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 1058.54@30.00 1009.09@30.00 [ 265.00- 2000.00]

400 600 800 1000 1200 1400 1600 1800 2000m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Rela

tive A

bu

nd

anc

e

935.3

862.0

1187.3

852.9

957.21522.2

991.0 1318.3

830.2

773.4

1609.4515.3

761.8 1722.4495.2 1336.5748.1 1759.2

342.1 1888.41205.2534.2

Sainsbur y5050NLM S3#3483 RT:36.82 AV: 1 NL: 3.63E4T: ITMS + c N SI d F ull ms3 1058.54@30.00 1009.09@30.00 [ 265.00- 2000.00]

400 600 800 1000 1200 1400 1600 1800 2000m/z

Sainsbur y5050NLM S3#3483 RT:36.82 AV: 1 NL: 3.63E4T: ITMS + c N SI d F ull ms3 1058.54@30.00 1009.09@30.00 [ 265.00- 2000.00]

400 600 800 1000 1200 1400 1600 1800 2000m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Rela

tive A

bu

nd

anc

e

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

100

Rela

tive A

bu

nd

anc

e

935.3

862.0

1187.3

935.3

862.0

1187.3

852.9

957.21522.2

991.0 1318.3

830.2

773.4

1609.4515.3

761.8 1722.4495.2 1336.5748.1 1759.2

342.1 1888.41205.2534.2

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

-0.4

-0.2

0

0.2

0.4

0.6

0 6 12 18 24(ZT) Hours

PC

3

Circadian Metabolomics

FT-IC-MS

Component 3 vs Component 1

Comp.1

Co

mp

.3

-20 -10 0 10 20 30

-20

-10

01

0

10811011612913013115216

165173179272285299

33133

3403483537

37153919699C

L108L110L116L129L130L131L152L16

L165L166L173L179L229L272L285L299

L3L31L33

L340L348L35L36L37L53L57L91L96L99LC

LSCSC

Infrared imaging

-30 -20 -10 0 10 20 30 40 50 60 70-40

-30

-20

-10

0

10

20

30

40

Component 1

Com

pone

nt 2

Principal components 1 and 2 800-1800 wl, label genotype autoscaled

2002

2004

-6 -4 -2 0 2 4 6 8-6

-4

-2

0

2

4

6

8

Component 9

Com

pone

nt 1

0

Principal components 9 and 10 800-1800 wl, label genotype autoscaled

2002

2004

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!

0.1

0.3

0.1

0.4

0.2

0.0

3800 1950 6502950

What's different ?

R2 = 0.9111

32

37

42

47

52

57

62

67

72

77

32 37 42 47 52 57 62 67 72 77

Actual DMD

Pre

dic

ted

DM

D

PLS-2 modelling of Py-GC-MS (TIC) data for DMD..

Complex trait analysis via“Reverse data modelling”

e.g. dry matter digestibility

7

11

22

44

55

66

8899

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

11

22

44

55

66

88

99

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

7

11

22

44

55

66

8899

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

11

22

44

55

66

88

99

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

7

11

22

44

55

66

8899

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

11

22

44

55

66

8899

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

11

22

44

55

66

88

99

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

11

22

44

55

66

88

99

1111

1313

12121414

1717

1919

2020

3232

33332424

2525

2626

2828

Fig.2a

Fig.2b

Factor loadingsplotted fromCalibrationmodel

Py-GC-MSTIC data.

Tools for breeding

Typesof Lignin

OH

O-CH3

G-lignin fragments

OH

O CH3

CH

CH2

0

100

200

300

400

500

600

700

800

2.14 2.24 2.4 2.4 2.5 2.53 2.75 2.77 2.77 2.9 2.9 2.95 3.23 3.72 3.74

Mill

ion

s

Actual Lignin (%)

Ch

rom

ato

gra

ph

pe

ak

un

it a

rea

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

113/26633/10

36

17

83

92

3/2

99

83

19 563/23

2/3

18

Parental0.1

0.3

0.1

0.4

0.2

0.0

3800 1950 6502950

Only polar fraction has currently been analysed

Single gene effects have global consequencesdetectable by Metabolomics.

Monocot seedling screening ??

-400

-300

-200

-100

0

100

200

300

400

-600 -500 -400 -300 -200 -100 0 100 200 300 400 500 600

t[2]

t[1]

enqi401 all data.M1 (PCA-X), all data set ctrt[Comp. 1]/t[Comp. 2]Colored according to values in variable enqi401 all data

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

gm-5-5

gm-2-3

gm-1-5

gm-5-2gm-2-1

gm-4-4

gm-4-3gm-4-1

gm-5-1

gm-2-4

gm-1-2

gm-2-2

gm-1-1

w t-1-3

w t-3-5

w t-1-4 w t-1-1

w t-3-2

w t-1-2

w t-3-3

w t-1-5

w t-3-1w t-3-4

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”)