QTL Mapping of Flavonoid and Phenolic Acid Variation in Broccoli

ABSTRACT

CHIU, YU CHUN.

QTL Mapping of Flavonoid and Phenolic Acid Variation in Broccoli

.

(Under the direction of Dr. Allan Brown).

Broccoli (

Brassica oleracea

L.

italica

) is a widely consumed vegetable with

increasing economic value. Broccoli is also rich in a number of putative anti-carcinogens that

includes glycosylated flavonols (quercetin, kaempferol and isorhamnetin) and a number of

hydroxycinnamic acids. Consumption of these compounds has been associated with reduced

risks of cardiovascular disease, certain cancers and a number of other chronic disorders.

Previous analyses of these compounds in broccoli have utilized extraction and analysis

methodologies that have provided inaccurate or incomplete pictures of the phytochemical

composition of broccoli florets. The first objective of this study was to accurately identify

and quantify acylated and non-acylated flavonol glycosides and hydroxycinnamic acids in

the broccoli mapping population ‘VI-158 x BNC’ via high performance liquid

chromatography in tandem with mass spectrometry (LCMS). The second objective of the

study was to utilize the mapping population (and the anchored genomic sequence) to identify

QTL and putative gene candidates associated with the variation of these compounds in

broccoli. The unambiguous nature of the molecular markers used in this mapping population

(in relation to the genomic sequence) and the close phylogenetic relationship of broccoli to

the model plant

Arabidopsis thaliana

allows for the use of known

Arabidopsis

gene

sequences to identify putative broccoli candidate genes that co-localize with identified QTL.

A population consisting of 150 F

lines were planted in two randomized complete

blocks at the Piedmont Research Station, Salisbury, NC, in 2009 and 2010. The cultivation,

harvesting and extraction of this material as well as the generation of the linkage map has

(Brown

et al

., 2015; Brown

et al

., 2014). Nine acylated and 3 non-acylated flavonols and 5

hydroxycinnamic acids were identified. The relative proportion of the flavonols was similar

in both years with approximately 55% in the form of quercetin, 42% kaempferol, and 5%

isorhamnetin, but the concentration of individual acylated flavonols was different between

years. A 5 to 14-fold difference in the concentration of these compounds among families was

observed in both years.

Using QTL analysis of individual flavonoid and hydroxycinnamic acid from two

years of data, three QTL (phe1, phe2 and phe3) were found at a genome wide significance

value of LOD = 4.0 that were individually associated with up to 30% of the phenotypic

variation. While two of these QTL (phe2 and phe3) were associated with multiple flavonols

and hydroxycinnamic acids in both years of analysis, Phe1 was primarily associated with

quercetin and kaempferol derivatives acylated with caffeic acid. Phe1 co-localized to the

same map interval as leucoanthocyanidin dioxygenase (LDOX) and a potential MYB

transcription (MYB7). Caffeoyl-CoA O-methyltransferase (CCoAOMT) was identified in an

adjacent interval and also remains a putative candidate. Phe2 mapped to the same interval as

4-coumarate-CoA ligase 3 (4CL3), and phe3 co-localized to the same map interval as

phenylalanine ammonia-lyase (PAL) and chalcone isomerase 1 (CHI or TT5). The results

suggest that altering or modifying the profile of flavonols or hydroxycinnamic acids in

broccoli could be accomplished by a relatively small number of genes. This modification to

the profile could involve either increasing the overall levels of all compounds or by

selectively altering the ratio of caffeic acid acylation in glycosides of quercetin and

© Copyright 2015 Yu Chun Chiu

QTL Mapping of Flavonol and Phenolic Acid Variation in Broccoli

by

Yu Chun Chiu

A thesis submitted to the Graduate Faculty of

North Carolina State University

in partial fulfillment of the

requirements for the degree of

Master of Science

Horticultural Science

Raleigh, North Carolina

2015

APPROVED BY:

_______________________________

_______________________________

Allan Brown,

Penelope M. Perkins-Veazie,

Co-Chair Co-Chair

ii

BIOGRAPHY

Yu Chun was born in 1989 in Taipei, Taiwan (R.O.C) and began her MS study at

North Carolina State University in the fall of 2013. Before becoming a graduate student at

NC State, she graduated from National Taiwan University with a degree from the

Department of Horticulture and was an exchange student at the University of Illinois at

Urbana-Champaign. Her research at UIUC inspired an interest in broccoli which led her to

iii

ACKNOWLEDGMENTS

I would like to extend my gratitude first and foremost to my major thesis advisor Dr.

Allan Brown for mentoring me over the course of my graduate studies. His kindness leads

me to understand much more knowledge in plant breeding and also motivates me to go

further in this field. The broccoli population he generated many years before was wonderful

and crucial for this study, therefore it is such a great honor for me to work on his masterpiece

with him during the time I am at NC state. Also, the accomplishment of this thesis is

impossible without his excellent editing skills and the patience he has shown me.

I would additionally like to thank Dr. Perkins-Veazie and Dr. Lila for their support in

both the research and especially as committee members on this thesis. Dr. Perkins is always

taking care of me and trying to help me in editing or presentation. Dr. Lila has always been

kind in providing information necessary to my MS study and I was deeply touched by her

passion for her expertise in her research. They both contributed significantly to my thesis and

I am so thankful for their advice.

I would also like to extend my appreciation to Dr. Gad Yousef who has served as a

HPLC and hands-on lab experience mentor for me. In addition, many thanks to Dr. Robert

Reid for providing all the technique support I need in bioinformatics. I would like to thank all

of the members in the Brown Lab and especially this thesis would have not been possible

without Ms. Aswathy Thomas’s help.

I appreciate NC State University’s Department of Horticultural Science for providing

such a great program in Plants for Human Health Institute (PHHI) located in Kannapolis.

iv

Special thanks to Dr. Kang-Mo Ku at UIUC for giving me wonderful comments on my thesis

and for future directions. Thanks to all the people around me (especially my roommate) and

all the family members and friends in Taiwan and in the US. It would be much more difficult

v

TABLE OF CONTENTS

LIST OF TABLES ... vii

LIST OF FIGURES ... viii

CHAPTER ONE: LITERATURE REVIEW ... 1

Broccoli (

Brassica oleracea

L.

var. italica

) ... 1

Health-promoting compounds in broccoli and the mechanism ... 4

Biosynthesis pathways of flavonoids and phenolic acids ... 8

Quantification of flavonoid and phenolic acid in broccoli ... 10

DNA marker, quantitative trait loci (QTL) mapping and the application to research

of broccoli ... 14

CHAPTER TWO: HARACTERIZATION OF FLAVONOIDS AND PHENOLIC

ACIDS IN POPULATION ‘VI-158’ x ‘BROCCOLETTE BERI E. CESPUGLIO’ (BNC)

... 17

Introduction ... 17

Methods and Materials ... 18

Plant materials ... 18

Field experimental design... 19

Extraction of flavonoids in broccoli florets ... 20

HPLC-MS analysis of phenolic compounds in broccoli ... 20

Statistical analysis ... 22

Results ... 23

Assigning target peak ... 23

Flavonols, hydroxycinnamic acids and phenolic acid content in broccoli florets ... 24

Discussion... 27

Variation of phenolic compounds ... 27

CHAPTER THREE: DENTIFICATION OF QTL AFFECTING ACCUMULATION

OF FLAVONOIDS IN BROCCOLI FLORETS ... 30

Introduction ... 30

Methods and Materials ... 31

SNP marker analysis... 31

QTL mapping and identification ... 32

vi

QTL analysis... 33

Discussion... 35

QTL analysis... 35

TABLES ... 41

FIGURES ... 58

REFERENCES ... 72

vii

LIST OF TABLES

Chapter One

Table 1-1 Index of broccoli production in the US………...41

Chapter Two

Table 2-1 Compound name, used code, retention time (Rt) recorded by HPLC, class,

molecular weight and chemistry formula of major individual phenolic compounds………..42

Table 2-2 Means, standard deviations, and ranges of phenolic compounds in floret of parental

derived F

broccoli family grown in Salisbury, North Carolina………44

Table 2-3 Source of variation (ANOVA) for phenolic compounds in the F2:3 broccoli

population evaluated in 2009 and 2010, Salisbury, NC.………..46

Table 2-4 Pearson’s correlation coefficient (

r

) among individual flavonoids and

hydroxycinnamic acid in broccoli floret in the F

broccoli population in 2009 and 2010…50

Chapter Three

Table 3-1 QTL associated with major individual flavonoids and individual hydroxycinnamic

acids in floret of F

broccoli population harvested in 2009 and 2010………..……….54

Table 3-2 Putative gene candidates in significant QTL interval from F

broccoli population

viii

LIST OF FIGURES

Chapter One

Figure 1-1 The triangle of U………..…….……….……58

Figure 1-2 Phylogenetic relation of

brassica

vegetables and

Arabidopsis

………...………...59

Figure 1-3 Basic chemical structure of hydroxycinnamic acid and flavonoid..……..………60

Figure 1-4 Biosynthesis pathway of hydroxycinnamic acid and flavonoid……….61

Chapter Two

Figure 2-1 Standard HPLC chromatography of individual flavonoids and hydroxycinnamic

acids at 340 nm in floret of F

broccoli population harvested in 2009 and 2010…………..62

Figure 2-2 Phenotypic distribution for individual flavonoids and hydroxycinnamic acids in

the floret of F

broccoli population harvested in 2009 and 2010 in Salisbury,

NC...63

Figure 2-3 Concentration of individual phenolic compound in broccoli floret in the F

population harvested in 2009 and 2010………...67

Chapter Three

Figure 3-1 QTL genetic linkage map for the F

broccoli population created by single

nucleotide polymorphism (SNP) markers………68

Figure 3-2 Composition of flavonols in floret of F

broccoli population harvested in (a)

1

CHAPTER ONE: LITERATURE REVIEW

Broccoli (

Brassica oleracea

L.

var. italica

)

Broccoli is a widely consumed

Brassica

vegetable with an increasingly important

economic value (Table 1-1). As of March 2014, broccoli was fourth in fresh market

vegetable production in the USA with a value of $896 million (USDA, 2014). From 2011 to

2013 alone, production of broccoli increased 12% with an increase of 18% in per unit value.

California and Arizona are currently the major producers of broccoli with over 95% of the all

production, but efforts in recent years to re-establish an East Coast broccoli production

system has shown promise.Broccoli belongs to the species

B. oleracea

which is believed to

have originated in the Mediterranean and Middle East (Augustine

et al

, 2014, G.R. Dixon,

2007, Lukens

et al

, 2003). Geographic and climatic variability in this region contributed to

an incredible diversity within the family that is demonstrated by dramatically different

morphological types such as broccoli, cabbage, kale, kohlrabi, and Brussels sprouts (Lukens

et al

, 2003). The original habitat of

B. oleracea

included rocky cliffs along the damp

Mediterranean coast, which nourished diversification by posing a wide array of unique

stresses and selection pressures (G.R. Dixon, 2007). Diversity also arose from natural

crossing with distant taxa within

Brassicaceae

that generated semi-fertile hybrids among

species with varying chromosomal compliments (G.R. Dixon, 2007). Additionally, multiple

instances of vertical human domestication and cultivation provided yet another dimension to

the enrichment of diversity which eventually produced the edible cruciferous vegetables we

2

B. oleracea

is a diploid species (2n=18 'CC') that belongs to a complex of 3 diploids

and 3 polyploid amphidiploids that are often referred to as the 'triangle of U' (U, 1935).

Additional diploid species of the triangle include

B. nigra

(2n=16 'BB') and

B. rapa

(2n=20

'AA') (Fig. 1-1). These species include several important vegetable, oilseed and condiment

crops. Seeds of

B. nigra

, when ground, are commonly referred to as black mustard, and are

used extensively as a spice, particularly in curries.

B. rapa

includes important vegetable

crops consumed in east and central Asia including Chinese cabbage, pak choi and turnip. The

genomic sequence of

B. rapa

is publically available (Augustine

et al

, 2014). Along with the

diploid species, there are 3 amphidiploids reflected by the sides of the triangle which include

B. juncea

(genome AABB, 2n=36),

B. carinata

(Genome BBCC, 2n=34) and

B. napus

(genome AACC, 2n=38). The amphidiploids were derived from natural hybridizations

between the diploid species followed by spontaneous doubling of the chromosomes

(Augustine

et al

, 2014, U, 1935), and their chromosome composition are really similar to the

entirety of their ancestor species (Axelsson

et al

., 2000; Osborn, 2004). These amphidiploid

species have also been artificially re-synthesized in the lab through techniques that include

embryo rescue and colchicine treatment to double the chromosomal compliment. The most

economically important member of the triangle is

B. napus

(rapeseed), which was bred for

reduced levels of glucosinolates and uric acid and is commonly referred to as canola. The

divergence of the brassica tribe from

A. thaliana

occurred about 20 million years ago, and the

divergence of the diploid

B. nigra

(genome BB) from

B. rapa

(genome AA) and

B. oleracea

(genome CC) is estimated to have occurred 6.2 million years ago. The evolutionary

3

about 3.2 million years ago (Fig. 1-2). The most recent evolutionary event in the

Brassica

families has been the development of the amphidiploids such as rapeseed which has been

estimated to have occurred as recently as 10,000 years. The genome of

B. oleracea

is

approximately 3 times larger than

Arabidopsis

and there is considerable evidence (including

gene copy numbers) to suggest that a triplication event occurred at some point in the

evolutionary history of the diploid species (Lysak

et al

., 2005; Wang

et al

, 2011).

The value of the crop, the relationships among the species of the genus, and the

relationship to the model plant

Arabidopsis thaliana

, make

B. oleracea

an extremely

important species to both agriculturalists and to those interested in studying plant

evolutionary development. Among all economically important crops,

Brassica

vegetables are

the most closely related plants to

A. thaliana

, which provides important benefits to

Brassica

researchers and is a target for translational research from

A. thaliana

. This close relationship

also provides important opportunities for

Brassica

plant breeders to exploit information

generated from basic research in

Arabidopsis

as tools or background to more efficiently

select superior plants. Among all of

Brassica

diploid species,

B. rapa

has been the most

extensively studied and much of what we know about its genome has been gained from

A.

thaliana

research. Over 93% (15,725) of predicted genes in

B. rapa

are shared with

A.

thaliana

and only 6% (1,003) are specific to

B. rapa

(Wang

et al

., 2011).

An example of how basic

Arabidopsis

research has led to important applied research

in

Brassica

involves time to flowering. To researchers in both the vegetable and oilseed

crops, time to flowering is directly related to the maturity of the crop and in expanding the

4

gene in

A. thaliana

and plays an important role in regulating flowering time. With the

expression of FLC, a delay occurs in flowering. Only two alleles (E for early flowering and L

for late flowering) of a single gene have been identified in

A. thaliana

, while three alleles of

three genes have been identified in

B. rapa

. This information has led to the current theory

that flowering time (and hence maturity) is regulated (at least in part) by a dosage effect of 3

FLC genes found in

B. rapa

(Lukens

et al

., 2003; Osborn, 2004).

Health-promoting compounds in broccoli and the mechanism

Epidemiological studies have long suggested that the consumption of

Brassica

vegetables is associated with a reduced risk of cancer (Day

et al

., 1994; Sarıkamış, 2009

Cartea, 2010; Pereira

et al

., 2009; Podsędek, 2007). Nutritional studies with

Brassica

vegetables attribute this in large part to a rich content of several health-promoting

compounds including glucosinolates, flavonoids and phenolic acids. Traditionally, the

benefits of flavonoids and phenolic acids have been attributed to antioxidant activity, but

concerns over the bioavailability of these compounds is currently debated (Podsędek, 2007).

Regardless of the mechanism, significant evidence suggests that the consumption of dietary

flavonoids reduces risk of cardiovascular disease, inflammatory disorders, viral infections,

diabetes and certain age-related neurological conditions (Androutsopoulos

et al

., 2010). This

makes the study of their genetic regulation an important topic in

Brassica

research.

Health-promoting compounds in broccoli can be broadly classified as 2 groups: those that provide

5

and vitamin A (β-carotene), and those that are not required but are associated with enhanced

health or reduced risk of disease. The latter group includes glucosinolates, flavonols

(quercetin and kaempferol) and hydroxycinnamoyl derivatives of phenolic acids.

The health benefits of glucosinolate hydrolysis products such as sulforaphane

(1-isothiocyanato-4-methylsulphinylbutane)

and

iberin

(1-isothiocyanato-3-methylsulphinylpropane) have been well studied in respect to reducing rates of prostate, lung,

liver and colorectal cancers (Hayes

et al

., 2008). The consumption of broccoli powder, for

example, by Copenhagen rats increased the activity of colon quinone reductase and

ethoxyresorufin-O-deethylase, which was reflective of Nrf2-dependent up-regulation of

phase II enzymes (Liu et al., 2009). Further research also indicated that the co-consumption

of broccoli and tomato can reduce tumor area and tumor weight to a greater degree than

controls and alternatives. Compared to controls, the mixture made of tomato and broccoli

powder decreased about 50% of tumor area and 66.7% of tumor weight in Copenhagen rats

(Canene-Adams et al., 2007). The potential synergistic effect of tomato and broccoli has been

implied, with at least part of this interaction attributable to flavonols and hydroxycinnamic

acids.

Hydroxycinnamic acids (Fig. 1-3) are a large group of non-flavonoid phenolic

compounds in broccoli. Hydroxycinnamic acids are characterized by a C6-C3 basic structure

6

can be found in a wide array of foods including commonly consumed beverages, grain

products and vegetables (El-Seedi

et al

., 2012). Health-promoting effects of

hydroxycinnamic acids include protection of low-density lipoprotein (LDL) and reduced

incidence of type II diabetes. Consumption of hydroxycinnamic acid has also been shown to

inhibit bone-reabsorbing factor-induced osteoclast-like cell formation in mice (El-Seedi

et al

.,

2012).

Flavonoids are polyphenolic compounds and one of the most diverse and widespread

classes of compounds in the plant kingdom (Podsędek, 2007). The heterocyclic benzopyran

ring is referred to as the C-ring, while the fused aromatic ring is known as the A ring, and the

phenyl constituent, the B-ring. A-rings and C-rings are connected to B ring with a connection

at position 1’ of B-ring and position 2 of C-ring. Structural diversity of these compounds

comes from modifications at different carbon positions of the rings, and include methylation,

hydroxylation and glycosylation (Fig. 1-3).

Different generalized substitutions and modifications of the carbon rings result in

sub-classes of flavonoids that include flavones, flavanones, isoflavones, anthocyanidins,

catechins, dihydroxyflavonones and chalcones (Bimlesh

et al

., 2011). In

Brassica

vegetables,

conjugation with glucose occurs at the 3 position of the C ring, while additional substitutions

can occur at the 5 and 7 positions of the A ring or at 3’, 4’ and 5’ positions of the B ring

7

One of the most common and widespread classes of flavonoids in plants are the

flavonols (Cartea

et al

., 2011). Quercetin, kaempferol and isorhamnetin are the most

common forms of flavonols in

Brassica

vegetables and in the model plant

Arabidopsis

. In

broccoli, quercertin and kaempferol tend to be in significantly higher concentration than

isorhamnetin (Koh

et al

., 2009). Kaempferol, quercetin and isorhamnetin glycosides are

mostly found as O-glycoside linkages in

Brassica

vegetables.

While many of the health studies conducted with flavonols have utilized in-vitro

assays that may not reflect what occurs in-vivo (due to issues associated with bioavailability),

a considerable amount of research suggests that they may be associated with providing

protection against cancer and other chronic health disorders. Flavonols have been associated

with protection against nuclear DNA damage (caused by hydrogen peroxide) resulting from

the chelation of intracellular redox-active iron. When Jurkat cells (a T lymphocytic cell line)

were treated with hydrogen peroxide, the higher concentration of flavonoids was

significantly correlated with a lower percentage of DNA damage (Melidou

et al.

, 2005).

The inhibition of cytochrome P450 1A1 (CYP1A1), a human gene that produces

intermediate precursors of the carcinogen benzo[

α

]-pyrene (B[

α

]P) has been observed with

dietary supplementation of quercetin (Schwarz

et al.

, 2005). The inhibition of CYP1A1 is

due to the molecular structure of the flavonols, in particular the double bond of position 2

and 3 of C-ring and hydroxyl substitutions of the A ring. Flavonols are also substrates of

P450 CYP1 enzymes which can activate the compounds and enhance antiproliferative

8

Research also suggests that kaempferol can inhibit HepG2 (human liver cell carcinoma),

HeLa (cervical cancer cells) and A549 (human alveolar cells) growth (Wang

et al.

, 2013a).

Biosynthesis pathways of flavonoids and phenolic acids

Phenylalanine is the final product of the shikimate pathway and a significant substrate

for the flavonoid biosynthesis (Saito

et al.

, 2013). The shikimate pathway builds a common

structural backbone that is used to synthesize aromatic amino acid in plants such as

phenylalanine, tyrosine and tryptophan (Fraser and Chapple, 2011). As phenylalanine enters

the phenylpropanoid pathway it is deaminated to form a trans-cinnamic acid via

phenylalanine ammonia-lyase (PAL), which represents the first committed step of the

pathway. In

Arabidopsis

, PAL is encoded by four genes designated PAL1-PAL4. These

genes appear to have some specificity, as PAL1 and PAL2 are considered to be the primary

genes responsible for the deamination of phenylalanine (Fornale

et al.

, 2014; Fraser and

Chapple, 2011). The double mutant

Arabidopsis pal1 pal2

results in an over-accumulation of

phenylalanine and a deficiency of anthocyanins. The other PAL genes in

Arabidopsis

can

partially compensate for the malfunction of

pal1

and

pal2

, but likely play other yet

unidentified roles in plant biochemistry (Fraser and Chapple, 2011).

Cinnamate (or cinnamic acid) 4-hydroxylase (C4H) is a cytochrome P450-dependent

9

At4CL3 is principally associated with flavonoid biosynthesis, and At4CL1 and At4CL2 are

associated with lignin biosynthesis. At4CL4 is functionally different from the other 3 genes

and associated with production of ferulate and sinapate. In addition to p-coumaroyl CoA,

malonyl CoA is also required for the production of flavonoids and is produced from the

carboxylation of acetyl CoA by acetyl CoA carboxylase (ACC).

Chalcone synthase (CHS) utilizes the substrates malonyl CoA and p-coumaroyl CoA,

to produce naringenin chalcone, and represents the first committed step of the central

flavonoid biosynthesis pathway. AtCHS (TT4, At5g13930) is one of three CHS-like genes

identified in

Arabidopsis

. Chalcone isomerase (CHI) catalyzes naringenin chalcone to

naringenin. Five CHI genes have been identified in

Arabidopsis

. Within this small gene

family, AtCHI (At3g55120) has been designated as TT5 (Saito

et al

., 2013).

To generate precursors of flavonols, flavanone 3-hydroxylase (F3H) and flavonoid

3’-hydropxylase (F3’H) convert naringenin to dihydrokaempferol and dihydroquercetin,

respectively. F3H (designated TT6 in Arabidopsis) oxygenates the 3-position of C-ring to

generate dihydrokaempferol, while F3’H (designated TT7 in

Arabidopsis

), catalyzes the

same reaction at 3’-position of B-ring to produce dihydrokaempferol.

Finally, the products of these 2 reactions (dihydrokaempferol and dihydroquercetin)

are converted to kaempferol and quercetin by the enzyme flavonol synthase (FLS), which

represents a branch point in flavonoid biosynthesis between flavonol synthesis and more

10

There are several transcription factors that have been identified as regulators of

flavonoid biosynthesis and these include members of the Myb, basic helix-loop-helix (bHLH)

and TTG1 (WD-repeat protein) families. Transcriptional factors associated with flavonoids

can be associated with early steps or late steps in the biochemical pathway. An example of an

early step transcription factor from soybean is GmMYB12B2 (Li

et al

., 2013) which

regulates CHS (chalcone synthase), but also impacts expression of several other enzymes in

the pathway including FLS and DFR. Due to the ease of visualizing anthocyanin color

mutants in flower or seed coats, much of the earliest work done in understanding

transcription factors was done with late steps in the pathway. In

Arabidopsis

, a considerable

number of Myb/bHLH/WD-repeat transcription factors have been identified as associated

with early and late steps in flavonoid biosynthesis (Albert

et al

., 1997; Dubos

et al

., 2008;

Gonzalez

et al

., 2008; Li

et al

., 2013; Xu

et al

., 2013) and include anthocyanin pigment 1

(PAP1), PAP2, Myb113, Myb114, transparent testa 8 (TT8), glabrous 3 (GL3), and enhancer

of glabra3 (EGL3), and CPC (single repeated R3Myb) (Fig. 1-4).

Quantification of flavonoid and phenolic acid in broccoli

High performance liquid chromatography coupled with mass spectrometry (LC-MS)

provides an opportunity to detect a wide array of compounds in single sample with high

quality results. Compounds separated effectively and property analysis such as estimates of

mass can be achieved at the same time (Xu

et al

., 2011). This has greatly facilitated the study

of plant metabolomics, and has helped decode genomic information from a series of

11

better understanding of plant secondary compounds and their relationship to the plant

genome. (Allwood and Goodacre, 2009).

Liquid chromatography is employed to separate sample compounds according to

different elution rates using a stationary and mobile phase gradient. Difference in retention

time allows for the differentiation of compounds (i.e. compounds with later retention time

elute more slowly due to the greater affinity to the stationary phase) (Allwood and Goodacre,

2009; Holĉapek

et al

., 2012; Nadella

et al

., 2012; Xu

et al

., 2011). However, compounds in

plant extraction samples are often extremely small and difficult to separate. Greater pressure

of the liquid mobile phase solvent was required to drive smaller particles through the column

because smaller particles have a greater flow resistance. Thus, high pressure pumps and

columns were introduced to LC inside metal casings to withstand greater pressures and this

technique. This has come to be known as high performance (HP) LC (Allwood and Goodacre,

2009).

The components of HPLC include a fortified column and high pressure pumps, an

interconnection between solvent reservoirs, an auto-sampler injecting system, a column and a

detector. Gradient elution, where the mobile phase is composed of one polar solvent and one

non-polar solvent, was developed to provide better separation of sample compounds. The two

mobile phases are referred to as solvent A (high aqueous solvent) and solvent B (high

organic solvent). Solvent A is used to elute samples slowly from the stationary phase, while

solvent B was used to rapidly elute samples. The gradient of solvent B is incrementally

increased from 0% to 100%. During the sample processing, the percentage of solvent B is

12

determines many of the variations of LC. Hydrophilic interaction chromatography (HILIC) is

an example of normal-phase partition chromatography used in HPLC. With the combination

of HILIC and an appropriate elution gradient, this technique is suitable for polar metabolites.

The MS portion of the LC-MS provides a detector (or detectors) to provide a more

detailed investigation of the samples. Light scattering, UV or fluorescence spectroscopy and

photodiode array (PDA) detection are three techniques widely employed in MS, and these

methods are very helpful in detecting glycosylated and acylated forms of kaempferol and

quercetin. MS can be simply described as the measurement of mass-to-charge ratio (m/z) of

the various compounds generated from ionization or the chromatographic fraction. Ionization

is extremely important for compound analysis as the ions are easier to detect and identify

than the more complex original compounds. From the m/z and the relative abundance of the

ions, absolute molecular weight of the compounds can be found, providing an important clue

to identification of the original compound (Allwood and Goodacre, 2009).

The MS system is made up of three components, an ionization source, a mass

analyzer and a detector. The two major ionization sources in LC-MS based systems include

electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI). ESI has

been intensively used with numerous compounds including organic acids and sugars. ESI has

13

Both ESI and APCI use needles to inject samples into the MS which then react with a

nebulizer gas and desolvation gas. The basic components of the two systems are quite similar,

and include rotary and turbomolecular pumps, an extraction cone and a radio frequency (RF)

hexapole lens. The major difference between the ESI and APCI systems is the presence of a

corona pin. In ACPI, the sample is ionized after the sample has been injected, and the

ionization is the result of a high charge to the corona pin. In ESI application, the sample is

directly ionized by a high electrical charge to the sample needle (Allwood and Goodacre,

2009).

After ionization, the sample is injected into the main body of the MS to undergo

further analysis. There are several types of mass analyzers and these include the quadrupole

(Q), triple quadrupole (QQQ), time of flight (TOF), reflectron time of flight (ReTOF) and

quadrupole time of flight (Q-TOF). TOF/MS has become increasingly more common and

popular owing to the emergence of ESI and matrix assisted laser desorption ionization

(MALDI) which allows the analysis of large biomolecules and high-speeding data handling.

This increasing sophistication of analysis platforms means that many of the earlier

studies of health-promoting compounds in broccoli were limited by the tools that were

available to researchers at the time. While a number of studies of kaempferol and quercetin

and hydroxycinnamic acid content in broccoli have been published, they differ dramatically

in the analysis platform used and in the results obtained (Valleojo,

et al

., 2004). The

opportunity to examine the content of these compounds in broccoli, using the latest and most

sophisticated equipment available, is of particular importance at a time when the value of

14

DNA marker, quantitative trait loci (QTL) mapping and the application to research of

broccoli

The development of molecular markers has had a large impact on plant breeding and

in the identification of specific genes associated with important traits. One of the most

important characteristics of genetic markers, is that unlike selection based on phenotypes,

markers are not affected by influences associated with different environments. With

quantitative traits (traits controlled by several genes) the genetic variation within a given

population can be expressed in the terms of molecular markers and this can greatly facilitate

a better understanding of the genetics that exist behind the phenotype expression.

The past few decades have seen a steady evolution of new molecular markers that are

increasingly becoming more adaptable to automation and therefore are also cheaper. The

older classes of molecular markers include RFLPs (restriction fragment length

polymorphism), RAPDs (random amplified polymorphic DNA), and AFLPs (amplified

fragment length polymorphism). RFLP is a non-PCR-based technique that uses restriction

endonucleases to shear genomic DNA and then uses a radioactive probe to identify

polymorphisms. RFLP markers are highly polymorphic, but are not often used today as they

involve an extremely time-consuming methodology and require the use of radiolabeled P32.

The development of PCR-based markers was a breakthrough but these early PCR-based

systems still had problems associated with them. Many of the earlier systems (RAPDs and

AFLPs) were sequence non-specific. Prior knowledge of specific DNA sequence was not

15

but they had problems associated with reproducibility of the markers among different labs

(caused by differences in reagents and reaction temperatures).

SSR (simple sequence repeats) or microsatellites are markers that utilize varying copy

number of short stretches of repetitive DNA (2-8 nucleotides in length) found throughout

plant genomes (Davey

et al

, 2011). These regions are highly polymorphic due to strand

slippage during DNA replication. DNA primers are designed to complement the flanking

region of these SSRs and polymorphisms can be easily visualized on agarose gels. Although

plentiful and informative, SSR markers still require a considerable amount of labor intensive

lab work to produce sufficient numbers of polymorphisms for most applications.

Single nucleotide polymorphisms (SNPs) are the most recent molecular markers

utilized in plant research. SNPs are highly polymorphic and are found throughout the genome.

In the maize genome, for example, a single base pair polymorphism was identified (on

average) once every 60-120 bps (Agarwal

et al

., 2008). SNPs have the advantage over other

markers in that they can be highly automated. The development of the high density (60,000)

SNP array for

Brassica

crops has reduced the time required to genotype plants and create

genetic linkage maps from months and even years to weeks (Brown

et al.

, 2015; Brown

et al.

,

2014).

The development and use of quantitative trait loci (QTL) analysis has been concurrent

with the development of DNA markers. By integrating phenotypic data (measured in the field

or lab) with marker data from genetic maps, statistically significant regions can be identified.

Markers flanking these regions can then be used in a marker-assisted selection breeding

16

2012). The use of saturated genetic linkage maps linked to genomic sequences also provides

an opportunity to identify specific genes associated the observed variation in a given crop.

The broccoli genetic linkage map recently generated by Brown (2014, 2015) is linked to the

TO1000 genomic sequence and has been successfully used to identify specific candidate

genes associated with variation in carotenoids and glucosinolates.

17

CHAPTER TWO: CHARACTERIZATION OF

FLAVONOIDS AND PHENOLIC ACIDS IN

POPULATION ‘VI-158’ x ‘BROCCOLETTE BERI E.

CESPUGLIO’ (BNC)

Introduction

Bioactive compounds in broccoli and their impact on human health have been

important topics of scientific research for a number of years. The availability of modern,

sophisticated platforms of analysis such as high performance liquid chromatography and

mass spectrometry allows for a greater in-depth examination of the phytochemical profile of

broccoli than has been previously accomplished (Allwood and Goodacre, 2010; Cartea

et al.

,

2011; Chaudhary

et al.

, 2014; Fernández-León

et al.

, 2012; Jeffery and Araya, 2009; Latté

et

al.

, 2011; Mattila

et al.

, 2000; Merken and Beecher, 2000; Podsędek, 2007; Vallejo

et al.

,

2004). Flavonoids and phenolic acids are pivotal components contributing to the beneficial

health effects associated with broccoli. The consumption of flavonoids and phenolic acids is

associated with positive effects on a number of chronic diseases including cancer and

cardio-vascular disease and a number of other disorders. (Cartea

et al.

, 2011; El-Seedi

et al.

, 2012;

Harnly

et al.

, 2006; Havsteen, 2002; Heim

et al.

, 2002); the most common forms of

flavonoids found in broccoli are flavonol glycosides (quercetin, kaempferol and isorhamnetin)

and phenolic acids are primarily found in the form of hydroxycinnamic acids (Brown

et al.

,

2002; Cartea

et al.

, 2011; Podsędek, 2007).

A number of previous studies have been conducted on the quantification of flavonols

18

of phenolic acids. Many of the previous studies of flavonols in broccoli utilized an acid or

alkaline hydrolysis in the extraction procedure, which removes attached glycoside and

acylated moieties) prior to analysis. The current studies utilizes methanol extracts of broccoli

(previously prepared for analysis of glucosinolates without hydrolysis) to provide an

improved quantification of phenolic acids in broccoli and a direct quantification of acylated

flavonol glycosides.

Methods and Materials

Plant materials

A total of 150 F

broccoli lines were utilized from the mapping population ‘VI-158’

(a calabrese-type double haploid derived from the F

hybrid ‘Viking’) x ‘BNC’ (a Brocolette

Neri ‘wild type” broccoli). BNC is phenotypically different from VI-158 in several

characteristics including flower color and leaf morphology. Petals from VI-158 are white and

the leaves are sickle-shaped, highly branched and darker in color. BNC has a tendency to

produce multiple small non-uniform-size heads after the primary head is removed while

VI-158 tends toward apical dominance (Brown

et al

, 2007; Gray

et al

, 1933). The population

segregates for a number of phenotypic or morphological traits including branching patterns,

waxy leaves, leaf shape, head size and maturity. The phytochemical profile of BNC is also

different from VI-158 (Brown

et al.

, 2002; Brown

et al.

, 2007; Brown

et al.

, 2014; Eberhardt

19

A previous study from the University of Illinois has also shown that the parents differ

in estimations of total phenolics through both direct measurements and in estimations of

oxygen radical absorbance capacity (ORAC) (Eberhardt

et al.

, 2005).

Field experimental design

Broccoli seedlings of 150 F

families were transplanted to the field at the Piedmont

Research Station, Salisbury, NC, in two consecutive years (Sept. 11 2009 and 2010). Plants

were seeded and raised in the greenhouse in flats and were approximately six weeks old at

time of transplant. Acclimation of plants was conducted for 10 days before transplanting. A

randomized complete block design with two replications was used in both years. Each

replicate consisted of ten plants spaced 30 cm apart on raised beds with black plastic mulch.

Standard field practices for broccoli were used including drip irrigation, fertilization, and pest

control.

As this population segregates for both head size and harvest date, samples were

harvested based on uniform compactness of the broccoli head. After harvest, heads were

packed on ice and transported to adjacent lab facilities at the Plants for Human Health

Institute (Kannapolis, NC) for processing. To provide standardization of samples, broccoli

heads were cut to standard sized florets with similar proportions of floret and stalk tissue.

Samples were flash frozen in liquid nitrogen and stored at -80 °C until they could be

lyophilized. Samples were lyophilized with a universal freeze dryer (VirTis 24Dx48; SP

20

samples were kept at -20 °C until extraction. A proportional bulked sample of each family

replication was prepared based on the number of heads on each harvest date.

Extraction of flavonoids in broccoli florets

The lyophilized broccoli florets were ground to fine powder with a coffee grinder.

Samples (0.2 g) were placed into 10 ml Oak Ridge tubes (Thermo Scientific Inc., Waltham,

MA) and heated with a temperature controlled block (Reacti-Therm III, Rockford, IL) at 95

°C for 10 minutes. Prior to heating, 2.0 ml of extraction solvent (70% methanol, 30% H

O)

was added to tubes and they were tightly capped. Periodic vortexing was used during the

heating. After heating, samples were cooled on ice for 5 minutes and then spun down using a

Sorvall Legend RT centrifuge (Thermo Fisher Scientific Inc., Waltham, MA) at 1200 xg

(3000 rpm) at 10

℃

for 15 minutes. The supernatant was collected into 12 x 75 mm glass

tubes and kept on ice after centrifuging. The pellet was extracted a second time using the

same procedure described above. The supernatant of both extracts were pooled and 4 ml of

the combined supernatant was collected. A 1.5 ml volume of supernatant from the extraction

was filtered into an amber HPLC vial using a 0.2 um polytetrafluorethylene (PTFE) filter and

stored at -20

℃

until HPLC analysis.

HPLC-MS analysis of phenolic compounds in broccoli

The HPLC analysis was conducted using an Agilent 1200 HPLC (Agilent Technologies

21

compartment (30 °C), and attached diode array detector (DAD). Phenolic compounds were

monitored at 320 nm and UV spectrum was stored from the range of 220 – 450 nm. The

HPLC was attached to a 6510 Quadratic Time of Flight mass spectrometer (QTOF, Agilent

Technologies Inc., Santa Clara, CA). A reversed-phase C18 column (Zorbax ods, 250 x 4.6

mm 5-micron, Agilent Technologies Inc., Santa Clara, CA) was used for flavonoid separation.

The mass detector used for analysis was an ion trap spectrometer (model G2445A) equipped

with an electrospray ionization (ESI) source, and all the parameters of analysis were

regulated using the Masshunter® software package. Data was acquired from MS using both

negative and positive ionization modes with mass scan data collected from the range of 100 –

2000

m/z

. Capillary temperature and voltage for analysis was set at 350

℃

and 4 kV,

respectively, for proper ionization. The nitrogen flow rate for analysis was set at 11 L per

minute, and the nebulizer pressure was set to 45.0 psi.

Samples (15 µl) were injected into HPLC-MS for chromatographic and spectral analysis.

The mobile phase consisted of two solvents: A: 5% formic acid and B: 100% methanol. Flow

rate was a constant 1 ml per minute, with a gradient of 5% B for 5 min, 10% B for 10 min,

15% B for 10 min, 25% B for 5 min, 60% B for 5 min and 5% B for 10 min. For the analysis,

commercial standards of kaempferol, quercetin, isorhamnetin, sinapic, and ferulic acids were

used at concentrations of 0.25, 0.125, 0.0625, 0.031, 0.016, 0.007 mg/ml to generate standard

curves for each compound. Compounds were monitored under a maximum UV absorbance

of 325 nm and peak areas were recorded and then used for quantification in comparison with

the available commercial standards. Due to the lack of commercial standards for many of the

22

kaempferol, isorhamnet, and sinapic acid for the corresponding phenolic derivatives in

broccoli floret tissue. The identification of the phenolic compounds was based on authentic

standards, whenever available. When standards were not available peak assignment was

based on UV and mass spectrum data obtained from QTOF spectrometer in comparison with

previously published data (Gu

et al

., 2004; Haminiuk

et al

., 2012; Hong

et al

., 2004; de

Pascual-Teresa

et al

., 2000, Vallejo

et al

., 2004; Olsen

et al

., 2010). For further calculation

and analysis, phenolic data are presented as equivalents of the commercial standard used for

the quantification and presented as µ g/g DW floret tissue.

Statistical analysis

SAS software (version 9.4 for Windows; SAS Institute, Cary, NC) was used to estimate

the means range and standard deviations of all compounds in both years of analysis. Analysis

of variance (ANOVA) was performed for all compounds with all factors (genotype, year,

replication, genotype



year) considered random using the Proc GLM procedure. The linear

random model used for analysis was y

= µ + G

+ E

+ R(E)j

+ G×E

+ e

, where y

is the

measurement of phenolic concentration corresponding to individual ijk, µ is the mean of the

population, G

is genetic effect, E

is the environmental effect, R(E)j

is the replicate effect

nested in each environment, and G×E

is the interaction of genetic effect and environment.

Pearson correlation coefficients for all compounds, average head size, and average harvest

23

Results

Assigning target peak

Twelve flavonols [quercetin-3-O-sophorotrioside-7-O-glucoside-di-caffeic acid (Q1),

quercetin-3-O-sophorotrioside-7-O-glucoside-p-coumaroyl

acid

(Q2),

quercetin-3-O-sophoroside (Q3), quercetin-3-O-sophorotrioside-7-O-glucoside-caffeic acid/sinapic acid

(Q4), quercetin-3-O-sophorotrioside-7-O-glucoside-di-sinapic acid (Q5),

sophoroside-caffeic acid (K1),

sophorotrioside-7-O-glucoside-sinapic acid (K2), kaempferol-3,7-di-O-glucoside (K3),

kaempferol-3-O-sophoroside (K4), kaempferol-3-O-sophorotrioside-7-O-glucoside-di-sinapic acid (K5),

kaempferol-3-O-sophorotrioside-7-O-sophoroside-ferulic

acid/sinapic

acid

(K6)

and

isorhamnetin-3-O-glucoside-7-O-sophoroside (ISO)] and six hydroxycinnamic

1,2-disinapoylgentiobiose

(SIN1),

1-sinapoyl-2-feruloylgentiobiose

(SIN2),

1,2-diferuloylgentiobiose (SIN3), 1,2’-disinapoyl-2-feruloylgentiobiose (SIN4),

1-sinapoyl-2,2‘-diferuloylgentiobiose (SIN5) and 1,2,2’-trisinapoylgentiobiose (SIN6)] were identified in

both years based on retention times and comparison to previously published spectral data

(Vallejo

et al.

, 2004) (Table. 2-1) at an absorbance of 325 nm (Fig. 2-1).

In accordance with previous literature, all flavonols identified in the current study

were O-glycosides (Cartea

et al.

, 2011; Lin and Harnly, 2010; Podsędek, 2007), with a

glycosylation pattern that included either three sophoroses (sophorotrioside), a

monosophorose or glucose moiety. Eight of the thirteen observed flavonols were acylated

24

acid). The six hydroxycinnamic acids identified were conjugated with gentiobiose through

hydroxycinnamic esters.

Flavonols, hydroxycinnamic acids and phenolic acid content in broccoli florets

On average, the total concentration of flavonols (quercetin, kaempferol and

isorhamnetin.) found in broccoli was 225 ug/g in 2009 (Table 2-2). In 2009, the relative

proportion of quercetin and kaempferol was 55% and 41%, respectively. Isorhamnetin

represented a relatively small concentration in broccoli florets (5%). In 2010, the average

concentration of total flavonols among families was comparable to 2009 (219 ug/g versus

225 ug/g) and the relative proportion of flavonols was also remarkably consistent with 54%

quercetin, 41% kaempferol, and 4% isorhamnetin. In 2009, 82% of all flavonols in broccoli

were acylated. In 2010 this fraction represented 86% of all flavonols.

Of the twelve individual flavonols identified in both years, approximately 2/3 of the

total flavonols in both years were represented by 4 compounds (Q1, Q2, K1, and K5). In the

total. The most significant difference between the two years was observed in the

concentration of Q1 (quercetin-3-

O

-sophorotrioside-7-

O

-glucoside-di-caffeic acid) which

accumulated in 2010 to almost twice the concentration observed in 2009. K1 represented

15% of total flavonols in 2009 and 27% of totals in 2010. The increase in Q1 was

accompanied by proportional decreases in 7 of the 12 identified flavonols (Q2, Q3, Q4, Q5,

25

higher in 2009 (Fig. 2-3). A considerable range of individual and total flavonols (greater than

10-fold) was observed among families in both years.

The principle hydroxycinnamic acid identified in both years was

1-sinapoyl-2-feruloylgentiobiose (SIN2) (465 µ g/g in 2009 and 410 µg/g in 2010) which represented 37 %

and 39% of total hydroxycinnamic acids in 2009 and 2010, respectively (Table 2-2). Three

hydroxycinnamic acids (SIN1, SIN2, SIN3) represented approximately 80% of total

hydroxycinnamic acids in both years. Phenolic acid concentration was approximately 20%

higher in 2009 (394 µg/g) than 2010 (316 µg/g). Individual and total hydroxycinnamic and

phenolic acids ranged from a 5 to 10-fold difference in both years.

The wild parent of the mapping population ‘BNC’ was consistently higher than the

cultivated parent ‘VI-158’ in all flavonoid and phenolic acid concentrations. The mean of the

population in regard to most compounds approximated the midparent value. The upper range

of F

family values exceed the high parent (BNC) value in regard to the accumulation of

most compounds which suggests transgressive segregation (favorable recombination of

alleles) is occurring. In 2009, for example, the accumulation of Q1 in the highest

accumulating families was twice what was observed in BNC (Fig. 2-2).

The ANOVA detected significant effects of genotype (G) with all flavonols and

hydroxycinnamic acids with the exceptions of ISO and SIN5. Significant environmental

26

of Q1, Q5, K2, K4 and K6. Significant but moderate interactions (G x E) were observed for a

number of compounds. As evidenced by the magnitude of the mean square associated with

both, environment (year) had a greater effect on expression than genotype, but significant

genetic effects were detected for almost all compounds. The variation of all compounds was

well explained by the global linear model (r = 0.76 to 0.99) used for the analysis of variance.

Moderate to high, positive correlations were observed among all flavonols (Table

2-4). In general, stronger correlations were observed between flavonols with similar structural

modifications. For example, the correlation between quercetin and kaempferol derivatives

acylated with caffeic acid (K1 and Q1)

(quercetin-3-O-sophorotrioside-7-O-glucoside-di-caffeic and kaempferol-3-O- sophorotrioside-7-O-glucoside-di-(quercetin-3-O-sophorotrioside-7-O-glucoside-di-caffeic) was r = 0.71, while

the correlation between acylated and non-acylated forms of kaempferol such as

kaempferol-3-O-sophorotrioside-7-O-sophoroside-caffeic acid (K1) and kaempferol-3-O-sophoroside

(K4) was considerably lower (r = 0.54). A strong correlation was also observed among

quercetin (Q4) and kaempferol (K5 and K6) derivatives acylated with sinapic acids (r = 0.84

and 0.74, respectively) indicating a similar pattern to what was observed with caffeic acid.

The weakest correlations observed among all flavonols were between compounds with

dissimilar modifications such as Q2 (acylated with coumaroyl) and Q3 (sophoroside, no

acylation) (r = .30) or involved the sole isorhamnetin flavonol (ISO).

Significant positive correlations were detected among almost all hydroxycinnamic

acids with the strongest correlations (r = .70 to .96) generally involving correlations with the

27

Meanwhile, moderate to weak positive correlations (r=.28 to .57) were detected between

other hydroxycinnamic acids. In 2010, No significant correlations could be detected between

the minor hydroxycinnamic acids, SIN5, SIN6 and SIN2, SIN3 or SIN4. Correlations

between hydroxycinnamic acids and total flavonols tended to be moderate (r= .36 to .72)

while correlations to phenolic acids tended to be lower (r= .14 to .55)

Harvest date was inversely correlated with all the phenolics measured in this study in

both years (r = -.21 to -.60). The correlation coefficient for harvest date with individual

flavonols ranged from -0.21 to -0.60 in both years. Similarly weak to moderate correlations

were observed between harvest date and hydroxycinnamic acid concentrations (r = –0.04 to

-0.6). Head weight was not significantly correlated to flavonol concentration, but some

hydroxycinnamic acids showed weak correlations to head weight (r=-.29 to -.357).

Discussion

Variation of phenolic compounds

The focus of this research was to identify the relative impact of genetics, environment

and potential interaction between the two on the accumulation of flavonols and phenolic

acids in a previously mapped F

population of broccoli. The parents of this population were

previously shown to differ significantly in the accumulation of total phenolic compounds and

in indirect measures of their concentration such as anti-oxidant capacity. The research also

28

chromosomal regions (QTL) that were significantly associated with genetic variation. Finally,

the project utilized the molecular markers flanking these QTL to identify putative candidate

genes that could be investigated in greater detail in future studies.

The content of flavonoids in various

brassica

vegetables has been previously reported

(Jeffery

et al.

, 2003; Koh

et al.

, 2009; Olsen

et al.

, 2009; Shao

et al.

, 2014a; Vallejo

et al.

,

2004). Compared to current USDA flavonoid content data (USDA, 2014), our measurement

of kaempferol are 2-fold higher and that of quercetin are 6.6-fold higher than values in

USDA database. The difference between our analytical method and method employed in

other research is acid hydrolysis. As addressed in the previous Methods and Materials section,

acid analyses was not done to measure the content of flavonoids. As the sugar moiety of

flavonoids is removed after acid hydrolysis, which would potentially partially degrade

aglycones (Nuutila

et al.

, 2002). An additional factor which may have influenced the

difference between our values and those of previous studies could likely be the use of freeze

dried tissue in the current study compared to the use of fresh broccoli in several previous

studies. In general, freeze-drying retains higher levels of phenolics content in plant samples

than air-drying or the use of fresh tissue (Abascal

et al.

, 2005).

Relatively few publications were found concerning the content of hydroxycinnamic

29

The analysis of variance detected a large significant effect associated with years. An

examination of the weather data from the Piedmont research station, Salisbury, NC (data not

shown), indicates that in 3.20 the monthly temperature was lower in 2010 and the first frost

was earlier. Phenolic compounds in plants are associated with abiotic stress such as

temperature and therefore it is possible that differences in temperature could have played a

role in the observed differences in flavonols and hydroxycinnamic acids between years

(Cheynier

et al.

, 2013; Hichri

et al.

, 2011). Despite the considerable impact of year to year

variation, however, significant genetic variation could be detected for almost all flavonols

and phenolic acids.

In accordance with previous results (Vallejo

et al.

, 2004), Q1, Q2, K1, K5, SIN2 and

SIN3 were the principle compounds we observed in this segregating population. In respect to

flavonols Q1, Q2, K1 and K5 are all double glycosylated and acylated with different