Mapping metabolic data to genetic information “ Metabolomics” “Metabonomics” Simon C Thain A...
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Transcript of Mapping metabolic data to genetic information “ Metabolomics” “Metabonomics” Simon C Thain A...
Mapping metabolic
data to genetic information
“Metabolomics”“Metabonomics”
Simon C ThainA practical tool for trait discovery & analysis ?
Sainsbur y5050NLM S3#3483 RT:36.82 AV: 1 NL: 3.63E4T: ITMS + c N SI d F ull ms3 [email protected] [email protected] [ 265.00- 2000.00]
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Sainsbur y5050NLM S3#3483 RT:36.82 AV: 1 NL: 3.63E4T: ITMS + c N SI d F ull ms3 [email protected] [email protected] [ 265.00- 2000.00]
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Sainsbur y5050NLM S3#3483 RT:36.82 AV: 1 NL: 3.63E4T: ITMS + c N SI d F ull ms3 [email protected] [email protected] [ 265.00- 2000.00]
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How can Metabonomics help in trait analysis?
• Model species to crops.
• Better germplasm ID & trait definition (tools for breeding).
• Mapping metabolite patterns to genetic information can provide direct cause and effect data.
“calibrations”“fingerprint”
Using metabolomic data for trait identification and
mapping
• Quantification and qualification of “Phenotype” /complex traits and QTLs; reduces non-parametric descriptions e.g. “Vigour” “tolerance”.
• Statistical association of multiple metabolite changes or “fingerprints” to alleles, point mutations (Tilling), markers, introgressed DNA etc.
Using the right tools
Chemometric/Statistics
Rapidity
SensitivityCompound
Specificity/Structure
FT-M
SLC/GC-MS
NMRX-ray
TOF-MS
FT-IRDI-MS
Reproducibility
High-throughput
Environmental variables and sampling scales ??
Dusk
Mid day
Mid morning
Midnight
Dawn
Principle Component Analysis (PC 1,2,&3) of Circadian FT-IC-MS data
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PC
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Circadian Metabolomics
FT-IC-MS
Component 3 vs Component 1
Comp.1
Co
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10811011612913013115216
165173179272285299
33133
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37153919699C
L108L110L116L129L130L131L152L16
L165L166L173L179L229L272L285L299
L3L31L33
L340L348L35L36L37L53L57L91L96L99LC
LSCSC
Infrared imaging
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Principal components 1 and 2 800-1800 wl, label genotype autoscaled
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Principal components 9 and 10 800-1800 wl, label genotype autoscaled
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Infrared fingerprinting
Weather/Season Cycles Tissue
Look for vectors/patters; modulate conditions to “stimulate” the metabolomic consequences
genotype
70%
Under grant application Under trial with Varian UK
>1%
Are they different ?
1
Factor 2
DFA analysis identified the chemical fingerprints 14 forage grasses
Metabolomic fingerprinting of grass varieties by FT-IR
Metabonomics relationships between forage grass varieties.e.g. Cell wall carbohydrates
Genotypes clusters – rapid, quantitative cheep!
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What's different ?
R2 = 0.9111
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Actual DMD
Pre
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PLS-2 modelling of Py-GC-MS (TIC) data for DMD..
Complex trait analysis via“Reverse data modelling”
e.g. dry matter digestibility
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Fig.2a
Fig.2b
Factor loadingsplotted fromCalibrationmodel
Py-GC-MSTIC data.
Tools for breeding
Typesof Lignin
OH
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G-lignin fragments
OH
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G Lignin S Lignin H Lignin Unknown Lignin
FT-IR metabonomic fingerprinting of Wheat nullisomic/tetrasomic lines
Roy Goodacre, Lunned Roberts, David Ellis, Danny Thorogood, Stephen Reader Ian King
• Wheat contains 3 genome sets (A, B, C) of 14 chromosome each.
• Group 1 chromosome are syntenic (carry the same genes or alleles in the Same order)
• Metabolomic fingerprinting could detectthe loss of each alternate Chromosome 1 pair.
What changes? If we know then new breeding targets can be identified
Metabolomic mapping in Lolium/Festuca Chromosome 3
substitution lines
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Only polar fraction has currently been analysed
Single gene effects have global consequencesdetectable by Metabolomics.
Monocot seedling screening ??
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Ellipse: Hotelling T2 (0.95)
Series (Settings for Var_1)
Missing gm wt
gm-4-2
gm-5-3
gm-2-5gm-4-5
gm-5-4
gm-1-4
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SIMCA-P+ 10.5 - 05/04/2005 15:35:01
wt bm
4.10 3.51 4.26 3.82 4.18 3.79 4.53 3.75 4.13 3.94 4.55 3.78 Mean 4.29 3.77 SE 0.20 0.14 P(T<=t) two-tail 0.000497
Figure 1. Photograph of 4 week-old maize leaf midribs from the second emergent leaf.
Primary metabolite fingerprinting via NMR
Metabolite mapping to SSR, SNP AFLPin isogenic/inbred lines.
•Less likely to miss “invisible” phenotypes.
•Large numbers of false positives
Perspectives
• Metabonomic trait analysis approaches can be rapid sensitive and informative of genotype & function.
• Metabolomic analysis methods, need not always be confined to controlled environments.
Acknowledgements:IGER
Iain DonnisonPhillip Morris
Sarah HawkinsCathy Morris
Collaborators & matterials:MeTRo
Romani Fahime (Aston)Deri Tomos (Bangor)
Ian King (IGER)
EPSRC, BBSRC
IRlight
Fourier-transformation
produces spectrograph
Fourier-transformation
produces spectrograph
Fourier-Transform InfraredSpectroscopy (“FT-IR”)