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Taking an Integrated Approach for Plant Germplasm Characterization and Utilization Ming Li Wang Molecular and Biochemical Genetics Laboratory PGRCU Curators.

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Presentation on theme: "Taking an Integrated Approach for Plant Germplasm Characterization and Utilization Ming Li Wang Molecular and Biochemical Genetics Laboratory PGRCU Curators."— Presentation transcript:

1 Taking an Integrated Approach for Plant Germplasm Characterization and Utilization Ming Li Wang Molecular and Biochemical Genetics Laboratory PGRCU Curators Workshop at Atlanta, February 3, 2010

2 Our Mission for Plant Germplasm Research Utilization

3 An Integrated Approach for Plant Germplasm Research ► Genetic analysis of sweet sorghum germplasm ► Genetic and biochemical analysis of peanut germplasm

4 Sorghum Grain sorghum (Starch) Forage sorghum (Biomass) Sweet sorghum (Sugars) Sorghum as Feedstock for Bioethanol Production

5 PretreatmentFermentation Products Feedstock Grain sorghum (Starch) Sweet sorghum (Sugars) Hydrolysis Squeeze Glucose Fructose Sucrose C5 sugars Animal feed Ethanol Chemicals Ethanol Chemicals Stalk residue Forage or energy sorghum (Biomass) Delignification Hydrolysis There are about 3,000 sweet sorghum accessions in the U.S. germplasm collection

6 Selected 96 Sweet Sorghum Accessions for Genotyping CountryLine NumberCountryLine Number Australia1Sudan15 China5Swaziland2 Ethiopia5Syria2 Hungary1Tanzania4 India7Turkey3 Kenya4Uganda4 Malawi5USA23 Mexico1Zaire4 Nigeria1Zambia1 Pakistan1Others2 South Africa5Total (22)96

7 Sweet Sorghum Growing in the Field

8 Selected SSR Markers for Genotyping Chromosome (LG)Number of markersCover region (Mb) Linkage Group Linkage Group Linkage Group Linkage Group Linkage Group Linkage Group Linkage Group Linkage Group Linkage Group Linkage Group Total95572 Mb (76.3%)

9 Genotyping Sweet Sorghum Accessions with SSRs SSR marker Xcup1

10 Statistics Results StatisticsOverallG1G2G3G4 Sample size Total number of alleles Number of alleles per locus Major allele frequency Genetic diversity PIC* * Polymorphism information content.

11 Determination of the Number of Subpopulations 1.Likelihood plot of the models, 2.Stability of grouping patterns across ten runs, 3.Germplasm information.

12 Analysis of Molecular Variation (AMOVA) Source of variation Degree of freedom Sum of squares Mean squares Percentage of variation Among population Within population Total

13 US historic syrup type Race durra from Asia Mainly from Africa (26/29) (16/20) (10/14) (24/33)

14 Genetic Distances between Sweet Sorghum Groups GroupG1G2G3G4 G G G G Note: The top diagonal is Nei’s minimum distance and the bottom diagonal is pairwise Fst.

15 Genotyped Sweet Sorghum Accessions on the World Map

16 Principal Component Analysis (PCA)

17 Geographical and Genetic Distributions of Genotyped Sweet Sorghum Accessions Wang et al., 2009 TAG

18 Summary of Sweet Sorghum Research Results Four subpopulations (G1, G2, G3, and G4) have been identified. Four branching groups (B1, B2, B3, and B4) have been classified. Results from genetic diversity and population structure analysis were correlated well with the geographical locations where these accessions were curated. Geographical origin of accessions had significant influence on genetic similarity of sweet sorghum germplasm. Germplasm accessions curated from different geographical regions should be used for developing sweet sorghum cultivars. Four subpopulations (G1, G2, G3, and G4) have been identified. Four branching groups (B1, B2, B3, and B4) have been classified. Results from genetic diversity and population structure analysis were correlated well with the geographical locations where these accessions were curated. Geographical origin of accessions had significant influence on genetic similarity of sweet sorghum germplasm. Germplasm accessions curated from different geographical regions should be used for developing sweet sorghum cultivars.

19 Income Fatty Acid Composition (12 fatty acids) Seed Oil Content (40-60%) Grain Yield of Oilseed Crop (bushel/acre) $ Biochemical and Genetic Analysis of Peanut Germplasm

20 Peanut as Nutritional and Bioenergy Crop Peanut Nutritional Crop High Protein (25%) High Oil (60%) High Oleic Acid (80%) Bioenergy Crop First Used Biodiesel High Yield Biodiesel 50 gallon oil /acre for soybean 123 gallon oil /acre for peanut

21 Peanut oil extraction by ether solvent Ankom XT15 Fat Extractor

22 Measure Oil Content by Nuclear Magnetic Resonance (NMR)

23 Plant oil + Methanol Esters + Glycerol Catalyst Convert Plant Oil to Fatty Acid Methyl Esters (FAME)

24 Linoleic acid 32.0% Oleic acid 48.0% Stearic acid 3.5% Palmitic acid 11.0% C18:1C18:2C16:0 C18:0 3.2% Behenic acid C22:0 1.6% Arachidic acid C20:0 80% (high oleic acid) Peanut Fatty Acid Composition

25 GC Analysis of Peanut Fatty Acid Composition

26 F.A.M.E. Standard 1 = C14:0 2 = C16:0 3 = C16:1 4 = C18:0 5 = C18:1 6 = C18:2 7 = C18:3 8 = C20:0 9 = C20:1 10 = C22:0 11 = C24:0 Time (min.)

27 FA HY A B A B A B B B A A B B A A AA A A Oil Content and Fatty Acid Composition among Different Subspecies

28 Hi Hy A B A A A A A A A A B B B B B B B B Oil and FAC among Different Botanical Varieties

29 Stearic acidC18:0 COOH Oleic acid COOH C18:1 COOH Linoleic acid C18:2 COOH Linolenic acid C18:3 Fatty Acid Desaturase (FAD) with Fatty Acid Composition Δ12 FAD2 ω-3 FAD3 Δ9 FAD1 x

30 From Gene Mutation to Fatty Acid Composition Change ABABABAB Gene mutation for FAD2 Wild type Mutation on AMutation on BMutation on A + B FAD2 enzyme activity Normal½ Normal Abnormal Oleic acid level 48% 64% 80% Low Middle High

31 Detection of FAD2 Mutation on B Genome by Real-time PCR Barkley et al., 2009 Molecular Breeding Ol 2 ol 2 Ol 2 ol 2 ol 2 Ol 2 Wild typeMutant Heterozygous

32 a. FAD2 mutation b. Allele-specific PCR amplification prediction Wild typeSubstitution InsertionSUB + INS

33 Detection of Mutation in FAD2 by Allele-Specific PCR Wild type Low oleate Common band Wild type band Common band Substitution band Common band Insertion band Common band Substitution band Common band Insertion band Substitution Mid oleate Insertion Mid oleate Sub + Ins High oleate Chen et al., 2010 Plant Molecular Biology Reporter

34 Wild Type O/L = 43.2 / 34.2 = 1.3 Insertion on B O/L = 60.2 / = 2.9 Substitution on A O/L = 63.6 / 18.5 = 3.4 Substitution + Insertion O/L = 80.0 / 2.9 = min Oleic acid Linoleic acid Low Oleic Acid Type Mid Oleic Acid Type High Oleic Acid Type Similar to Olive oil (64%)

35 Summary of Peanut Germplasm Research Results Significant difference identified on oil content and fatty acid composition among botanical varieties and subspecies. Real-time PCR assay was developed for detection FAD2 mutation on B genome. Allele-specific PCR assay was developed for detection FAD2 mutations on both A and B genomes including: Wild type (no mutation), Substitution type (G→A) on A genome, Insertion type (→A) on B genome, Double mutation type (Substitution + Insertion). Real-time PCR and allele-specific PCR markers developed in our lab can be used for MAS and germplasm screening. GC analysis identified accessions with different levels (L, M, H) of oleic acid. The results from Genetic analysis and GC analysis were consistent. Genetic analysis in combination with biochemical analysis is a powerful approach for germplasm research. Significant difference identified on oil content and fatty acid composition among botanical varieties and subspecies. Real-time PCR assay was developed for detection FAD2 mutation on B genome. Allele-specific PCR assay was developed for detection FAD2 mutations on both A and B genomes including: Wild type (no mutation), Substitution type (G→A) on A genome, Insertion type (→A) on B genome, Double mutation type (Substitution + Insertion). Real-time PCR and allele-specific PCR markers developed in our lab can be used for MAS and germplasm screening. GC analysis identified accessions with different levels (L, M, H) of oleic acid. The results from Genetic analysis and GC analysis were consistent. Genetic analysis in combination with biochemical analysis is a powerful approach for germplasm research.

36 PGRCUCollaborators Mr. Brandon TonnisDr. John ErpeldingUSDA-ARS, Puerto Rico Dr. Noelle BarkleyDr. Charles ChenUSDA-ARS, Dawson Mr. Dave PinnowDr. Paul RaymerUGA, Griffin Ms. Sarah MoonDr. Manjee ChinnanUGA, Food Science Dept. Ms. Jessica NorrisDr. Zhenbang ChenUGA, Crop & Soil Dept. Dr. Corley HolbrookUSDA-ARS, Tifton Mr. Ken ManleyDr. Dick AuldTexas-Tech University Ms. Lee-Ann ChalkleyDr. Baozhu GuoUSDA-ARS, Tifton Ms. Tiffany FieldsMr. Jerry DavisUGA, Statistics Dept. Ms. Merrelyn SpinksDr. Tom StalkerNCSU, Crop & Soil Dept. Dr. Gorge MosjidisAuburn University, All Supporting StaffDr. Zhanguo XinUSDA-ARS, Lubbock All CuratorsDr. Anna ResurrreccionUGA, Food Science Dept. Dr. Gary PedersonDr. Jianming YuKansas State University PGRCUCollaborators Mr. Brandon TonnisDr. John ErpeldingUSDA-ARS, Puerto Rico Dr. Noelle BarkleyDr. Charles ChenUSDA-ARS, Dawson Mr. Dave PinnowDr. Paul RaymerUGA, Griffin Ms. Sarah MoonDr. Manjee ChinnanUGA, Food Science Dept. Ms. Jessica NorrisDr. Zhenbang ChenUGA, Crop & Soil Dept. Dr. Corley HolbrookUSDA-ARS, Tifton Mr. Ken ManleyDr. Dick AuldTexas-Tech University Ms. Lee-Ann ChalkleyDr. Baozhu GuoUSDA-ARS, Tifton Ms. Tiffany FieldsMr. Jerry DavisUGA, Statistics Dept. Ms. Merrelyn SpinksDr. Tom StalkerNCSU, Crop & Soil Dept. Dr. Gorge MosjidisAuburn University, All Supporting StaffDr. Zhanguo XinUSDA-ARS, Lubbock All CuratorsDr. Anna ResurrreccionUGA, Food Science Dept. Dr. Gary PedersonDr. Jianming YuKansas State University Acknowledgements


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