Getting to the root of domestication traits in carrot (Daucus carota L.)
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The document summarizes a seminar presentation on mapping traits in carrots, including carotenoids and root system architecture. It describes using genotyping-by-sequencing and RNA-Seq to identify genes associated with carotenoid accumulation. Candidate genes were identified on chromosomes 5 and 7 linked to lutein and beta-carotene levels, respectively. The presentation also discusses using 2D imaging software to measure root system architecture traits in a mapping population, and finding genomic regions linked to economically important root traits.
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Getting to the root of domestication traits in carrot (Daucus carota L.)
- 1. Shelby Ellison, PhD Dec. 14th, 2015 CIAT seminar
- 2. • Introduction to carrots and their domestication history • Traits we are interested in mapping • Carotenoids • Root System Architecture (RSA) • Part I - Utilizing Genotyping-by-Sequencing (GBS) and RNA-Seq to identify carotenoid traits of interest • Part II – Utilizing 2D imaging to identify RSA traits • Ongoing work and future directions
- 3. • Carrots are the 7th most economically important vegetable crop in the United States • The majority of carrot consumption is fresh market • In 2014, on average, one person consumed 8.5lbs (~4kg) of carrots • Total production was valued at almost 700 million US dollars (2.3 trillion COP) in 2013, up from 550 million in 2004 • California produces 85% of all carrots grown in the U.S.NASS, 2014
- 4. • Historical • Pre-900s purple and yellow carrot varieties in Afghanistan and surrounding vicinity • 1100 AD domesticated carrots moved into SW Europe • European cultivated carrots found in Americas soon after Columbus’ first visit • 1600s orange colored carrots frequently described • Molecular • Clear separation between wild and domesticated; Eastern and Western • Wild carrots from Central Asia are the closest genetic relatives to domesticated carrots • Domesticated carrot maintains a high level Iorizzo et al., 2013
- 5. Wild carrot has a heavily branched, small, white taproot Domestication
- 6. Wild carrot has a heavily branched, small, white taproot Accumulation of lutein and β-carotene Domestication
- 7. Wild carrot has a heavily branched, small, white taproot Accumulation of lutein and β-carotene Reduction of lateral branching and increased tap root mass/depth Domestication
- 8. Part I
- 9. • Play an essential role in plant life… • Light collection • Photoprotection • Biosynthesis of abscisic acid • Production of strigolactones • …and animal life • Provitamin A • Anti-cancer effects • Healthy immune system • Reduced heart disease • Can also be used as food colorants, for cosmetics, or in pharmaceuticals
- 10. • Carrots are one of the highest naturally occurring sources of β-carotene, an essential vitamin A precursor • Carrots can also be red and yellow which contain lycopene and lutein, respectively • Carrots have relatively few genomic resources and the carrot community could benefit greatly with better tools to improve key agronomic traits
- 11. • Transcriptional regulations of genes controlling carotenoid biosynthesis and carotenoid degradation Maize – PSY, ZDS, LCYE, CRTRB, ZEP K Chandler et al. (2012) Crop Sci
- 12. • Transcriptional regulations of genes controlling carotenoid biosynthesis and carotenoid degradation Maize – PSY, ZDS, LCYE, CRTRB, ZEP • Regulation of storage structures (chromoplasts) that act as carotenoid sinks Cauliflower - Or Lu S et al. (2006) Plant Cell K Chandler et al. (2012) Crop Sci
- 13. • No studies to date, in carrot, have found a direct link between a carotenoid biosynthetic gene with increased lycopene, lutein or β- carotene accumulation • Some of these mechanisms require looking outside of the pathway to identify potential carotenoid accumulation candidate genes
- 14. • No studies to date, in carrot, have found a direct link between a carotenoid biosynthetic gene with increased lycopene, lutein or β- carotene accumulation • Some of these mechanisms require looking outside of the pathway to identify potential carotenoid accumulation candidate genes Need a genome-wide approach!
- 15. DNA from a segregating population GBS Run the Tassel GBS Pipeline SNPs Genomic regions of interest GLM with phenotypic data
- 16. DNA from a segregating population RNA from 3 white, 3 yellow and 3 orange genotypes GBS RNA- Seq Run the Tassel GBS Pipeline SNPs Identify differentially expressed genes Cross reference and identify candidates Genomic regions of interest GLM with phenotypic data Run the Tophat/Cufflinks Pipeline
- 17. • 74146 (Wild x Orange) • 240 F4 individuals • Segregating for β- carotene accumulation • 97837 (White Belgian x Yellow) • 270 F2 individuals • Segregating for lutein accumulation
- 18. Visual - Binary HPLC – Carotenoid Concentration
- 19. • 23,650 SNPs • 5% missing data for marker, 10% missing data for genotype Phenotypic class Lutein (μg/g) 1) White (3) 6.90 ± 4.10* 2) Yellow (1) 33.78 ± 13.86 *Values are mean ± standard deviation.
- 20. • 23,650 SNPs • 5% missing data for marker, 10% missing data for genotype • Region of interest on Chr 5 = 198Kb Phenotypic class Lutein (μg/g) 1) White (3) 6.90 ± 4.10* 2) Yellow (1) 33.78 ± 13.86 *Values are mean ± standard deviation. 0 2 4 6 8 10 12 -log(P-value) Genome location Lutein Accumulation Chr1 Chr2 Chr3 Chr4 Chr5 Chr6 Chr7 Chr8 Chr9
- 21. • Only one differentially expressed gene in region of interest • Gene contains 212bp insertion in 2nd exon • Homologous to Arabidopsis PEL gene – where overexpression leads to Pseudo-Etiolation in Light
- 22. • 31,180 SNPs • 5% missing data for marker, 10% missing data for genotype Phenotypic class β-carotene (μg/g) 1) Yellow (3) 0.44 ± 0.49* 2) Orange (1) 99.75 ± 62.05 *Values are mean ± standard deviation.
- 23. 0 2 4 6 8 10 12 14 16 18 20 -log(P-value) Genome location β-Carotene Accumulation Chr1 Chr2 Chr3 Chr4 Chr5 Chr6 Chr7 Chr8 Chr9 • 31,180 SNPs • 5% missing data for marker, 10% missing data for genotype • Region of interest on Chr 7 = 1Mb Phenotypic class β-carotene (μg/g) 1) Yellow (3) 0.44 ± 0.49* 2) Orange (1) 99.75 ± 62.05 *Values are mean ± standard deviation.
- 24. DCARv2_Chr7:33261159-33271538 replication protein A1 DCARv2_Chr7:33272106-33274379 pseudogene, similar to putative helicase DCARv2_Chr7:33294672-33296044 Chalcone synthase DCARv2_Chr7:33414357-33416815 Polygalacturonase -1 DCARv2_Chr7:33630387-33633403 Plant protein of unknown function (DUF869) 60 Genes in ROI Five DEGs One ROI gene in MEP/Caroteno id pathway Phenotype 33.0 33.2 33.31 33.36 33.41 33.47 33.63 33.80 33.87 33.94 34.30 Or S Y G T C C T T T . T Or G C G T C C T T T A K Or G C G T C C T Y W R K Y S Y R Y Y Y Y Y W R T Y S Y R Y Y Y Y Y W R K Y C T A C T T C C . G T Y S Y R Y Y Y Y Y W R K Y S Y R Y Y Y Y Y W R K Y S Y R Y Y Y Y T T A T Y S Y R Y Y Y Y C A G T Y S Y R Y Y Y Y Y W R T Y S Y R Y Y Y Y Y W R K 1-Deoxy-d-xylulose 5-phosphate reductoisomerase (DXR) catalyzes the first committed step of the 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway for isoprenoid biosynthesis
- 25. chlorophylls tocopherols gibberellins phylloquinones plastoquinones β-ionone Stringolactone isopentenyl pyrophosphate geranylgeranyl pyrophosphate phytoene ζ-carotene α-carotene zeaxanthin antheraxanthin neoxanthin abscisic acid xanthoxin Carotenes Xanthophylls 3X PSY*(3) PDS, Z-ISO ZDS(2), CRTISO PTOX LCYBLCYE*, LCYB CHXB*(2) CYP97A3* CYP97A3* CYP97B3 CHXB*(2) CHXE ZEPVDE* NSY*(2)NCED*(9) Cleavable by CCD*(6) violaxanthin VDE* ZEP lycopene β-carotene lutein pyruvate + glyceraldehyde 3-phosphate 1-deoxy-D-xylulose-5-phosphate 2-C-methyl-D-erythritol 4-phosphate 4-diphosphocytidyl-2-C-methyl-D-erythritol 4-diphosphocytidyl-2-C-methly-D-erythritol 2-phosphate 2-C-methyl-D-erythritol 2,4-cyclodiphosphate 1-hydroxy-2-methyl-s-(E)-butenyl 4-diphosphate MEP DXS*(4) DXR(1) MCT(2) CMK MDS HDS(2) HDR*(2) GGPS(6) NCED*(9) PREA, GGPR, KSB sesquiterpenes sterols triterpenes polyterpenesIDI, GPS, FPS monoterpenoids IPPI, GPPS, TPS Or : Y Or : W Y : W DXS 0.24 1.71* 1.47* HDR 0.79 1.34* 0.55 PSY1 0.40 3.33* 2.93* PSY2 0.70 2.85* 2.14* LCYE 0.49 4.30* 3.81* CYP97A3 -5.00* -5.00* 0.20 CHXB 2.33* 2.61* 0.28 VDE 0.42 1.83* 1.41* NSY1 0.86 1.70* 0.84 NCED4 -1.46 -2.15* -0.69 CCD7 -2.35* -3.48 -1.13 CCD8 -2.72* -4.14* -1.42* -LOG2(fold_change) -5 -4 -3 -2 -1 0 1 2 3 4 5
- 26. • GWAS in ~300 PIs to further fine-map and analyze linkage disequilibrium around domestication loci • Verification of differentially expressed genes with qPCR • Use the CRISPR/Cas9 system to knockout candidate genes • Utilize SNPs within candidate genes to create robust co- dominant markers to be used to evaluate the carrot PI collection and breeding populations for β-carotene and lutein accumulation • Increase breeding efficiency and genebank characterization
- 27. Part II
- 28. • Wild carrots have a thin taproot that is highly branched • These traits are highly undesirable in current cultivars and heavily selected against in wide- crosses • There are many different carrot cultivar shapes that are important in different regions of the world • Understanding RSA can improve water- and nutrient-use efficiency • Need an effective way to phenotype • 2D imaging!
- 29. Create, image and genotype F2 mapping population Adapt existing 2D software, RootNav and SmartRoot, for carrot Analyze correlations between RSA traits and hand-measured traits Identify genomic regions or genes associated with economically important root architecture traits
- 30. • Create new F2 mapping population • B493 (orange inbred) x QAL (wild from Uzbekistan) • n = 262 • Wash, label, sample for leaf tissue (DNA), scan, sample for root tissue (HPLC) • Images as saved as JPEGs and ready to be imported in 2D image analysis software Epson Expression 10000XL ~31cm 600 dpi ~44cm
- 33. Need to convert to real value (dpi to cm)
- 34. Length, number, area, convex hull
- 37. length latlength nlats Lengthm Surface Surfacem LatSurface Volume LatVolume LatDiameter TotalLength TotalLengthm AverageLengthAllRoots AverageLengthPrimaryRoots PrimaryLengthm AverageLengthLateralRoots LateralRootCount ConvexHull MaximumWidth MaximumDepth TotLatHM AveLatHM LengthHM MaxWidthHM length latlength nlats 0.72 Lengthm 0.97 Surface 0.72 0.84 Surfacem 0.81 0.84 1.00 LatSurface 0.98 Volume 0.74 0.97 0.98 LatVolume 0.93 0.96 LatDiameter 0.72 0.97 TotalLength 0.91 0.77 0.90 0.88 0.77 TotalLengthm 0.92 0.79 0.91 0.91 0.81 1.00 AverageLengthAllRoots AverageLengthPrimaryRoots 0.93 0.77 0.86 PrimaryLengthm 0.93 0.97 0.86 0.86 0.76 1.00 AverageLengthLateralRoots 0.76 0.69 0.74 0.71 0.76 0.78 LateralRootCount 0.70 0.87 0.88 0.82 0.86 0.71 ConvexHull 0.79 0.78 0.76 0.74 0.75 0.89 0.90 0.75 0.79 MaximumWidth 0.75 0.81 0.72 0.72 0.80 0.82 0.85 0.87 0.85 0.86 MaximumDepth 0.92 0.95 0.77 0.85 0.98 0.98 TotLatHM 0.87 0.86 0.73 0.72 0.85 0.73 0.76 AveLatHM 0.89 0.88 0.71 0.72 0.86 0.72 0.76 0.97 LengthHM 0.91 0.93 0.80 0.87 0.95 0.94 0.95 MaxWidthHM 0.82 0.82 0.87 RootNav Hand Measured SmartRoot
- 38. length latlength nlats Lengthm Surface Surfacem LatSurface Volume LatVolume LatDiameter TotalLength TotalLengthm AverageLengthAllRoots AverageLengthPrimaryRoots PrimaryLengthm AverageLengthLateralRoots LateralRootCount ConvexHull MaximumWidth MaximumDepth TotLatHM AveLatHM LengthHM MaxWidthHM length latlength nlats 0.72 Lengthm 0.97 Surface 0.72 0.84 Surfacem 0.81 0.84 1.00 LatSurface 0.98 Volume 0.74 0.97 0.98 LatVolume 0.93 0.96 LatDiameter 0.72 0.97 TotalLength 0.91 0.77 0.90 0.88 0.77 TotalLengthm 0.92 0.79 0.91 0.91 0.81 1.00 AverageLengthAllRoots AverageLengthPrimaryRoots 0.93 0.77 0.86 PrimaryLengthm 0.93 0.97 0.86 0.86 0.76 1.00 AverageLengthLateralRoots 0.76 0.69 0.74 0.71 0.76 0.78 LateralRootCount 0.70 0.87 0.88 0.82 0.86 0.71 ConvexHull 0.79 0.78 0.76 0.74 0.75 0.89 0.90 0.75 0.79 MaximumWidth 0.75 0.81 0.72 0.72 0.80 0.82 0.85 0.87 0.85 0.86 MaximumDepth 0.92 0.95 0.77 0.85 0.98 0.98 TotLatHM 0.87 0.86 0.73 0.72 0.85 0.73 0.76 AveLatHM 0.89 0.88 0.71 0.72 0.86 0.72 0.76 0.97 LengthHM 0.91 0.93 0.80 0.87 0.95 0.94 0.95 MaxWidthHM 0.82 0.82 0.87 RootNav Hand Measured SmartRoot • Significant correlations (p<0.0001, r2 > 0.7) found between both programs and with hand measurements • Interesting correlations between total width and convex hull with total number of lateral roots
- 39. • Establish correlations between root traits that can be integrated into future phenotyping work • Identify genomic regions or genes associated with economically important root architecture traits • Develop molecular markers for desirable root traits to utilize in the USDA carrot breeding program
- 40. • People • Dr. Philipp Simon • Dr. Douglas Senalik • Dr. Massimo Iorizzo • Dr. Megan Bowman • Rob Kane • Stephanie Miller • Dr. Malcolm Bennett • Dr. Jonathan Atkinson • Dr. Michael Pound • Dr. Guillaume Lobet • Brianna Fochs • Funding • National Science Foundation award 1202666 • European Research Council-NSF Initiative • Carrot Genome Sequencing Project • California Fresh Carrot Advisory Board