant-to-plant variability? 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: 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
At the phenotypic level, there is – for plants –a lack of information structured in a database: Leda Glopnet TRY (TurboVeg) ? (Floral DB) Chloroplast 2010, Germinate TAIR, PLEXdb, Genevestigator, Drastic, CSB,DB, Germinate
How do plants respond to their environment? Investigator A: Arabidopsis Investigator B: Brassica
The classical dose-response curve: Mitscherlich (1909) Yield Nutrient supply
The example ofSLA vs Light: g m-2 SLA: leaf area / leaf dry mass LMA mg cm-2 mm2 mg-1 cm2 mg-1 µg cm-2 LSW W m-2 µg mm-2 dm2 g-1 LSM Ma µmol m-2 s-1 lumen foot-2 m2 kg-1 cal cm-2 s-1 mol m-2 day-1 lux MJ SLM langley min-1 ft-c SLA Irradiance PAR SLW Light intensity PPFD PFR PFD
How can we achieve a clear picture from fragmented data?: SLA (m2 kg-1) Daily Photon Irradiance (molm-2day-1)
A literature analysis of >1100 data points (mean values) from >150 experiments on >300 species: SLA (m2 kg-1) DPI (mol m-2 day-1)
Four different experiments show that interspecific variation in SLA is large:
After scaling SLA relative to the (interpolated) value at a reference light intensity of 8 mol m-2 day-1:
10th and 90th percentiles give norm values, by which you can compare new experiments: The red line is an example of an outlying experiment Terminaliaivorensis
Stress box 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. Submergence 10.Temperature 11. Salinity 12. Soil compaction Can we follow the same approach for other environmental factors?
SLA responses to light, gases, and nutrients: 1000 50 30 700 150 600
SLA responses to water, temperature, salinity and soil compaction: 300 90 70 70 300 200
An overall non-linear equation to describe the response: r2 = 0.72; PI = 2.92
Plasticity index: highest divided by lowest fitted value across a predefined range
Species traits Environmental niche Growth environment - Species family / name - woody / herbaceous - deciduous / evergreen - shrub / tree - annual / perennial - N2 fixing - C3 / C4 / CAM - Shade / Sun - Dry / Wet - Cold / Warm - Non-saline / Saline - Glasshouse, Growth chamber, OTC, Garden - Light (DPI) - Temperature (24h-average) - Substrate (hydroponics / soil, pot volume) Are there differences between subgroups?
Most experiments with herbs were in growth chambers,most with trees were outside in shade houses: Growth chamber Glasshouse OTC, shade house Functional groups
Yes, for example the % allocation of biomass to leaves as dependent on light intensity: PI = 1.22 n = 400
Chem. comp. (> 5) Growth box (> 4) 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 - Yield - RGR, ULR, LAR - SLA - LMF, SMF, RMF, (HI) - PHOT actual - PHOT capacity (/m2, /g, /N) - gs,Transpiration, - ci/ca - J / Vmax - RESP leaf, stem, root, fruit (/g) Do the same for these plant traits: Enzyme box (> 4) • Rubisco capacity • PEP carboxylase • - SBPase • - AGPase • - NR • - etc Morphology / anatomy (> 3) - leaf size - plant height - leaf thickness - leaf density (or FW / DW) - vol / % epidermis, mesophyll air spaces, sclerenchyma - cell size - link to mRNA
Conclusions: This meta-phenomics approach : ► Is able to summarise data across many experiments ► Yields quantitative response curves ► As well as normal limits ► Is applicable to (almost all) environmental factors ► Is applicable to all plant traits ► Will be very useful for modeling (global change, limiting factors)
Dina Rhonzina Francesco Ripullone Catherine Roumet Peter Ryser Dylan Schwilk Susanne Tittmann Jan HenkVenema Rafael Villar Thanks to: Frank Gilmer, FZJ Uli Schurr, FZJ Gerard Bönisch, MPI-Jena Benjamin Bruns, FZJ Dina Rhonzina Francesco Ripullone Catherine Roumet Peter Ryser Dylan Schwilk Susanne Tittmann Jan HenkVenema Rafael Villar IsmaelAranda Owen Atkin Corine de Groot YulongFeng JurgFranzaring Keith Funnell YaskaraHayashida Vaughan Hurry Ken Krauss for more info see: - J. Exp. Bot. (2010) 61: 2043-2055 - www.metaphenomics.org