The use of metabolomics approaches to mine

the genetic diversity in tomato.

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Dr. Arnaud Bovy

Genetic variation in classical Plant Breeding

Genetic variation is the existence of variation in genetic material within a species or between a crossable species

Basis is mutation

8

Breeding for quality

Quality traits:

Flavour

Fragrance

Nutritional value

Colour

Shelf-life

OHO

OH O

OH

OH

OH

CO2H

HO

CO2H

HO

OHO

OH

OH

OH

OH

OHO

OH

OH

OH

OH

OHO

OH O

OH

OH

OH

OH

O OH

OHHO

H3C O

OCH3

C H 3 O

O C H 3 H 3 C O O H

H O

C H 3

C H 3

C H 3 C H 3

C H 3 C H 3 C H 3

H 3 C

C H 3

C H 3

O

H

OH

H

OH

OH

H

HO

HH

CH2OH

HO

O

OHHO

HCOH

CH2OH

O

CH3

CH3

CH3 CH3 CH3

CH3 CH3 CH3

H3C

CH3

H2N

O

OH

CO2H

H2N

O

OH

O NH2

PO4-

Ca2+

HO

O

Many quality traits are determined by metabolites

‘Rich Food’: rich in secondary metabolites

Primary metabolites

Are present in all plant cells and are essential for growth and development, eg sugars, amino acids,….

Secundary metabolites

(apparently) not essential for growth and development

Presence limited to specific compound classes or organs, tissues

How are secondary metabolites produced? CO2 + H2O

Sugars

Fotosynthesis

Amino acids Acetyl CoA Proteins

Fatty acids Terpenoïds Phenolics Alkaloïds

Saponins Glucosinolates

Respiration

Which secondary metabolites do we know?

Flavonoids, terpenoids, carotenoids, alkaloids, glucosinolates………

Large groups of compounds:

> 5.000 flavonoids

> 20.000 terpenoids

> 100.000 sec. metabolites in Plant Kingdom

Why does the plant produce sec. metabolites?

Pathogen resistance

Terpenoids, alkaloids, flavonoids

Cope with abiotic stress (UV, cold, drought, salt)

Flavonoids, vitamin C, vitamin E (antioxidanten)

Attract pollinators and seed dispersers

Terpenoids (fragrance)

Flavonoids en carotenoids (colour)

Sec. metabolites are BIOACTIVE compounds

Systems genetics: linking genotypes to phenotypes

bioinformatics databases

DNA mRNA Protein Metabolite Phenotype

Terminology

Metabolomics:

Technology aimed at obtaining a broad as possible insight in the biochemical make-up of a biological product

Large scale – datastream requires in silico approach

Tomato- 94 varieties, 203 analyses, 4.06 million data points, some Gb data – for one type of analysis!

Metabolomics: what can you do with it?

Get more insight in complex traits

Taste

Fragrance

Nutritional value

Acquire detailed knowledge on the influence of genetic or environmental factors on the biochemical composition

Processing

Storage

Tracking & tracing: markers for authenticity, origin, quality

Tool to monitor genetic diversity......

Metabolomics

Non-targeted analyses

Detect as many as possible compounds in a given extract

Targeted analyses

Analyses optimised for selected (groups of) compounds

GC-MS LC-MS

extraction

robot

WU-NMR

Technology platform Wageningen

RT: 0.00 - 48.21

0 5 10 15 20 25 30 35 40 45

Time (min)

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Rela

tive A

bundance

11.77

18.9413.86

15.80

34.30

17.8638.3828.91 32.319.30 23.8721.64

26.62 31.82

6.88 35.67 46.325.962.73 45.1239.52

NL:4.17E7

TIC MS PPO 63 Aromatica complet

GC-MS

LC-MS

Headspace SPME-GC-MS

LC-QTOF-MS

Metabolomics

Comparative metabolomics

sample A

sample B

A B

A B

Bioinformatics for metabolomics

2. 3.

4. 1.

Breeding for quality: general strategy

Explore genetic diversity

Collections and breeding populations

Link markers and genes to traits

Unravel metabolic pathways

Metabolomics/Biochemistry

Gene isolation

Modify the composition

Marker-assisted breeding

Genetic engineering

Bioconversion

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Research examples

Unravel the taste of tomatoes

Screen metabolic diversity in wild tomato accessions

Collection of wild relatives

Introgression line population

Unravel the taste of tomatoes

Genes & markers Taste & Flavour

Metabolites

Characterisation of commercial tomato germplasm: 94 cultivars

Gas Chromatography-Mass Spectrometry (GC-MS)

Liquid Chromatography-Mass Spectrometry (LC-MS)

Volatiles, sugars & acids Semi-polar compounds

Sensory panel

B004

B005

B006

B009

B011

B012

B013

B014

B027

B031

B053

B055

B056

B057

B072

B073

B080

B091B098

B118

B121

B122

B123

C003

C021

C030

C033C035

C058

C059

C074

C079

C083

C085

C095

C096

C100

C110

C111

C112

C132

R001

R002

R007R008 R010

R015 R016R017R018

R019 R020

R022

R023

R024

R025

R026

R028

R029

R032

R034

R036

R037

R038

R039R040

R041

R043

R044

R045

R046

R047

R048

R049

R050

R061

R068

R070

R071

R075

R081

R086R087

R088

R089

R090

R092

R093

R094

R097

R099

R102

R104

R119

R120

R124

R125

R126

R127

R131

S_scent_aromaintensity

S_scent_tomato

S_scent_spicy

S_scent_sweet

S_scent_smoky

S_taste_pungent_scherp

S_taste_sweet

S_taste_sour

S_taste_tomato

S_taste_earth_gronderig

S_taste_unripe

S_taste_spicyS_taste_watery

S_mouthfeel_contract

S_mouthfeel_moist

S_mouthfeel_mealy_melig

S_mouthfeel_solid

S_mouthfeel_toughskin_taaieschil

S_aftertaste_sweet

S_aftertaste_sour

S_aftertaste_salty

S_aftertaste_bitter

S_aftertaste_fresh

S_aftertaste_chemical_perfume

S_aftertaste_rough_stroef

55%

Sensory data

Metabolic contrast based on 300 volatiles

2-methoxyphenol

methylsalicylate

Phenylethanol

Cherry

round

beef

reference-samples

Tikunov et al. (2005) Plant Physiology 139: 1125

Relations between sensory traits and metabolites

Understanding the metabolic basis for taste speeds up

the identification of the underlying pathways and key genes

Flavour QTL regions

Metabolite QTLs Sensory & Metabolite QTLs

1 2 3 4 5 6 7 8 9 10 11 12

• Smoky aroma

• Smoky taste

• Guiacol

• Methylsalicylate

• Eugenol

Into the wild!

S. chmielewskii

S. cheesmaniae S. habrochaites

S. huaylasense

S. neorickii

S. pennellii

S. peruvianum

S. pimpinellifolium

http://tgrc.ucdavis.edu/

Screening a collection of wild relatives

Materials:

80 S. pimpinellifolium

20 S. galapagensis

20 S. cheesmanii

Approach:

Genotyping (SNP’s) and phenotyping (LC & GC-MS)

QTL/Association analysis

Bootstrap analysis

5000 SNP markers 300 metabolites

Hypothesis:

When genetic diversity is low, metabolic diversity may be more informative

S. galapagense

S. chmielewskii ILs

x

S. lycopersicum S. chmielewskii

.........

IL01a

IL01b

IL12d

F1

BCn

1 2 3 4 5 6 7 8 9 10 11 12

Chromosomal order wt

LC-PDA-QTOF-MS/MS

Pink tomatoes: an Asian growth market

■ Moneyberg ○ IL1b

Stages of ripening

Green BreakerTurning RipeC

on

ce

ntr

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(mg

Kg-1

FW

)

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Stages of ripening

Green BreakerTurning Ripe

Co

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Naringenin chalcone

Flesh

Stages of ripening Stages of ripening

Peel

Stages of ripening

Green BreakerTurning RipeC

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Stages of ripening

Green BreakerTurning Ripe

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Flesh

Stages of ripening

Green BreakerTurning Ripe

Rel. e

xpre

ssio

n

0.0

0.5

1.0

1.5Peel

Stages of ripening

Green BreakerTurning Ripe

Rel. e

xpre

ssio

n

0

200

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800 Peel Flesh

Stages of ripening Stages of ripening

MYB transcription factor

What breeders want: molecular markers

The gene is the best possible marker

First tomato varieties on the Asian market in less than 4 years!

+ + - Marker -

Conclusions

Huge phenotypic variation in the (commercial) tomato germplasm

Metabolomics is a useful tool to get insight in complex traits such as flavour, colour and health potential

Combining metabolomics and genetics leads to the identification of markers for mQTLs

Metabolomics may be an alternative or complementary tool for biodiversity studies Unbiased May reflect functional variation In case genetic variation is very low BUT metabolites reflect genetics x environment