0.00

50.00

100.00

150.00

200.00

250.00

300.00

9520

5B

9522

9A

9529

1A

9521

2A

9520

9B

9521

5B

9524

2B

9527

5A

9524

1A

9524

8B

9522

7A

9527

4A

9522

6A

9524

4A

9530

5B

9529

5B

9527

1A

9523

4B

9523

9A

9523

9B

9521

1A

9526

1A

9529

2A

9526

8B

9528

0B

9528

1A

9528

6A

9521

7B

9528

4B

9526

1B

mon

eyA

9530

0B

9522

8A

9520

6B

9521

4A

9526

2B

9529

7B

9523

0A

9523

0B

9529

9A

t50

(hr)

0%

20%

40%

60%

80%

100%

9526

9A

9530

7A

9525

6B

9524

3A

9525

5B

9521

7A

9523

3A

9523

8B

9521

4B

9528

8B

9523

9A

9521

8A

9523

8A

9525

0A

9521

0B

9524

5B

mon

eyB

9528

7A

9527

5B

9526

8B

9528

3B

9527

4A

9524

4B

9521

1A

9527

9B

9530

5A

9526

6B

9522

5B

9528

3A

9530

2B

9524

6A

9525

3A

9522

2A

9525

4A

9522

0A

9525

1A

9527

6B

9530

6A

9521

5A

9526

3B

Ger

min

atio

n %

Genes for seed qualityA physiological genetical genomics approach to find genes for seed quality in tomato

Rashid Kazmi, Wilco Ligterink, Leo Willems, Noorullah Khan, Ronny Joosen and Henk W. Hilhorst

INTRODUCTION

Seed quality is defined as the ability of seeds to germinate under a wide variety of environmental conditions and to develop into healthy seedlings. Seed quality is determined by several features including genetic and physical purity, mechanical damage and physiological conditions (viability, germination, dormancy, vigour, uniformity etc.) (Dickson 1980, Hilhorst et al. 2006). We are trying to understand the mystery of seed quality by developing a multidisciplinary approach i.e. interlinking the physiology, genetics and genomics together.

To identify the problems related to seed quality at physiological, genetical and genomics level and to find the genes that are responsible for the intrinsic seed quality and to investigate their possible use for marker assisted breeding.

1- Development of molecular markers to aid in marker assisted breeding

2- Enable monitoring and prediction of seed quality during production and processing

3- Genetic modification to enhance seed quality

Genetical-genomics approach

We thank Syngenta and Incotec for their involvement in the current project and the STW users committee and all people in the seed physiology group for their expertise, advice, support and technical help.

Dickson, M. H. (1980). "Genetic aspects of seed quality." Hortic Sci 15: 771-774.Eshed Y and D. Zamir (1994) A genomic library of Lycopersicon pennellii in L. esculentum: A tool for fine mapping of genes. Euphytica 79: 175-179.Foolad, M. R. and G. Y. Lin (1999). "Relationships between cold- and salt-tolerance during seed germination in tomato: Germplasm evaluation." Plant Breeding 118(1): 45-48.Hilhorst, H.W.M., Bentsink, L. and Koornneef, M. (2006). Dormancy and Germination. InA.Basra, ed., Handbook of Seed Science and Technology. Haworth’s Food Products Press, Binghamton, New York. pp. 271-301.Monforte, A.J. and S. D. Tanksley (2000) Genome 43: 803-813.

OVERVIEW OF QTLs

GOALS & DELIVERABLES

ACKNOWLEDGEMENTS

REFERENCES

Fig. 2 The central dogma outlines the flow of information that is stored in a gene, transcribed into RNA and finally translated into protein. The ultimate expression of this information is the phenotype of the organism. Each step of the central dogma is accompanied by recent technological innovations that allow genome-wide analysis. Although the central dogma once presented a view that was essentially descriptive, and limited to gene-by-gene studies, it can now be coupled with technology and viewed as experimental and testable. Hypotheses can be formulated and revised for the purpose of elucidating the detailed connections between genotype and phenotype, therefore unravelling the complete molecular biology of an organism.

1. Continue to phenotype RIL population in more detail

2. More in depth QTL analysis

5. Grow and analyse S. pimpinelliffolium IBL lines, Microarray analysis, “likely candidate gene approach” and synteny with Arabidopsis to find the corresponding genes

Fig.1. Principles of mapping quantitative trait loci.The basic strategy behind mapping quantitative trait loci (QTL) is illustrated here. Two parents Solanum lycopersicum x Solanum pimpinellifolium that are genetically different are crossed to form a F1 population. A F1 individual is selfed to form a population of F2 individuals. Each F2 is selfed for six additional generations, ultimately forming several recombinant inbred lines (RILs). The RILs are scored for several genetic markers, as well as for the phenotype.

We are doing an extensive physiological and morphological study of 103 recombinant inbred lines (RILs) from a cross between Solanum lycopersicum x Solanum pimpinellifolium. Wild tomato species, such as S. habrochaitis, S. pimpinellifolium,and S. pennellii offer the genetic resource for cold, temperature, and water stress tolerance with respect to seed quality (Foolad and Lin, 1999). They generally have higher resistance to biotic and abiotic stresses and are frequently used in resistance breeding programmes. In addition to the RILs a collection of Lycopersicum pennellii-derived introgression lines (ILs) that together cover the entire genome in the background of S. lycopersicum Var. M82 (Eshed and Zamir 1994) and Solanum habrochaites ILs representing the genome of S. habrochaitisaccession LA1777 in the background of S. lycopersicum cv. E62039 (Monforte and Tanksley, 2000) will be assessed for seed quality associated traits.

OBJECTIVES

RIL populations

3. Start phenotyping IL populations

4. Genotyping RIL population and isolate HIFs (~NILs)

SYNTENY

(Orsi and Tanksley 2008))

Fig.5 QTLs found for different Germination traits of tomato seeds under abiotic stresses shown for one of the 12 chromosomes

Fig.3 a & b Germination percentage, rate and uniformity were used to evaluate the osmotic tolerance in the RIL population. QTL analysis was used to identify chromosome regions related to those traits.

Graphical representation of QTLs

Fig.6 Relationship of genes in tomato BAC containing Sw4.1 and corresponding syntenic regions in Arabidopsis genome.

Selfing

Repeated Selfing

F2

F1

Cross

x

x

P

Germ% 98% 97% 80% 82%

QTL for Germination %

Fig. 4 Graphical representation of the QTLs found by MAP QTL 5.0 with composite interval mapping with LOD 2.0.The vertical lines show the 12 chromosomes of tomato. Germination percentage is an estimate of the viability of a population of seeds. The germination rate (t50) is the time in which 50% of the seeds germinate.

Wageningen UR, Laboratory of Plant Physiology, Arboretumlaan 4, 6703 BD Wageningen, The Netherlands E-mail: Rashid.Kazmi@WUR.nl

QTLs for Germination rate (t50) at - 0.3 MPa PEG

0

0.5

1

1.5

2

2.5

3

3.5

LOD

0

5

10

15

20

QTLs for Germination % at - 0.3 MPa PEG

0

0.5

1

1.5

2

2.5

3

3.5

4

LOD

0

5

10

15

20

Germination % at - 0.3 MPa PEG with stratification

t50 at - 0.3 MPa PEG with stratification

Gene

mRNA transcript

Genotype

Transcriptome

Protein

Phenotype

Proteome

Transcript profiling

GenomeSequencing

Proteinfunction

attacgatataccacagacgaagaagaccgtaatcgaattgatgacgacgtataacgtactataatccaagagccatgggcttaaaccgttcatctaggttaaactggcttattataccccacagacgaagaa

AAAA

Central Dogma New Technology Genomic Hypothesis

35°C

PEG

NaC

l

H2 O

2

36°C

12°C

PEG 10d

35°C

PEG

NaC

l

H2 O

2

36°C

fresh

stratified

12°C

35°C

stratified

NaC

l

H2 O

2

36°C

fresh

seed weight

Germination % u7525t5012°C

03_TG40_3.00.0

03_E35/M60-5e27.2

03_TG74-1_3.5749.8

03_E36/M51-13e55.6

03_TG214_3.11583.2

1_11_2

2_1

4_14_2

5_1

6_16_2

12_2

17_1

19_1

20_1

Chromosome 3

3_2

msat1000080.0t1g113.5

f21m129.7

ind499215.4ind637519.0msat1.1021.6msat10819326.6

nga24832.4

ind113638.7

t27k1249.1

msat1.4254.7

nga12861.3ind218863.8dcapsapr266.1f5i1469.6

msat1.1376.3

msat12708882.7

msat1.591.3

x

F7