getting to the root of domestication traits in carrot (daucus carota l.)

Shelby Ellison, PhDDec. 14th, 2015CIAT seminar

Getting to the root of domestication traits in carrot (Daucus carota L.)

Presentation overview• 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

Carrots- U.S. Statistics• 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

Carrot Domestication • 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 of genetic diversity

Iorizzo et al., 2013

Domestication traits of interest

Wild carrot has a heavily branched, small,

white taproot

Domestication



white taproot

Accumulation of lutein and β-caroteneDomestication



white taproot

Accumulation of lutein and β-carotene

Reduction of lateral branching and increased tap root mass/depth

Domestication

Part ICarotenoid Accumulation

Why study carotenoids?• 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

Why study carotenoids in carrots?

• 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

Carotenoid accumulation mechanisms

• Transcriptional regulations of genes controlling carotenoid biosynthesis and carotenoid degradation

Maize – PSY, ZDS, LCYE, CRTRB, ZEP

K Chandler et al. (2012) Crop Sci


• 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


• 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


• 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!

Research workflow

DNA from a segregating population

GBS Run the Tassel GBS Pipeline

SNPs

Genomic regions of interest

GLM with phenotypic data

Research workflow

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/Cufflink

s Pipeline

Mapping populations• 74146 (Wild x

Orange)• 240 F4 individuals• Segregating for β-carotene accumulation

• 97837 (White Belgian x Yellow)• 270 F2 individuals• Segregating for lutein accumulation

Phenotypic evaluation

Visual - Binary

HPLC – Carotenoid Concentration

97837 (Lutein) GBS RESULTS• 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.

97837 (Lutein) GBS RESULTS• 23,650 SNPs


• 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 Lutein Accumulation Chr1Chr2Chr3Chr4Chr5Chr6Chr7Chr8Chr9

Genome location

-log

(P-v

alue

)

97837 (Lutein) Fine-mapping and RNA-Seq

• 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

74146 (β-carotene) GBS RESULTS• 31,180 SNPs


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.

02468

101214161820

β-Carotene Accumulation Chr1Chr2Chr3Chr4Chr5Chr6Chr7Chr8Chr9

Genome location

-log

(P-v

alue

)74146 (β-carotene) GBS RESULTS

• 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.

74146 (β-carotene) Fine-mapping and RNA-Seq

DCARv2_Chr7:33261159-33271538 replication protein A1DCARv2_Chr7:33272106-33274379 pseudogene, similar to putative helicaseDCARv2_Chr7:33294672-33296044 Chalcone synthase DCARv2_Chr7:33414357-33416815 Polygalacturonase -1DCARv2_Chr7:33630387-33633403 Plant protein of unknown function (DUF869)

60 Genes in ROI

Five DEGs

One ROI gene in

MEP/Carotenoid

pathway

Phenotype 33.0 33.2 33.31 33.36 33.41 33.47 33.63 33.80 33.87 33.94 34.30Or S Y G T C C T T T . TOr G C G T C C T T T A KOr G C G T C C T Y W R KY S Y R Y Y Y Y Y W R TY S Y R Y Y Y Y Y W R KY C T A C T T C C . G TY S Y R Y Y Y Y Y W R KY S Y R Y Y Y Y Y W R KY S Y R Y Y Y Y T T A TY S Y R Y Y Y Y C A G TY S Y R Y Y Y Y Y W R TY 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

chlorophylls tocopherolsgibberellinsphylloquinonesplastoquinones

β-iononeStringolactone

isopentenyl pyrophosphategeranylgeranyl pyrophosphate

phytoeneζ-carotene

α-carotenezeaxanthin

antheraxanthin

neoxanthin abscisic acidxanthoxin

Carotenes

Xanthophylls

3XPSY*(3)PDS, Z-ISOZDS(2), CRTISO

PTOX

LCYBLCYE*, LCYBCHXB*(2)CYP97A3*CYP97A3* CYP97B3CHXB*(2)CHXE ZEPVDE*

NSY*(2)NCED*(9)

Cleavable by CCD*(6)

violaxanthinVDE* ZEP

lycopeneβ-carotene

lutein

pyruvate + glyceraldehyde 3-phosphate1-deoxy-D-xylulose-5-phosphate

2-C-methyl-D-erythritol 4-phosphate4-diphosphocytidyl-2-C-methyl-D-erythritol

4-diphosphocytidyl-2-C-methly-D-erythritol 2-phosphate2-C-methyl-D-erythritol 2,4-cyclodiphosphate

1-hydroxy-2-methyl-s-(E)-butenyl 4-diphosphateMEP

DXS*(4)DXR(1)MCT(2)CMKMDSHDS(2)HDR*(2)GGPS(6)

NCED*(9)

PREA, GGPR, KSB

sesquiterpenessterolstriterpenespolyterpenesIDI, GPS, FPSmonoterpenoids IPPI, GPPS, TPS

Or : Y Or : W Y : WDXS 0.24 1.71* 1.47*HDR 0.79 1.34* 0.55PSY1 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* 0.20CHXB 2.33* 2.61* 0.28VDE 0.42 1.83* 1.41*NSY1 0.86 1.70* 0.84NCED4 -1.46 -2.15* -0.69CCD7 -2.35* -3.48 -1.13CCD8 -2.72* -4.14* -1.42*

-LOG2(fold_change)-5 -4 -3 -2 -1 0 1 2 3 4 5

DEGs in MEP/Carotenoid

pathway

Ongoing work• 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

Part IIRoot System Architecture

Why study Root System Architecture?

• 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!

RSA research workflowCreate, 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

Image/data acquisition

• 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

RootNav – length and number

RootNav - https://www.cpib.ac.uk/tools-resources/software/rootnav/

Need to convert to real value (dpi to cm)

Length, number, area, convex hull

SmartRoot- http://www.uclouvain.be/en-smartroot

RSA trait and hand-measured correlations

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 Lengthm0.97 Surface0.72 0.84

Surfacem0.81 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

AverageLengthPrimaryRoots0.93 0.77 0.86

PrimaryLengthm0.93 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 MaximumDepth0.92 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 LengthHM0.91 0.93 0.80 0.87 0.95 0.94 0.95

MaxWidthHM 0.82 0.82 0.87

RootNav HandMeasuredSmartRoot

RSA trait and hand-measured correlations

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 Lengthm0.97 Surface0.72 0.84

Surfacem0.81 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

AverageLengthPrimaryRoots0.93 0.77 0.86

PrimaryLengthm0.93 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 MaximumDepth0.92 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 LengthHM0.91 0.93 0.80 0.87 0.95 0.94 0.95

MaxWidthHM 0.82 0.82 0.87

RootNav HandMeasuredSmartRoot

• 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

Ongoing work• 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

Thank you! ¡Muchas Gracias!• 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