Association of Markers in The

ASSOCIATION OF MARKERS IN THE

VITAMIN D RECEPTOR WITH MHC CLASS II

EXPRESSION AND MAREK'S DISEASE RESISTANCE

Dana Praslickova

Department of Animal Science

McGill University

Montreal

Canada

December, 2007

A thesis submitted to McGill University in partial fulfilment

of the requirements of the degree of

Doctor of Philosophy

© Dana Praslickova, 2007

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MOTTO

"...for it is not the rock that is the most solid and it is not steel that is the most firm.

In fact, it is an ordinary Joe whose endurance is greatest."

J. C. Hronsky: Jozef Mak.

Published in Czechoslovakia by Tatran, 1965, p. 271

This is dedicated to

my beloved daughter Zuzana for being supportive

and patient throughout all these years...

Contents

TABLE OF CONTENTS

Page

TABLE OF CONTENTS i

ABSTRACT v

RESUME vii

ACKNOWLEDGEMENTS ix

LIST OF TABLES xi

LIST OF FIGURES xii

ABBREVIATIONS xiv

STATEMENT OF ORIGINALITY xvii

CONTRIBUTION OF CO-AUTORS TO MANUSCRIPTS

FOR PUBLICATION xix

CHAPTER 1 - INTRODUCTION 1

1.1 GENERAL INTRODUCTION 1

1.1.1 Hypothesis 2

1.1.2 Objective 2

1.1.3 Experimental model 3

1.2 OVERVIEW OF THESIS CONTENT 3

1.3 REFERENCES 4

CHAPTER 2 - LITERATURE REVIEW 5

2.1 GENERAL DESCRIPTOPN OF MAREK'S DISEASE 5

2.1.1 Marek's disease virus 5

2.1.2 Phatogenesis 7

2.1.3 Immune response of the organism 10

2.1.4 Diagnosis of Marek's disease 12

2.2 CONTROL STRATEGY 13

2.2.1 Vaccination 13

2.2.2 Genetic resistance 15

2.2.3 Major histocompactibility complex genes 16

2.2.4 Non-major histocompactibility complex genes 16

2.3 GENES USED IN OUR STUDY 17

2.3.1 Growth hormone 17

2.3.2 Growth hormone receptor 18

2.3.3 Macrophage inflammatory protein 3a 19

2.3.4 Vitamin D 20

2.4 REFERENCES 23

CONNECTING STATEMENT I 35

CHAPTER 3 - SEQUENCE VARIATIONS IN GENES ENCODING

ENZYMES INVOLVED IN THE VITAMIN D METABOLISM AND

ASSOCIATION WITH SUBCLASSES OF PERIPHERAL BLOOD

MONONUCLEAR CELLS IN CHICKENS 36

3.1 ABSTRACT 37

3.2 INTRODUCTION 38

3.3 MATERIALS AND METHODS 39

3.3.1 Strains of chickens and data collection 39

3.3.2 Flow cytometry 39

3.3.3 Genetic analysis 40

3.3.4 Statistics and graphics 41

3.4 RESULTS 41

3.4.1 Determination of blocks of co-segregating SNP 41

3.4.2 Association of single genes with the cell

differentiation antigens on peripheral blood

mononuclear cells 42

3.4.3 Gene interaction 43

3.4.4 Correlation with production traits 43

3.5 DISCUSSION 44

3.6 ACKNOWLEDGEMENTS 47

3.7 REFERENCES 60

CONNECTING STATEMENT II 63

CHAPTER 4 - EFFECT OF MARKER ASSISTED SELECTION ON

INDICATORS OF MAREK'S DISEASE IN A VACCINATED

COMMERCIAL WHITE LEGHORN STRAIN 64

4.1 ABSTRACT 65

4.2 INTRODUCTION 66


4.3.1 Strains of chickens and selection strategy 68

4.3.2 Markers selection 68

4.3.3 Challenge 69

4.3.4 Apramycin treatment 69

4.3.5 Viral titers in feather tip extracts 69

4.3.6 Statistical analysis 70

4.4 RESULTS 70

4.4.1 Efficacy of vaccination 70

4.4.2 Effect of selection trial on viral titers 71

4.4.3 Survival analysis 71

4.4.4 Necropsy analysis 72

4.4.5 Effect on body weight, spleen weight and bursal

weight 73

4.5 DISCUSSION 73

4.6 REFERENCES 86

CONNECTING STATEMENT III 89

CHAPTER 5 - ASSOCIATION OF A MARKER IN THE VITAMIN D

RECEPTOR GENE WITH MAREK'S DISEASE RESISTANCE IN

POULTRY 90

5.1 ABSTRACT 91

5.2 INTRODUCTION 92


5.3.1 Strains of chickens and challenge test 93

5.3.2 DNA extraction and viral titration 93

5.3.3 Genetic analysis of the VDR gene 94

Contents

5.3.4 Statistical analysis 94

5.4 RESULTS 95

5.4.1 Association with viral proliferation 95

5.4.2 Association with MD lesions, mortality and weight

of the bursa 95

5.5 DISCUSSION 96

5.6 REFERENCES 106

CHAPTER 6 - GENERAL CONCLUSION 109

APPENDIX 113

Abstract

ABSTRACT

Vaccination, biosecurity and selection for genetic resistance are used world-wide

in the poultry industry against the threat of Marek's disease (MD). Unfortunately there

are new outbreaks of MD that cause serious economic problems. Scientists are therefore

searching for new and more effective ways to improve existing controls of the disease.

Knowledge of the chicken genome and progress in the study of the molecular biology of

the MD virus are providing new approaches to MD control. A particularly useful strategy

is the identification of genes that affect viral and tumor susceptibility. Genetic markers in

the growth hormone receptor (GHR), the growth hormone (GH) and the chemokine MIP-

3 a that are associated with MD resistance have previously been identified in our

laboratory. In this thesis we identified additional candidate genes by analyzing genes of

the vitamin D metabolism; conducted a large scale challenge experiment with Marek's

disease virus and tested genes encoding enzymes involved in vitamin D metabolism for

association with disease resistance.

The first manuscript describes the analysis of three genes of the vitamin D

metabolism for sequence variability and their association with the proportion of

peripheral blood mononuclear cells (PBMC) that display the surface antigens LYB, MHC

II, CD3, CD4, CD8, TCR1 (Ty8) and TCR2 (Tap1). We identified a genetic marker in the

vitamin D receptor (VDR) gene that affected the frequency of the MHC class II

expressing leukocytes (P=0.0007), and a marker in the vitamin D binding protein gene

that (DBP SIP 15) affected the expression of TCR1.

The goal of the second study was to conduct selection in a commercial strain of

White Leghorns for markers in the GH, GHR and MIP-3a genes that had previously been

associated with MD resistance and to compare the resistance of the selected and non-

selected commercial cross. We conducted two challenge experiments three months apart

with 100 chickens from the selected and 100 chickens from a non-selected population in

each challenge. To maintain similarity in poultry management, we followed a commercial

vaccination procedure. A database was compiled that comprised measurements of the

viral titers in extracts of feather tips on a weekly basis up to 8 weeks post infection, a

record of mortality and a necropsy analysis of all chickens, including those that died

Abstract

during the experiment. The outcome in challenge 1 differed from challenge 2. In

challenge 1 the selected population had a two-fold lower viral load than the non-selected

control population (P= 10") while in challenge 2 the situation was reversed (P=T0"). A

comparison of the effect of the challenge on the two populations shows that the titers in

the non-selected population in the two challenges were similar, while the titers in the

selected population differed by a factor of four. Hence the challenges were reproducible

for the non-selected population but not for the selected population. The same conclusion

was reached when other indicators of MD, such as mortality, frequency of proliferative

lesions, loss of body weight or atrophy of the bursa were measured. The source of the

different behaviors is unknown, but it raises the possibility that immune compromising

factors such as stress, nutritional status, maternal antibodies or infections may

compromise the response to MD infection in a manner that is dependent on the genetic

background.

In the third manuscript we used the database we had created to analyze the

influence of three markers in the chicken VDR gene on MD resistance. We found that the

marker that had been found to be associated with MHC class II was also associated with a

reduced viral titer (P=0.002). The effect of the genotypes was additive with a 50%

difference between the two homozygotes. It was independent of the population as well as

the challenge. Other indicators of MD behaved concordantly. The result is the first

evidence that genetic variants in genes encoding enzymes involved in vitamin D

metabolism may affect MD resistance in chickens.

Resume

RESUME

La vaccination, la biosecurite et la selection pour une resistance genetique sont

utilisees mondialement dans l'industrie avicole contre le danger impose par la maladie de

Marek (MD). Malheureusement, il y a des nouvelles apparences de MD qui causent de

graves problemes financiers. Des scientifiques sont en train de chercher de nouvelles et

meilleures facons d'ameliorer les methodes de controles presentement utilises contre le

MD. Une strategie particulierement utile consiste a identifier les genes qui affectent

l'hypersensibilite virale et tumorale. Les marqueurs genetiques trouves dans le recepteur

de 1'hormone de croissance (GHR), l'hormone de croissance (GH) et le chemokine MIP-

3a, qui sont associes avec la resistance contre la MD, ont deja ete identifies dans notre

laboratoire. Dans cette these on a identifie d'autres genes candidats en analysant les

genes du metabolisme de la vitamine D. Pour accomplir cette tache, on a cree une

experience scientifique, effectuee sur une grande echelle, avec le virus de la maladie de

Marek, et on a teste les genes du metabolisme de la vitamine D associes avec la resistance

contre la maladie.

Dans le premier manuscrit on a decrit 1'analyse des trois genes du metabolisme de

la vitamine D pour la variability dans leur sequence genetique et leur association avec la

proportion des cellules mononuclees du sang peripherique (PBMC) qui exposent les

antigenes de surface: LYB, MHC II, CD3, CD4, CD8, TCR1 (TyS) et TCR2 (Tap). On a

identifie un marqueur genetique dans le gene du recepteur de la vitamine D qui a un effet

sur la frequence des leucocytes exprimant des MHC II (P=0.0007), et un autre marqueur

dans le gene de la proteine fixant vitamine D (DBP S1P15) qui a un effet sur l'expression

deTCRl.

Le but de la deuxieme etude etait d'effectuer une selection dans la lignee

commerciale de White Leghorns pour des marqueurs genetiques dans les genes GH,

GHR, et MIP-3a qui ont deja ete associes avec l'hormone de croissance, et de comparer

la resistance des volaille commerciale selectionnes et non-selectionnes. On a prepare

deux defis avec trois mois d'ecart et avec 100 individus selectionnes et 100 individus

non-selectionnes dans chaque defi. Pour atteindre une coherence dans la gestion des

volailles, on a suivi la procedure pour la vaccination commerciale. Une base de donnees

vii

Resume

a ete compilee, contenant les mesures des titres viraux venant des extraits de bout de

plumes qui ont ete preleves chaque semaine pendant huit semaines apres l'infection, et les

analyses d'autopsie de toutes les volailles, y compris celles qui sont mortes pendant la

manipulation. Les resultats du premier defi etaient differents du second. Dans le premier

defi, la population selectionnee avait une charge virale deux fois moins de celle de la

population non-selectionnee (P=10~5), tandis que cette situation etait inversee lors du

deuxieme defi (P=10"3). Une comparaison de l'effet du defi sur les deux populations

demontre que les titres de la population non-selectionnee sont similaires dans les deux

defis, pendant que les titres de la population selectionnee different d'un facteur de quatre.

Ainsi, les defis etaient reproductibles pour la population non-selectionnee et non pour la

population selectionnee. La meme conclusion a ete atteinte quand les autres indicateurs

de MD, telles que la mortalite, la frequence des lesions proliferatives, la perte de poids,

ou l'atrophie de la bourse ont ete mesurees. La source des differents comportements est

inconnue, mais ceci donne la possibility que les facteurs qui compromettent les defenses

immunitaires comme le stress, l'etat nutritionnel, et les anticorps materaels, ou les

infections peuvent compromettre la reponse a l'infection de MD d'une maniere qui

depend du contexte genetique.

Dans le troisieme manuscrit, on a utilise la base de donnees qu'on a creee dans le

but d'analyser l'influence des trois marqueurs du gene VDR des volailles sur la resistance

contre la maladie de Marek. On a trouve que le marqueur deja associe avec MHC classe

II etait aussi associe avec un titre viral reduit (P=0.002). L'effet des genotypes etait

additif, avec une difference de 50% entre les deux homozygotes. Cet effet se manifestait

independamment de la population et du defi. D'autres indicateurs de la maladie de Marek

se sont conduits en consequence. Le resultat est la premiere indication que les variables

genetiques des genes du metabolisme de la vitamine D peuvent influencer la resistance

contre les maladies des volailles.

V1U

Acknowledgements

ACKNOWLEDGEMENTS

I wish to extend my sincere gratitude and appreciation first of all to my supervisor

Dr. Urs Kiihnlein. His wonderful guidance, encouragement, research suggestions and

daily discussions have made my work in the laboratory very enjoyable. His feelings of

excitement and joy towards science and novel research will always remain etched in my

mind. I will remember everything, and if I ever have my own students I wish they will

have the same feelings about me, as I have about this extraordinary man. Thank you Dr.

Kiihnlein!

My gratitude also belongs to Dr. David Zadworny, my first supervisor and mentor

at the Department of Animal Science, for his guidance and support, and for encouraging

me in my further studies.

I wish to thank Dr. Al Kulenkamp and Dr. George Ansah from the Shaver Poultry

Breeding farms Ltd. Cambridge, ON and Dr. Shayan Sharif, A.J. Sarson, M.F. Abdil-

Careem from the department of the Pathobiology, Veterinary School, University of

Guelph, ON for carrying out the breeding and the Marek's disease challenge experiments

and for their advise in the writing of my thesis.

I wish to acknowledge a very special man who is no longer with us. His rich life

was broken by cancer and I could not be there to express my gratitude. His name is Jozef

Cerman M.D. (Institute of Parasitology, Slovak Republic, Europe), and I thank him for

opening the door for me to the world of science by encouraging my curiosity. His

patience and teachings have taught me how important it is to be organized and to

maintain clear and detailed records of every step made, including mistakes.

I wish to thank Dr. Chadee and Kathy Keller at the Institute of Parasitology,

McGill University, for helping me when I first arrived to Canada in 1999. Particularly

Kathy, she was my voice when I could not speak English.

I wish to thank Dr. Xin Zhao, Dr. Roger Buckland and Dr. Ciro Riuz-Feria, for

acting as my committee members and for always having time to answer my questions.

My sincere thanks go to Alejandra Burchard-Levine, Myriam Fenina and Jean

Daniel Lalande, summer students of Dr. Kiihnlein and Dr. Zadworny, for their help with

DNA extraction in my project. I believe that the amount of feathers that passed trough our

ix

Acknowledgements

hands could provide a few of pairs of the wings for Daedalus and Icarus to escape from

the Crete.

I am thankful to Barbara, Cindy and Sandra, our secretaries for being so friendly

and supportive. Especially for Barbara's advice of an Iron treatment when I was feeling

ill, for Cindy's smile and helpful advice in parenting, and for Sandra's amicable and

cheerful personality.

I want to thank my school mates Katja, Susana, Jimmy, Babu, Reza, Marilyn,

Jovette, Benoit, Nabil, Juliette, Ming-Kai, Stephanie, Gen, Yonju Ha, Vinay, Marsha,

Deeni, Audrey, Jessica, Fadi and Jose for making my everyday life in the department a

bearable one, for introducing me to their different cultures, for making me laugh and for

the many social activities behind the walls of the University.

I would like to thank Charles-Olivier Basile for his tireless effort in correcting my

English.

Last but not least, I would like to thank my loving husband Jan and my family for

the unconditional support they provided me throughout all these years. Especially to my

mother for her boundless love and for her complete understanding when I could not be

with her during her heart surgery. Thank you mamicka!

List of Tables

LIST OF TABLES

Page

Table 3.1 Primers used to amplify segments of the VDR, DBP and Cyp24

genes 48

Table 3.2 Distribution of the polymorphisms 49

Table 3.3 Identification of groups of co-segregating markers in he genes

encoding the DBP 50

Table 3.4 Association of single and pairwise combinations of marker

genotypes with cell surface antigens on leukocytes 52

Table 3.5 Spearman rank correlation between FACS counts 53

Table 3.6 Correlation between egg quality trails and leukocyte cell surface

antigens 54

Table 4.1 Influence of selection on the genotype distribution 76

Table 4.2 Comparison of the cumulative viral titer at 3 and 5 weeks post

infection 76

Table 4.3 Frequency of lesions among chickens for various tissues in trial

1 and 2 77

Table 4.4 Effect of lesions on viral load, body, spleen and bursa weight in

surviving chickens 77

Table 5.1 GLM analysis of the dependence of the integrated viral titers on

trial, population and VDR S1P4 genotype 99

Table 5.2 Mean viral load for different VDR S1P4 genotypes 100

LIST OF FIGURES

List of Figures

Page

Figure 2.1 Marek's disease virus 5

Figure 2.2 Pathogenesis of Marek's disease 9

Figure 2.3 Immune responses to Marek's disease virus 10

Figure 2.4 The sources and metabolism of vitamin D 22

Figure 3.1 Association of VDR genotypes with the MHC class II count and

the CD8/CD3 ratio 55

Figure 3.2 Mean of TCR1 counts for different genotype combinations of

the marker S1P3 in the Cyp24 gene and S1P15 in the DBP gene 57

Figure 3.3 Interactive effect between the DBP gene and the Cyp24 gene on

the TCR1 ratio 58

Figure 4.1 Mating strategy to produce the commercial strain 123 78

Figure 4.2 Protective effect of vaccination on MD mortality 79

Figure 4.3 Effect of vaccination on the viral load integrated over the first 3

weeks post infection 80

Figure 4.4 Time course of viral proliferation 81

Figure 4.5 Percentile distribution of the viral load to 3 weeks in

dependence of trial and population 82

Figure 4.6 Survival curve of the two populations S and U in trial 1 and 2 83

Figure 4.7 Percentile distribution of the bursa-body weight ratio among the

survivors of the challenge experiment 84

Figure 4.8 Relationship between the mean bursa weight and mean of the

log transformed viral load to 35 dpi among survivors of the

challenge experiment 85

Figure 5.1 Map of the VDR gene 101

Figure 5.2 Time course of viral titers in feather tips for different VDR S1P4

genotypes 102

Figure 5.3 Tissue distribution of proliferative and inflammatory lesions 103

Figure 5.4 Survival and hazard rate for different VDR SIP4 genotypes 104

List of Figures

Figure 5.5 Relationship between the proportion of MHC class II positive

peripheral leucocytes and mortality, the viral load to 21 dpi, and

the frequency of lesions/chicken 105

Abbreviations

ABBREVIATIONS

AEV

ANOVA

APC

bp

Ca2+

CCL20

CCR6

CD3

CD4

CD5

Cyp24

DBP

DNA

DNAman

dpi

EARCs

EDTA

et al.

EWT

FP-SBE

F2

g

GH

GHR

GHBP

GLM

HTV

IBD

ICP4

IGF

Avian Encephalomyelitis Virus

Analyses of Variance

Antigen Presenting Cells

Base Pair

Calcium Ions

Chemokine (C-C Motif) Ligand 20

CC Chemokine Receptor 6

Cluster of Differentiation 3 (Accessory Molecules for T Cell)



Vitamin D 24-hydroxylase

Vitamin D Binding Protein

Deoxyribonucleic Acid

Sequence Analysis Software for Windows and Macintosh

Day Post Infection

Ellipsoid Associated Reticular Cells

Ethylene Diamine Tetraacetic Acid

et alia, Latin for "and others"

Egg Weight in Specific Period

Fluorescence Polarization - Single Base Extension

Second Filial Generation

Gram

Growth Hormone

Growth Hormone Receptor

Growth Hormone Binding Protein

General Linear Model

Herpes Turkey Virus

Infectious Bursal Disease

Infected Cell Protein 4

Insulin Like Growth Factor

Abbreviations

IgGl

INFy

Log

k

LYB

LTD

mRNA

MD

MDV

MHCII

N

NaCl

NCSS

NIH

NK

NO

ODC

P-value

pH

PBMC

PBS

PCR

PEPCK-C

Per

RB1B

RFLP

PFU

S

SDS

SLD

SNP

Immunoglobulin G

Interferon Gamma

Logarithm

Haplogroup

Pan-B cell Monoclonal Antibody

Laron Type Dwarfism

Messenger Ribonucleic Acid

Marek's Disease

Marek's Disease Virus

Major Histocompatibility Complex Class Two

Number of Observations

Sodium Chloride

Number Cruncher Statistical System

National Institutes of Health

Natural Killer Cells

Nitric Oxide

Ornithine Decarboxylase

Probability

Negative Logarithm of the Hydrogen Ion Concentration

Peripheral Blood Mononuclear Cells

Phosphate Buffered Saline

Polymerase Chain Reaction

Cytosolic form of Phosphoenolpyruvate Carboxykinase

Period

Very Virulent Strain of MDV

Restriction Fragment Length Polymorphism

Plug of Units

Selected Population

Sodium Dodecyl Sulfate

Sex Linked Dwarfism

Single Nucleotide Polymorphism

Abbreviations

SPG

T

TCR1 (TyS)

TCR2 (Tap)

Tris-HCl

VD

VDR

VMRD

U

USDA

l,25-(OH)2D

25-OHD

Egg Specific Gravity

Trial

T Cell Receptor (Distinct Chains)

T Cell Receptor (Distinct Chains)

Trishydroxymethylaminomethane Hydrochloride

Vitamin D

Vitamin D Receptor

Veterinary Medical Research and Development

Unselected Population

United States Department of Agriculture

1,25 Dihydroxyvitamin D3

25 Hydroxyvitamin D3

Statement of Originality

STATEMENT OF ORIGINALITY

1) In chapter 3 (manuscript #1) the genetic variations in three genes of the vitamin D

metabolism were analyzed. Arbitrarily chosen markers were tested for an association with

differences in the profile of peripheral blood leukocytes. A marker in the vitamin D

binding protein and a marker in the vitamin receptor were found to affect the proportion

of TCR1 (TCRyS) and MHC class II expressing leukocytes, respectively. This is the first

report of an association of markers in genes of the vitamin D metabolism with immune

parameters in chickens.

2) Chapter 4 (manuscript #2) is the first report on DNA based selection for the markers

associated with Marek's disease resistance in poultry. Two hatches of a control

population and a population enriched for markers located in the growth hormone receptor,

the growth hormone, and the macrophage inflammatory protein 3a (MIP-3a) were

compared. Although the results in the two hatches were contradictory, it makes an

important point. Extraneous factors such as stress or the health status of the chickens may

affect the course of a disease and may reverse the effects of genetic markers on disease

resistance.

3) The challenge test was conducted in 400 vaccinated chickens, is larger and more

complete than those found in the literature. In particular, the database included a profile

of viral proliferation over 8 weeks, a record of mortality, measurements of the body

weight, spleen weight and bursa weight and extensive necropsy data. Furthermore, it is

the first such database established in vaccinated chickens. This database will be useful for

the identification of markers associated with Marek's disease in commercial population

that are usually vaccinated.

4) In chapter 6 (manuscript #3) we used our database to test the association of three

markers in the vitamin D receptor gene with disease resistance. The marker that had been

found to affect the expression of MHC class II was found to be associated with Marek's

disease susceptibility. All the other markers had no effect. This is the first report of

xvii

Statement of Originality

showing that the chicken segregates for vitamin D receptor variants that affect Marek's

disease susceptibility. Besides a recent report that the vitamin D receptor affects

susceptibility of humans to the hepatitis virus B, it is the first documentation of the

vitamin D receptor to have an impact on a viral disease.

XVUl

Contributors

CONTRIBUTION OF CO-AUTHORS TO MANUSCRIPTS FOR PUBLICATION

The thesis follows the form of manuscripts according to the "Guidelines

concerning Thesis Preparation" of the faculty of Graduate Studies and research. The

thesis consists of three manuscripts that are under review. Each manuscript has several

co-authors. The description of their contribution in the articles, and their corresponding

address are mentioned below.

Dana Praslickova4, (Ph.D. Candidate), conceptualized the project, conducted the

laboratory experiments, gathered and analyzed the data collected, and wrote the first and

subsequent drafts of all three manuscripts for scientific publication.

Urs Kuhnlein4 (Professor), supervisor of the Ph.D. candidate, provided invaluable

guidance on the experimental design, advised throughout the project, the data analysis,

the interpretation and the editing for the scientific publication and theses.

David Zadworny4 (Associate Professor), provided laboratory supplies and financial

support.

Dr. All Kulenkamp1 (Research Scientist) and Dr. George Ansah1 (Research Scientist)

carried out breeding and selection of the White Leghorn strains and vaccination.

M. Lessard 2 (Research Scientist) provided database for the analysis of immune traits.

Dr. Shay an Sharif3 (Associate Professor), Aimee J. Sarson3 (Graduate student),

Mohamed Faizal Abdul-Careem3 (Graduate student), carried out the Marek's disease

challenge experiments, necropsy analysis and valuable advises in the writing of my thesis.

Four different institutions were involved in this study: 1 Shaver Poultry Breeding Farms Ltd. 500 Franklin Boulevard, Cambridge, ON,

Canada, N1R8G6.

Contributors

Dairy and Swine research and development Centre, Agriculture and Agri-Food

Canada, Lennoxville, QC, Canada, JIM 1Z3.

Department of the Pathobiology, Veterinary School, University of Guelph,

Guelph, ON, Canada, NIG 2W1.

Department of Animal Science McGill University, 21-111 Lakeshore Road, Ste.

Anne de Bellevue. QC, Canada, H9X 3V9.

Chapter 1

CHAPTER 1

INTRODUCTION

1.1 GENERAL INTRODUCTION

Fossil records indicate that birds separated from mammals about 310 million years

ago (Furlong, 2005). The birds that we know as domestic poultry descended from the Red

Jungle Fowl (Gallus gallus). They were domesticated about 8000 years ago in South East

Asia (Kaul et al., 2004), and over the years have evolved into many different breeds. In

recent years selection by the poultry industry has been very extensive, using various

crossbreeding strategies to increase rate of growth, body weight, egg production, and

other traits aimed at increasing production.

New horizons, emerged, when the National Human Genome Research Institute

(NHGRI) announced the first draft of the chicken genome in 2004 (International Chicken

Genome Sequencing Consortium, 2004). Composed of about 1 billion base pairs of

sequence, the chicken genome was the first non-mammalian vertebrate, and the first

agricultural animal genome to be sequenced. The sequencing of the chicken genome was

accelerated because of outbreaks of avian flu that emphasized the need to learn more

about the chicken genome, and how genetic variation may play a role in the susceptibility

to the disease.

Identification of genetic variations that are involved in resistance, susceptibility or

tolerance to diseases such as salmonellosis, coccidiosis, lymphoid leucosis, Marek's

disease, ascites, sudden death syndrome, and infectious bursal disease will make it

feasible to select for disease resistance in poultry.

Our laboratory focused on the study of variation in the genes influencing the

resistance of chickens to Marek's disease (MD). Marek's disease is a highly contagious,

lymphoproliferative, re-emerging, and economically important disease in the poultry

industry. It is caused by an avian herpes virus. The continual evolution of MD virus

(MDV) towards greater virulence, and the evidence of reoccurrence of MD in vaccinated

flocks in the last decade, suggests that future outbreaks of MD could cause serious

problems. As a consequence, there has been a renewed effort to improve existing control

of the disease. Such control strategies are effective vaccination, good biosecurity, and

1

Chapter 1

selection for genetic resistance (Gimeno, 2004). It is also very important to note that the

economic impact of MD on the world poultry industry is thought to be in the range of

US$1-2 billion annually (Morrow and Fehler, 2004).

Genetic resistance is a reliable, long lasting, and environmentally sound solution.

The essential strategy is to identify the presence of variations in specific regions of the

genome (termed "marker") which affect viral and tumor susceptibility. We have

identified variations in the growth hormone receptor (GHR) gene, the growth hormone

(GH) gene (Kuhnlein et al, 1997; Feng et al, 1998) and the macrophage inflammatory

protein 3a (MIP-3a, CCL20, ahl89) gene (Masilamani, 2003) that were associated with

MD resistance.

The search for genetic variants that affect disease resistance is a continuous

process and the first manuscript in this thesis describes the association of three genes of

the vitamin D metabolism with changes in the immune response. In the second

manuscript we describe marker assisted selection for the three genes described above.

This experiment also provided us with a database to test the association of markers with

resistance. In the last manuscript, we used this database to analyze the effect of a marker

of the vitamin D receptor gene that we had found to be associated with a reduced

expression of MHC class II antigen on peripheral blood leukocytes.

1.1.1 Hypothesis

1) Similar to humans, chickens are segregating for variants in genes of the vitamin D

metabolism that affect the cognate immune system.

2) Selection for the favorable alleles can be used to improve disease resistance for MD in

commercial strains of the chicken.

3) Genes of the vitamin D metabolic pathway that affect the immune response will also

affect MD.

1.1.2 Objective

1) Characterization of genes encoding enzymes in the vitamin D pathway. Search for

markers that affect the profile of peripheral blood leukocytes. Such markers will provide

us with candidate markers for MD resistance.

2

Chapter 1

2) Conduct marker assisted selection in a commercial cross to improve Marek's disease

resistance (MDR) using markers previously associated with MDR and to simultaneously

create a database for the detection of additional resistance associated markers.

3) Test the genetic variations in the genes of vitamin D pathway that affect immune traits

for association with resistance to MD.

1.1.3 Experimental model

A strain of White Leghorn chickens was used in our study, a non-inbred

experimental strain that was developed and maintained at Agriculture Canada (Ottawa,

Ontario), and a commercial strain that was developed by Shaver Poultry Breeding Farms

Ltd. (Cambrige, Ontario).

The genes we chose for selection in our experiment were candidate genes (GH,

GHR and MIP-3a) involved in growth and immune responsiveness. The genetic variation

of the genes were previously identified in our laboratory as being associated with

resistance to MD. The genes of the vitamin D metabolism were chosen on the basis of

their association with immune responsiveness and disease resistance in humans.

Breeding was carried out by the Shaver Poultry Breeding Farms Ltd. (Cambridge,

ON) and the challenge by the Dept of Pathobiology of the Ontario Veterinary School, of

the University of Guelph (ON). The project was approved by the Animal Care Committee

of that department. They are an accredited institution specialized in poultry breeding with

personal and/or animal protective equipment, animal housing in standard cages, and

environmentally safe procedures that avoid exposure with (and/or inactivate) all of the

potential pathogens.

1.2 OVERVIEW OF THE THESIS CONTENT

There are six chapters in this thesis, beginning with the introduction describing the

hypothesis and the objective of this study, followed by a literature review. The literature

review contains a general description of Marek's disease, control strategies of the poultry

industry and a characterization of the genes used in our study. Chapter 3, 4 and 5 are

manuscripts describing our results, including the references specific to each manuscripts.

Chapter 6 contains the conclusion.

3

Chapter 1

1.3 REFERENCES

Feng, X.P., Kuhnlein, U., Fairfull, R.W., Aggrey, S.E., Yao, Y. and Zadworny, D. (1998)

A genetic marker in the growth hormone receptor gene associated with body

weight in chickens. J. Hered., 89: 355-359

Furlong, R.F. (2005) Insights into vertebrate evolution from the chicken genome

sequence. Genome Biol, 6:207

Gimeno, I.M. (2004) Future strategies for controlling Marek's disease. Marek's disease:

An Evolving problem. Elsevier Academic press, London, UK, pp. 186-199

International Chicken Genome Sequencing Consortium (2004) Sequence and comparative

analysis of the chicken genome provide unique perspectives on vertebrate

evolution. Nature 432: 695-716

Kaul, R., Shah, J.N. and Chakrabarty, B. (2004) An assessment of important physical

traits shown by captive Red Jungle Fowl in India. Curr. Sci., 87:1498-1499

Kuhnlein, U., Ni, L., Weigend, S., Gavora, J.S., Fairfull, W. and Zadworny, D. (1997)

DNA polymorphisms in the chicken growth hormone gene: Response to selection

for disease resistance and association with egg production. Anim. Genet., 28: 116-

123

Masilamani, T.J. (2003) Identification of genetic markers associated with Marek's disease

in chickens. M.Sc. Thesis, McGill University

Morrow, Ch., and Fehler, F. (2004) Marek's disease: a worldwide problem. Marek's

disease: An Evolving problem. Elsevier Academic press, London, UK, pp. 49-61

4

Chapter 2

CHAPTER 2

LITERATURE REVIEW

2.1 GENERAL DESCRIPTION OF MAREK'S DISEASE

Marek's disease was first described by Jozef Marek in 1907 (Marek, 1907). It is

characterized by paralysis, as a result of lymphoid infiltration into peripheral nerves,

lymphomas in various organs, skin lesions, immunosuppression and blindness

accompanied by non-specific signs such as weight loss and pallor (Zelnik, 2004). It is

caused by a herpesvirus, the Marek's disease virus (MDV). An important step forward in

the study of the disease was the isolation of MDV from a chicken kidney tumor cell

culture by Churchill and Biggs, (1968) and independently from cultured duck embryo

cells by Nazerian et al. (1968) and Solomon et al. (1968).

2.1.1 Marek's disease virus

MDV (Figure 2.1) is a member of the Alphaherpesvirinae subfamily of the

Herpesviridae (Cantello et al, 1991). Herpesviridae genomes are double stranded linear

DNA molecules that range in size from 108 - 230 kbp (Davison, 2002).

Figure 2.1 Marek's disease virus (Shumacher, 2001).

5

Chapter 2

Three MDV serotypes have been identified, of which only serotype 1 (MDV-1) is

pathogenic, whereas MDV serotype 2 (MDV-2) and serotype 3 (herpesvirus of turkeys,

HVT) are not pathogenic or only weakly pathogenic and non-oncogenic in chickens

(Tischer et al, 2002). All three serotypes have been sequenced and it has been shown

that the gene contents and linear arrangements are similar but they vary in the content of

guanine and cytosine (GC): 44.1 %, 53.2 % and 47.2 % in MDV-1, MDV-2 and HTV,

respectively (Lee et al, 2000). Genome comparisons suggest that the three viruses have

evolved in parallel from different viral species (Fragnet et al, 2003). The virus genome

is comprised of a long unique sequence (UL) and short unique sequence (Us) each of

which is bracketed by inverted internal (IRL, IRS) and terminal repeats (TRL, TRS) (Nair et

al, 2004).

A total of 103 (MDV-1), 102 (MDV-2) and 99 (HTV) genes have been identified.

The putative oncogene for MDV1 is MDV EcoRI-Q (meq) (Brown et al, 2006). Other

important genes associated with pathogenicity of MDV are phosphoprotein 38 (pp38) and

viral interleukin 8 (vIL8). MDV EcoRI-Q and pp38 both play important roles in latency

and tumor formation (Ross et al, 1997), and vIL8 (Parcells et al, 2001) attracts

lymphocytes and plays a role in oncogenesis (Sick et al, 2000). An important

immediate-early (IE) gene expressed early after infection is ICP4 (infected cell

polypeptide 4) which regulates aspects of viral replication (DeLuca and Schaffer, 1985).

Another group includes the genes that code for the proteins involved in MDV replication

and infection of cells. For example, the capsid protein VP5 and the tegument proteins

VP11/12, VP13/14, VP16 play important roles in MDV growth in cells (Lupiani et al,

2001), and the tegument protein VP22 is required for virus replication and virus

transmission between cells (Dorange et al, 2002). MDV membrane glycoproteins gE

and gl are absolutely required for MDV replication (Schumacher et al, 2001). The gC

gene encodes antigen A, which is expressed on the cell surface and in the cytoplasm of

infected cells (Coussens et al, 1989), but does not appear to be oncogenic (Calnek and

Witter, 1997). More research is needed to study genes that are involved in MDV

replication and pathogenesis to better understand and control MD.

6

Chapter 2

2.1.2 Pathogenesis

Marek's disease spreads by horizontal infection. Sources of infectious virus are

dust and dander shed from the feather follicle epithelium. The virus can then be inhaled

and enter the respiratory tract. Although most chickens develop symptoms of the disease,

many are carrier of the virus. Contaminated poultry units may be infectious for several

months at 20 to 25 °C and for several years at 4 °C (Calnek, 1980).

Marek's disease progresses through four sequential stages of infection: an early

cytolytic phase, a latent phase, a late cytolytic phase and transformation (Figure 2.2).

MDV is carried by macrophages from the pulmonary epithelium into the bloodstream and

enters primary (thymus and bursa) and secondary (spleen) lymphoid tissues. The peak

viral titer in these tissues is observed at 4 days post infection (dpi). It is likely that these

tissues become infected synchronously. Recent studies show that MDV also replicates

cytolytically in macrophages (Barrow et al, 2003). The open-ended capillaries of the

spleen are surrounded by ellipsoid-associated reticular cells (EARCs). The EARCs can

phagocytose the virus and present MDV antigens in the early stages of the disease

(Jeurissen et al, 1989). Macrophages and EARCs play important roles in the transfer of

MDV to the primary targeted cells: B lymphocytes. This stage is characterized as an

early cytolytic infection (Shek et al, 1983). The pathogenicity of the infecting virus and

the resistance of the host may affect the severity of the early cytolytic infection in terms

of the extent of lymphoid organ atrophy and early mortality (Witter et al, 1980).

As immune responses of the host develop around 6-7 dpi, the disease enters into

the latent stage. Latency is characterized by the infection of activated T lymphocytes.

Resting T cells cannot be infected, but cytolytic infection of B cells activates T cells and

they become susceptible to infection (Calnek et al, 1984). Some latently infected T cells

may become transformed; the exact reasons and time for the switch between the latent

and transformed stage is not fully understood (Baigent and Davison, 2004). In

genetically resistant chickens, MDV infection does not progress past latency, although

viral particles continue to be shed from the feather follicle epithelium (Calnek and Witter,

1997). In susceptible birds, a late cytolytic infection (14-21 dpi) occurs simultaneously

with permanent immuno-suppression. Focal necrosis in the tissues of epithelial origin

and in visceral organs occurs, and inflammatory reactions develop. Lymphoproliferative

7

Chapter 2

changes lead to the final stage of the infection. Lymphomas generally consist of

neoplastic, inflammatory and immunologically active cells. Activated T helper

lymphocytes are the primary targets for transformation (Schat et al, 1991).

Transmission of the virus to the environment is through the feather-follicle

epithelium. The virus is carried to the skin by latently infected peripheral blood

lymphocytes (10-12 dpi) that aggregate around the infected follicles. At around 13 dpi,

virus replication is fully productive, resulting in cell cytolysis and release of enveloped

cell-free virus. The amount of the shed virus increases until 21 dpi and then decreases

(Kiihnlein, 2006). All chicken strains can be infected by MDV but infection differs on the

basis of their susceptibility.

8

Chapter 2

PATHOGENESIS

Inhalation.

Enveloped MD virus

W LUNG.

MDV infected cells

Bursa •Spleen Thymus

Productive

infection

Death

Productive cytolytic infection

( Latent

infection

, Transformation

Lymphoma

Tumor cells

Figure 2.2 Pathogenesis of Marek's disease. Marek's disease virus is inhaled by the

chicken. It is phagocytized by macrophages and transported to the primary and secondary

lymphoid tissues where it infects B and activated T lymphocytes and replicates

cytolytically (early cytolytic stage). As a reaction to the immune response of the

organism, disease enters a latent stage at 6 -7 dpi. Around 14-21 dpi because of the

inflammatory changes in the lymphoid organs, cytolytic replication resumes (late

cytolytic infection). Some of the infected T and B-cells infiltrate the feather follicles and

infect epithelial cells that produce infectious enveloped viruses that are shed into the

environment. Some T-cells become transformed and infiltrate a diverse array of tissues,

leading to inflammatory and proliferative leasions and death (Calnek, 1986).

9

Chapter 2

2.1.3 Immune response of the organism

Non-specific (innate) and specific (humoral and cell-mediated immunity, also

called aquired immunity) immune response control virus infections (Schat and

Markowski-Grimsrud, 2001) (Figure 2.3).

Figure 2.3 Immune responses to Marek's disease virus. The line at the bottom of the

figure represents dpi starting at day 0 (day of the infection). After MDV enters the lungs,

macrophages phagocytose MDV particles and transport them to lymphoid tissues where

they infect B cells and activated T cells (early cytolytic infection). Natural killer cells

play an important role in the innate defense against herpesviruses by killing infected cells.

The acquired immune response involving the activation of CD4+ cells and CD8+ cells

develops around 6-7 dpi disease. The infected cells then enter the latent stage when no

further viral replication takes place. Two to three weeks later, the cytolytic stage resumes

in the susceptible chickens (late cytolytic phase). Lesions and tumors appear and death

may occur.

10

Chapter 2

Innate immunity is the first line of defense against invading pathogens. MDV

enters the narrows of the lung parabronchi capillaries where it is ingested by

macrophages. Macrophages carry the virus from the pulmonary epithelium to the

primary and secondary lymphoid tissues where lymphocytes become infected and the

virus begins to replicate. Macrophages play an important role in the development of the

adaptive immune response by acting as antigen presenting cells (APC). They express

MDV antigens ICP4, pp38 and gB (Barrow et al, 2003) and release a variety of cytokines

as well as nitric oxide (NO) which can inhibit replication of MDV (Xing and Schat,

2000). Cytokines attract natural killer (NK) cells of the innate immune system. They are

non-phagocytic but are able to kill virus-infected and tumour cells (Cerwenka and Lanier,

2001). NK cells produce cytokines that regulate some of the functions of T lymphocytes,

B lymphocytes, and macrophages. They are the major source of interferon gama (IFN-y).

The level of IFN-y is higher in resistant chickens and increases with maximal activity at 7

dpi (Heller and Schat, 1987). NK cells also play a critical role in the defense against

MDV (Davison and Kaiser, 2004).

The major histocompactibility complex (MHC) proteins play important roles in

the response of an organism to infection. The MHC class I complex presents antigens to

cytotoxic T cells, thus offering targets for cytolysis (Ambagala et.al, 2005). The MHC

class II complex activates T helper cells (CD4) by displaying antigenic peptides

(LeibundGut-Landmann et.al, 2004). In the past it was shown that herpesviruses down-

regulate expression of MHC class I and II on the cell surface. However, Niikura, (2007)

published new findings which suggest possible up-regulation of MHC class II by MDV in

in vitro studies in MDV-infected chicken embryo fibroblasts as well as in vivo in infected

lymphocytes. MacLea and Cheng (2007) suggested that MDV increases expression of

MHC class II proteins to promote cytolytic infection of CD4 cells, as a strategy that

enables the virus to spread faster.

The specific immune response requires the activation of the B and T lymphocytes

to produce antibodies and cytotoxic T lymphocytes (CD8+). Antibodies delay

development of clinical signs of MD, tumor formation, and mortality (Calnek, 1972). It

is difficult to study the role of B and T lymphocytes in the defense against MDV because

the cells themselves become infected.

11

Chapter 2

The role of the cytokines in the immune response of an organism with MD were

poorly understood, but the development of a comprehensive panel of chicken cytokine

reagents by Secombes and Kaiser, (2003) made it possible to identify their function and

expression profile. The TH1 cytokines INF-y, interleukins (IL-2, IL-12, IL-18), the pro

inflammatory cytokines (IL-1(3, IL-6, IL-15) from resistant and susceptible chicken have

been sequenced and studied. Expression of INF-y in all infected birds was associated

with increased MDV loads. Differences were found in the expression of IL-6 and IL-8.

Susceptible birds over-express these cytokines while resistant chickens express neither of

them (Kaiser et al, 2003).

In addition the immune defense mechanism of the host, the transfer of maternal

antibodies by the vaccinated hens (maternal antibodies) has also been shown to reduce the

severity of the disease (Chubb and Churchill, 1969).

2.1.4 Diagnosis of Marek' s disease

Marek's disease virus was first identified by electron microscopic observations

(Nazerian et al, 1968). Chicken kidney cells or duck embryo fibroblasts are commonly

used for in vitro propagation of MDV and can be used to titrate MDV on the basis of

plaque formation (Churchill and Biggs, 1967). Three serotypes can be identified based

on the morphological characteristics of the plaques (Witter, 1983). Today electron

microscopy techniques are used mainly to study MDV morphology, physiology and tissue

distribution of the virus during infection (Zelnik, 2004).

Progress in immunological and molecular biology has enabled faster, more

accurate and more sensitive methods to detect MDV. Ideally, two independent

techniques for detection of MDV should always be included. Laboratory diagnosis of

MD involves mostly the isolation of the virus, followed by identification and

characterization using DNA analysis, antigen and antibodies.

Viral antigens can be detected in feather follicle epithelium from feather tips,

infected lymphoid tissues, or infected cell cultures by fluorescent antibody tests (FA)

(Spencer and Calnek, 1970), agar gel precipitation tests (AGP) (Haider et al, 1970) and

enzyme-liked immunosorbent assays (ELISA) (Cheng et al, 1984). Use of these tests is

12

Chapter 2

limited because MD antigen containing cells are rare in the lymphomas and latently

infected tissues. FA, AGP and ELISA can also be used for the detection of antibodies in

the chicken sera (Sharma, 1989).

The polymerase chain reaction (PCR) introduced in the 1980s has revolutionized

the detection and quantification of MDV. PCR can also be used to differentiate between

pathogenic and attenuated (Silva, 1992), and oncogenic and non-oncogenie (Zhu et al.,

1992) strains of MDV. Attenuated, non-oncogenic strains of serotype MDV-1 contain

multiple copies of a 132 base pair repeat; pathogenic strains contain one to three copies of

the same repeat sequence. After PCR, this difference is effectively visualized by

electrophoresis (Hirai et al, 1984). Complete DNA sequences of the MDV-1, MDV-2

and HTV genomes are available (Kingham et al, 2001) which allowed us to design

primers that can differentiate among MDV serotypes. Another application of PCR is the

quantification of the viral titer in samples (Bumstead et al, 1996). Absolute

quantification of viral DNA in a sample can be achieved by competitive PCR using viral

DNA of known concentration as an internal standard (Reddy et al., 2000; Linher, 2000).

2.2 CONTROL STRATEGY

Various factors can affect resistance of a chicken organism to MD. Some of these

factors include different methods of exposure to the virus (either by direct contact or

injection), gender (females are more susceptible to the disease than males) (Grander et

al, 1972), genetic background of the host, and the environmental factors.

In general, strategies to prevent MD in poultry include proper management to

avoid exposure to the virus (biosecurity), stress, prevention to infection by other

pathogens that suppress the immune system, vaccination and selection for disease

resistance.

2.2.1 Vaccination

Vaccination is the primary control strategy for MD in the poultry industry.

Churchill et al. (1969) described the first vaccine against MD. The vaccination strategy

involves intramuscular or subcutaneous administration of the vaccine to one-day-old

chicks, or the automated delivery of the vaccine in ovo to the amniotic fluid at

13

Chapter 2

embryonation day 18 (Sharma and Ricks, 2002). Other protocols include vaccination in

ovo followed by revaccination on the day of hatch or vaccination at hatch followed by

revaccination after 4-12 hours or at 7, 18, 21 days of age (Gimeno, 2004). Four general

types of MD vaccines are commercially available, HTV alone, HTV combined with

MDV serotype 2, CV1988 (also known as Rispens) alone or combined with MDV

serotype 2 or 3 (Witter, 1998), and the new MDV-1 vaccine (strain BH16) introduced by

Karpathy et al. (2002, 2003). New vaccines (recombinant vaccines) and adjuvants to

enhance the immune response are under trial (Gimeno, 2004). Recombinant vaccines are

individual viral proteins (i.e., not the whole virus) produced in yeast or bacteria. In

studies that compared different recombinant vaccines such as gB, gC, gD, gE, gl, gH

(viral membrane glycoproteins), the best level of protection was shown by gB

recombinant vaccine (Heine et al, 1997; Lee et al, 2003).

Vaccination can prevent mortality by reducing viral proliferation several fold and

by decreasing formation of tumors and clinical signs. Vaccination success depends on the

proper handling of the vaccine, strain of chicken, time between vaccination and exposure,

stress, and immune status of the chicken (Calnek and Witter, 1997). Continual evolution

of MDV towards greater virulence and recurrence of MD in vaccinated flocks (vaccine

failures) in the last decade suggest that future outbreaks of MD can cause serious

problems. The latest information about the occurrence of the disease was an outbreak in

six districts in vaccinated flocks in Haryana state in India (Kamaldeep et al, 2007). In

73,300 chickens of different production types (layer, broiler, and breeder) and age, the

morbidity and mortality ranged from 0.66% - 4.44% and 0.24% -1.00% respectively.

As a consequence, there is a great need to improve existing control of the disease

(Witter, 2001; Morrow and Fehler, 2004). There is therefore a renewed interest in host

genetic resistance. The genetic background of the host affects pathogenicity (Smith and

Calnek, 1974) as well as vaccine efficacy (Spencer et al, 1974). The mechanism by

which genetic background affects these processes remains to be elucidated (Calnek et al,

1988).

14

Chapter 2

2.2.2 Genetic resistance

The importance of genetic resistance in the control of MD was reported several

years ago, and it was used as the sole method of prevention prior to vaccine development.

The application of phenotypic selection in the poultry industry is impractical because it

requires exposure of the chicken to the pathogens with the danger of contaminating the

environment (Asmudson and Biely, 1932; Hutt and Cole, 1947; Cole, 1968).

Identification of genetic markers appears to be a more feasible strategy for improving

disease resistance.

The first genes identified as being associated with MD resistance were those

involved in the immune response of the chicken organism (Keller and Sevoian, 1981;

Calnek and Witter, 1997). These included the MHC genes, which regulate antigen

processing and presentation of antigenic peptides on the cell surface (Kaufman and

Venugopal, 1998). Selection for MHC haplotypes with resistance to MD has been used in

the poultry industry. However, since MHC genes are specific for peptides, it is feared that

such selection may reduce the repertoire of antigen recognition and render chickens more

susceptible to other infectious diseases.

There is evidence for variation in other genes that affect disease resistance; either

by modifying immune responsiveness or through other biological pathways

(Fredericksen et al, 1977; Bacon et al., 2001). We provided evidence for variations in

genes of the growth hormone axis (Kuhnlein et al, 1997), the macrophage inflammatory

receptor 3a (MIP-3a), ornithine decarboxylase (ODC) (Aggrey et al, 1996; Masilamani,

2003) and mitochondrial phosphoenolpyruvate carboxykinase (PEPCKM) (Li et al,

1998a, 1998b). Although we originally thought that these genes were unrelated to

immune responsiveness, recent evidence indicated that these genes have a general effect

on immune responsiveness. It reflects how extensively genes interact with each other.

The genes associated with genetic resistance can be divided into MHC and non-

MHC genes (Bacon et al, 2001).

15

Chapter 2

2.2.3 Major histocompactibility complex genes

The chicken MHC molecules are divided into three classes: class I, class II and

class IV. Based on similarity to mammals, class I molecules are present in all cell types

while class II are expressed by antigen presenting cells (APC). The MHC class II

molecules play an important role in APC - B cells - T cells interaction. Class IV

molecules are unique to avian species (Sander, 1993). They are expressed on

erythrocytes, liver cells, bursa and thymic lymphoblasts, and intestinal epithelial cells.

These molecules function in the antibody response of the organism, and they might be

involved in the enteric mucosal immunity against viruses and other pathogens (Zekarias

et al, 2002).

Chicken MHC was originally described as the blood group system B (chickens

have four blood groups: A, B, C, and E) (Plachy et al, 2003). Certain haplotypes of the

MHC gene have been associated with resistance to MD (Bacon, 1987; Zekarias et al,

2002). It was demonstrated that the B21 haplotype is associated with resistance to MDV,

while the B19 haplotype is associated with susceptibility to MDV tumor development

(Hansen et al, 1967). Kaufmann et al (1995) found a correlation between the level of 91

MHC class 1 expression and resistance to MD. The B haplotype expresses the lowest

level of MHC class 1 and has the highest degree of resistance. The possible explanation

of this mechanism is that low levels of MHC class I molecules lowers cytotoxic T cell

activity, and maximizes activity of NK cells (Zekarias et al, 2002; Plachy et al, 2003).

Selection for resistance associated with MHC haplotypes is used in the poultry industry.

MHC haplotypes also influence the efficacy of the response to MD vaccines depending

on the serotype used for the vaccination (Bacon and Witter, 1992, 1994).

2.2.4 Non-major histocompactibility complex genes

Evidence for the importance of non-MHC genes in disease resistance has been

known for more than two decades but was not well studied (Gimeno, 2004). This

changed when the sequence of the chicken genome was published by the National Human

Genome Research Institute (NHGRI) in 2004 (International Chicken Genome Sequencing

16

Chapter 2

Consortium, 2004). The availability of the chicken genome offers great opportunities to

expand studies of genes and genetic resistance.

Non-MHC markers for genetic resistance may be present in a variety of genes.

The genes may either be directly involved in the immune response or they can be

unrelated or only indirectly involved with the immune system (Fredericksen et ah, 1997;

Bacon et ah, 2001).

The proliferation and pathogenicity of MD virus involves the interaction of many

genes encoded by the viral genome with host genes. It is therefore expected that multiple

host genes will affect disease resistance (Witter, 1997). Such genes may affect the course

of the disease at the level of viral proliferation, oncogenic transformation, the viral

antigens that are recognized by the humoral and innate immune system, or the general

resilience of the host to MD (Calnek, 2001).

2.3 GENES USED IN OUR STUDY

2.3.1 Growth hormone

Growth hormone (GH), also known as somatotropin, is a protein hormone

synthesized and secreted in the anterior pituitary gland by somatotrophs. GH is also

produced in neural tissue, immune cells and reproductive tissue (Harvey and Hull, 2003).

These extrapituitary tissues also have GH receptors and are target sites for GH action. It

is therefore possible that the local production of GH has paracrine and autocrine actions

in addition to the endocrine actions of pituitary GH (Harvey et ah, 2000).

GH is obligatory for growth and also plays an important role in the regulation of

the metabolism of many cells. GH has direct and indirect effects. Direct effects are

mediated by GH binding to a specific receptor on a target cell. For example, GH

suppresses the ability of adipocytes to absorb lipids (Richelsen, 1997). Indirect effects on

a cell are mediated by another endocrine factor, insulin-like growth factor-I (IGF-I),

whose production is stimulated in other cells by GH. Insulin-like growth factor-I

stimulates the proliferation and differentiation of chondrocytes (cartilage cells) resulting

in bone elongation (Baker et ah, 1993).

17

Chapter 2

The GH promotes growth of the thymus, a gland responsible for the maturation of

T cells. For this purpose it is used in the treatment of HIV positive patients (Napolitano et

al, 2002). Cytokines such as interleukin-1 (IL-1) and interleukin-6 (IL-6) stimulate the

secretion of GH during the immune response (Koyu et al, 1999).

The GH gene in the chicken has been analyzed for its association with egg

production, age at first egg, egg weight in layers, gain in body weight, and feed

conversion in the broilers. Kuhnlein et al. (1997) and Feng et al. (1997) showed the

association of polymorphisms in the chicken GH gene with production traits. Linher

(2000) identified a marker in the GH gene that was associated with MDV proliferation in

lymphoid tissue (spleen). The same marker had been previously shown to be co-selected

with selection for MD resistance (Kuhnlein et al, 2006). Liu et al. (2001) confirmed the

association of this GH marker with MD resistance and showed that GH and the MDV

protein SORF2 (found only in serotype MDV-1) bind to each other.

2.3.2 Growth hormone receptor

The GHR is one of several members in the family of hematopoietin receptors

(Moutoussamy et al, 1998). It has three domains, a ligand binding domain, a single

hydrophobic trans-membrane domain and an intracellular domain. Although GHR is

expressed in a number of tissues (Hughes and Fiessen, 1985; Isaksson et al, 1985), the

highest concentration of receptors is found in the liver, where GH induces synthesis and

secretion of IGF-I (Isaksson et al, 1985).

Growth hormone exerts its effect by binding to the GHR resulting in the

dimerization of the GHR and recruitment of the cytoplasmatic tyrosine kinase named

Janus Associated Kinase 2 (JAK-2) and its phosporylation. Phosporylation of JAK-2 can

be detected within a few minutes of exposure the targeted cells to the extracellular

stimulation of GH. Activated JAK-2 phosporylate tyrosines on transcription factor

proteins (STATs). STATs will form dimmers and undergo translocation into the nucleus

where they act as transcriptional regulatory proteins. JAK-2 also activates mitogen-

activated protein kinase (MAPK) pathways affecting gene regulation (Alberts et al,

2002).

18

Chapter 2

In chickens, Vanderpooten et al. (1993) reported that the level of hepatic GHR

expression is affected by selection for growth or feed efficiency. A marker in the GHR

gene has been shown to be associated with juvenile body weight in strain 7 and strain 9 of

White Leghorn chickens (Feng et al, 1998). Variants of the GHR may affect interaction

with GH and, ultimately, the cellular response. Laron-type dwarfism (LTD) in humans

and sex-linked dwarfism (SLD) in the chickens are examples of disorders involving

defects in the GHR. LTD caused by a deletion in the GHR gene is characterized by high

levels of GH and low levels of IGF-I in the circulation (Godowski et al, 1989). SLD has

a similar profile and the mutation caused 30-40 % reduction in body weight, shortened

long bones, lower basal metabolism and greater accumulation of body fat (Burnside et ah,

1991).

2.3.3 Macrophage inflammatory protein 3a

MIP-3a, also known as a CCL20, LARC or EXODUS, is a chemokine involved in

attracting cells of the immune system (lymphocytes and dendritic cells) to sites of

inflammation and to facilitate their entry into the tissue.

Chemokines are categorized by the number and position of the conserved cysteine

residues. Four groups are distinguished: C, CC, CXC, and CXXXC (Sick et al, 2000).

Chemokines have small molecular weights (8-14 kDa). They regulate immune responses

by binding to seven-transmembrane G-protein-coupled receptors on the surface of

leukocytes (Giansanti et al., 2006). Chemokines are induced by proinflammatory

cytokines, extracellular proteins that regulate the intensity of the immune response by

stimulating or inhibiting the activation, proliferation, and/or differentiation of various

cells, and by regulating the secretion of antibodies or other cytokines.

The only known receptor for MIP-3a is the CC-chemokine receptor 6 (CCR6)

which is expressed on resting memory T cells, B cells and dendritic cells. This suggests

that the complex of CCR6 and MIP-3a plays a role in the physiology of resting memory

T cells, B cells and dendritic cells, and the interaction among these cells (Liao, 1999;

Wang, 2005). MIP-3a and CCR6 participate in morphogenesis and hematopoiesis (Sick

et al., 2000). The human alveolar epithelium is an important source of MIP-3a and may

play a critical role in the control of the movement of dendritic cells through the lung

19

Chapter 2

under normal and inflammatory conditions (Thorley, 2005). They therefore may also play

an important role in the host defense against MD infection. Studies indicate that MIP-3a

is overexpressed in pancreatic tumors (Kleeff et al, 1999) and hepatocellular carcinomas

(Yamauchi et al., 2003), but very little is known about the connection between cancer and

the expression of the chemokines in the avian species.

Hughes et al. (2001) isolated cDNA clones of three chicken chemokines

belonging to the CC group. The three clones identified were ah 189, ah 294 and ah 221

with EMBL accession numbers AY037861, AY037859 and AY037860 respectively.

According to Hughes et al. (1985), ahl89 showed 58% homology to human MIP-3a. In

our laboratory, Masilamani (2003) reported the association of Hin6l RFLP in the MIP-3a

(ahl 89) gene with the viral titer in MD challenged non-vaccinated chickens.

2.3.4 Vitamin D

Vitamin D is a steroid hormone that must undergo metabolic alteration before

being biologically active. Vitamin D has a wide spectrum of actions that include the

regulation of the levels of calcium and phosphorus, the growth and differentiation of cells

and the modulation of the immune system.

There are two sources of vitamin D, dietary intake and activation of an

endogenous precursor by solar radiation (Figure 2.4). The dietary sources of vitamin D

are ergocalciferol from plants (vitamin D2) (Windaus et al, 1930) and cholecalciferol

(vitamin D3) from animal sources (Brockmann, 1936). In poultry, the vitamin D binding

protein (DBP) does not bind vitamin D2 effectively; therefore this form of vitamin D2

cannot be used as a feed additive (Soares et al, 1995). Cholecalciferol is generated in

epidermal skin cells by ultraviolet light induced conversion of 7-dehydrocholesterol, a

derivative of cholesterol (Goldblatt and Soames, 1923). Vitamin D3 is transported by

DBP to the liver, where it is hydroxylated to 25-hydroxy vitamin D3. This form of

vitamin D3 is then transported to the kidney where it is converted to the most active form

of vitamin D3, 1,25-dihydroxy vitamin D3, by the enzyme 1-alpha hydroxylase. The

cellular effects of vitamin D3 are mediated by the vitamin D receptor (VDR) (Kato, 2000;

Marcinkowska, 2001). VDR has two cellular locations, the nucleus and the cellular

matrix. The nuclear VDR acts as a transcription factor, while the membrane located

20

Chapter 2

receptor mediates the immediate actions of vitamin D3 via a signaling pathway. The

chicken VDR was isolated and characterized by Zhongjian et al. (1997, 2000).

Vitamin D within physiological concentrations has a positive effect on human

health. One of these effects is an attenuation of the immune system, thus preventing

autoimmune diseases (Deluca and Cantorna, 2001). In mouse model systems it was

shown that 1,25-dihydroxyvitamin D3 can either prevent or markedly suppress model

autoimmune disorders such as autoimmune encephalomyelitis, multiple sclerosis,

rheumatoid arthritis, systemic lupus erythematosus, type I diabetes, and inflammatory

bowel disease. The mechanism may be explained by the stimulation of transforming

growth factor (TGFP-1) and interleukin 4 (IL-4) productions which in turn may suppress

inflammatory T cell activity. Vitamin D also has antiseptic functions. Wang et al. (2004)

reported its ability to increase the expression of catathelicidin antimicrobial peptide

(camp) in human neutrophils.

In addition to its effect on the immune response, vitamin D also affects other

human diseases. McGrath et al. (2004) reported an association between vitamin D and a

reduced risk of schizophrenia. Intake of vitamin D by males in the first year of their life

may help prevent schizophrenia, but no similar pattern was found for women. Vitamin D

has protective effects against certain cancers. Epidemiological studies in humans

demonstrated a correlation between low levels of vitamin D intake and an increased risk

of several cancers. Kallay et al. (2005) reported that 1,25-dihydroxy vitamin D3

decreased the expression of p21 protein (regulating intestinal cell proliferation,

maturation and tumorigenesis), and thus led to hyperproliferation in the ascending colon.

In poultry, vitamin D has mostly been studied for its metabolic and physiological

effects when added as a feed component. Interestingly, feeding studies in poultry showed

that 25-hydroxy vitamin D3, but not 1,25-dihydroxy vitamin D3> has a positive effect on

fertility and hatchability (Soares et al., 1995). Increasing levels of 1,25-dihydroxy

vitamin D3 for a long period of time even showed toxic effects. Harms et al. (1988)

reported a decrease in egg production, egg weight, and feed consumption in laying hens

that were fed high levels of 1,25-dihydroxy vitamin D3. Atencio et al. (2005) compared

different levels of vitamin D3 and 25-hydroxy vitamin D3 fed to the broiler breeder hens,

and it was found that 25-hydroxy vitamin D3 had a greater potency than vitamin D3 but

21

Chapter 2

only at very low levels of supplementation. Levels of 25-hydroxy vitamin D3 can be

easily manipulated through dietary supplements.

Sunshine

Vitamins D, and D, Vitamin D,

Liver *"

25-hydroxyvitamin D2

or 25-hudroxyvitamin D3

1 Kidney

1,25-dihydroxyvitamin D2 *~ or 1,25- dihydroxyvitamin D3

* 24,25-dihydroxyvitamin D2

or24,25-dihydroxyvitamin D3

Acts on the intestine to Increase calcium absorption

Role uncertain

Figure 2.4 The sources and metabolism of vitamin D (Nelson, 2000).

22

Chapter 2

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34

Chapter 3

CONNECTIVE STATEMENT I

Our overall goal is to identify non-MHC genes that segregate for variants that affect

disease resistance to Marek's disease in chickens. As discussed in the previous chapter,

vitamin D metabolism is an important modulator of the immune system as shown in mice

models and epidemiological studies in humans. We chose to analyze the variability of

three genes of the vitamin D metabolism in a non-inbred strain of chickens for which a set

of immune parameters had been measured. It showed that all three genes were highly

variable. Some non-redundant markers were then screened in the entire database for

association with cell surface markers of peripheral blood leukocytes. Since the vitamin D

metabolism can be modulated by dietary means, the identification of associated markers

may not only provide means to improve disease resistance by selection, but also by feed

additives.

35

Chapter 3

CHAPTER 3

Sequence Variations in Genes Encoding Enzymes Involved in

Vitamin D Metabolism and Association with Subclasses of Peripheral

Blood Mononuclear Cells in Chickens

Dana Praslickova ', Martin Lessard2, Donna L. Hutchings 3, David Zadworny1,

Urs Kiihnlein l

department of Animal Science, McGill University, 21-111 Lakeshore Road, Ste. Anne

de Bellevue, QC, Canada, H9X 3V9 2Dairy and Swine Research and Development Centre, Agriculture and Agri-Food Canada,

Lennoxville, QC, Canada, JIM 1Z3 3 Canadian Food Inspection Agency,Veterinary Biologies Section,Ottawa, ON, Canada,

K1A 0Y9

Corresponding author:

Urs Kiihnlein

Tel: (514) 398 7799

Fax:(514)398 7964

e-mail: [email protected]

36

mailto:[email protected]

Chapter 3

3.1 Abstract

The sequence variability of three genes involved in VD metabolism was analyzed

in a non-inbred White Leghorn strain. The genes were vitamin D 24-hydroxylase

(Cyp24), vitamin D receptor (VDR) and vitamin D binding protein (DBP). For each gene

two segments of about lkb each were sequenced in 20 individuals and the minimal

number of segregating haplotypes was determined. Intron variability was similar in all

three genes and ranged from 2.0 to 2.6 SNP per 100 bp. However, the number of

segregating haplotypes varied with the VDR being the most variable with a minimum of

15 haplotypes, followed by the Cyp24 and DBP with 7 and 5, respectively.

Blocks of co-segregating SNP in each gene were determined by aligning the

genotypes of the 20 individuals. Two SNP belonging to different blocks were analyzed

for association with the proportion of peripheral blood mononuclear cells (PBMC) that

displayed the surface antigens LYB, MHC class II, CD3, CD4, CD8, TCR1 (Ty5) and

TCR2 (Tap). The proportion of subsets of PBMC characterized by these antigens varied

significantly between genotypes defined by single markers or pairs of markers. The only

exception was the proportion of cells displaying the B-cell marker LYB. The results

indicate that genetic variations in genes that regulate vitamin D metabolism affect the

differentiation of cells of the immune system, in particular the proportion of cells that

express MHC class II and TCR1. Similar to mammals, such genetic variants may

therefore also be associated with disease resistance in poultry.

Keywords: vitamin D, vitamin D 24-hydroxylase, vitamin D binding protein, single

nucleotide polymorphisms, haplotypes, chickens, lymphocyte subclasses, TCR1, class II

MHC

37

Chapter 3

3.2 Introduction

Accessibility to the annotated chicken genome sequence has greatly facilitated the

genetic dissection of phenotypic traits. Primers to amplify DNA segments of known

location can be identified by inspecting the genome sequence; genotypes can be classified

and diagnostic markers analyzed for trait associations. It will lead to an understanding of

the complex dynamics of the genotype-phenotype relationship and enable us to conduct

selection at the DNA level. Such selection can be used as an adjunct to phenotypic

selection and may be particularly important for developing disease resistant strains of

poultry.

In this communication we analyzed variability of three genes associated with

vitamin D (VD) metabolism in chickens. It has been shown in humans and mice that VD

status is associated with the innate and adaptive immune response, in addition to its

classical role in the regulation of Ca2+ and PO4 " homeostasis. In particular, VD reduces

the susceptibility to cancer, to auto-immune disorders and to infection by Mycobacterium

tuberculosis (van Etten and Matthieu, 2005; Liu et ah, 2006; Wee-Chian et ah, 2005;

Agoston et ah, 2006). Genes that regulate vitamin D metabolism are therefore functional

candidate genes for variants that affect the immune response and hence disease resistance.

Clarification of the interrelationship between vitamin D metabolism and immune

response may provide rationales for improving disease resistance by dietary means in

addition to selection for appropriate alleles.

Three genes were analyzed, the vitamin D binding protein (DBP), the 24-

hydroxylase (Cyp24) and the vitamin D receptor (VDR). VDR is a transcription factor

that binds to VDR response elements. When activated by binding to 1,25-hydroxy VD3

(l,25-(OH)2D), it modulates gene transcription (Uitterlinden et ah, 2004). Cyp24 plays a

pivotal role in the control of levels of l,25-(OH)2D by inactivating 25-hydroxy VD3 and

l,25-(OH)2D through hydroxylation at the 24 position (Masuda et ah, 2006). It is itself

under the transcriptional control of a VDR response element. DBP has multiple roles,

some of which are unrelated to VD metabolism (Gomme and Bertolini, 2004; Speeckaert

et ah, 2006). It serves as a transport protein of VD and fatty acids, acts as an extracellular

actin scavenger, and is involved in macrophage chemotaxis.

38

Chapter 3

The chicken strain analyzed had been founded from 4 North American

commercial White Leghorn strains in 1958 and was propagated as a closed breeding

population while maintaining a large effective population size (Gowe et ah, 1959, 1993).

A database consisting of production traits and immune traits had been established in

1995. It provides us with the opportunity to analyze variability within or between genes,

and to correlate such variants with production traits and immune traits.

3.3 MATERIALS AND METHODS

3.3.1 Strains of chickens and data collection

Chickens were from strain 7, a strain that was established by mating 4 commercial

North American White Leghorn strains in 1958 (Gowe et al.,1959; Gowe et ah, 1993). It

was propagated by pedigreed random mating using 100 sire families and mating one sire

to two females in every generation. At the time of data collection, the inbreeding

coefficient was estimated to be 0.026 (Kiihnlein et ah, 1990). The collection of

production traits in this strain has been described previously (Parsanejad et ah, 2002).

3.3.2 Flow cytometry

All birds were immunized between 8-10 month of age with vaccines against

Marek's disease virus (MDV), infectious bronchitis disease (IBD) and avian

encephalomyelitis virus (AEV). Twelve days after the MDV, IBD and AEV injections,

blood samples were taken to evaluate cell mediated immunity and characterize different

populations of leukocytes by flow cytometry. Peripheral blood mononuclear cells

(PBMC) were prepared from heparinized blood samples as described by Lessard et al.

(1993). Briefly, whole blood samples were layered onto Lymphocyte-H (Cederline

Laboratories, Hornby, Ontario, Canada) and centrifuged. The PMBC were harvested and

washed three times in Hank's basal salt solution and adjusted to 5 x 106 cells/ml in PBS,

pH 7.2. Monoclonal antibodies specific for the T-cell surface antigens CD3, CD4 and

CD8 and the T-cell receptors TCR1 (Ty8) and TCR2 (Ta|3) were purchased from

Southern Biotechnologies Associates (Birmingham, AL 35226). Pan-B cell monoclonal

antibody (LYB) directed against Bu-la cell surface antigen was provided by Dr. Ratcliffe

39

Chapter 3

(McGill University, Montreal, Ca) and antibody against MHC class II antigen by H.

Lillehoj (USDA, Agricultural Service, Livestock and Poultry Sciences Institute,

Beltsville, MD 20705). PBMC were stained with 1:100 dilutions of the monoclonal

antibodies and goat anti-mouse IgG-FITC conjugate as previously described (Griebel et

al, 1987; Lillehoj et al, 1988). Data from 10,000 cells were collected on a FACScan

(Becton Dickinson, Missasuaga, Ontario, Canada) and two-parameter analysis of

forward-angle light scatter was used to channel the lymphocyte population to

fluorescence analysis. The percentage of positive cells was determined and corrected for

the percentage of PBMC that stained with mouse monoclonal antibody specific for the

CD5 marker on equine leukocytes (HT23A monoclonal IgGl class; VMRD at Pullman,

WA).

3.3.3 Genetic analysis

DNA was extracted from erythrocytes of 20 unrelated males of strain 7. Blood

samples were lysed, digested with proteinase K, extracted with phenol-chloroform and

ethanol precipitated following standard procedures. Primers for two DNA segments in

each gene were designed using the NIH database and the DMAman for Windows™

(Lynnon Bio Soft, Vaudreuil, Quebec, Canada). Initially we used the cDNA sequences to

search for homology with unassembled genomic clones. At a later date we used the

assembled and annotated chicken genomic sequence (http://www.ncbi.nlm.nih). The

primers used to amplify gene segments are listed in Table 3.1. Amplification conditions

were as described previously (Parsanejad et al, 2004). The reverse and forward

sequences of the amplified products were determined by Genome Quebec (McGill

University and Genome Quebec Innovation Centre, Montreal, Quebec, Ca) using the

amplification primers as sequence primers. SNPs and indels were read by visual

inspection of the sequence traces. In clean runs there were no difficulties in reading

heterozygotes. Two SNPs of each gene were chosen and genotyped by primer extension

in the entire database using either Fluorescence polarization - single base extension (FP-

SBE) genotyping or the GenomeLab™ SNPstream® Genotyping System (formerly

Orchid SNPstream UHT).

40

http://www.ncbi.nlm.nih

Chapter 3

3.3.4 Statistics and graphics

For statistical evaluations and graphs we used the NCSS software (Hintze, 2004).

Association analyses were conducted by GLM procedures. Linkage disequilibria between

pairs of marker genotypes were analyzed by exact tests without breaking up the

genotypes at individual loci (Lewis and Zaykin, 2001). The frequency of double-

heterozygotes within a gene was estimated by using Hill's iteration method, a procedure

that assumes Hardy-Weinberg equilibrium (Hill, 1974).

3.4 RESULTS

3.4.1 Determination of blocks of co-segregating SNP

Two randomly chosen sections of each gene were sequenced in 20 chickens

(Table 3.2). On average 2 to 2.6 variants per 100 nucleotides were observed in introns, a

frequency that was similar to that observed previously for ornithine decarboxylase (ODC)

in the same set of individuals (Parsanejad et al, 2004). Although there was no difference

of the overall density of variants in the different genes, there were considerable

differences within intronic regions of the same gene, indicating sequence conservation.

As an example, in the DBP gene, 19 variants were observed in 530 bp of segment 1, but

none in 448 bp of segment 2. Only four mutations were observed in exons, three of which

were synonymous substitutions (Table 3.2).

Blocks of co-segregating markers in the population of interest were determined by

aligning the genotypes and sorting the markers by zygocity. The minimal requirement for

markers to co-segregate is that they have the same zygocity among all individuals and

that the homozygotes are in phase (e.g. if in one individual the genotype at PI is A/A and

at P2 is C/C then it has to be so in all homozygotes). Table 3.3 shows the DBP gene as an

example. In this gene a total of 19 markers were identified. Based on the observed

genotypes, markers co-segregated in a minimum of five blocks. In the Cyp24 gene, 29

markers were observed, forming 7 blocks and in the VDR gene 14 markers forming 11

blocks.

By first defining blocks, redundant genotyping can be avoided. The number of

blocks is also an indicator of the minimal number of haplotypes. Assuming maximal

41

Chapter 3

parsimony, markers in a single block define 2 haplotypes and each additional block adds

a haplotype. Hence k blocks will give rise to a minimum of k+1 haplotypes. Additional

haplotypes are to be expected if markers were scrambled by historical recombination or

gene conversion. Indeed, inspection of the marker genotypes of the three genes analyzed

here indicates that the linkage disequilibrium between markers is not maximal and hence

additional haplotypes must have been formed by past recombination (Table 3.3).

The k+1 haplotypes can combine to form (k+1) • (k+2)/2 different genotypes.

Hence the minimal number of genotypes expected to segregate in strain 7 ranges from 21

for the VDB gene to a staggering 71 for the VDR gene.

Two markers from different blocks were chosen in each gene and genotyped in

the entire population. As expected in a randomly mated population, genotypes defined by

single markers did not deviate significantly from Hardy-Weinberg disequilibrium

(0.21<P<0.89). Linkage disequilibria between markers in the same gene were significant

(P<10~6). However, as already inferred from the sequence analysis of a sub sample, the

linkage disequilibrium although significant, was not maximal; reflecting historical

recombination, gene conversions or reoccurrence of identical mutations. Markers located

in different genes were not at disequilibrium (P>0.28), with the exception of the marker

S1P1 in the Cyp24 gene and the marker S2P2 in the VDR gene (P=0.009).

3.4.2 Association of single genes with cell differentiation antigens on peripheral

blood mononuclear cells

Genotypes defined by single markers or pairs of markers in the same gene were

analyzed for association with the distribution of PBMC classified by cell surface antigens

(Table 3.4). The cell surface antigen measured were the T-cell receptors TCR1 and

TCR2, the T-cell surface markers CD3, CD4 and CD8, the B-cell antigen LYB and the

MHC class II antigen. Since proportions rather than the absolute titers of cells on

different PBMC classes were measured, the values are highly correlated (Table 3.5). As

an example LYB is negatively associated with all markers on T-cells, while all the T-cell

markers are positively associated with CD3, an antigen common to all T-cells.

42

Chapter 3

Analysis of genotypes defined by single markers revealed associations between

DBP S1P15 and the frequency of TCR1 positive cells (P=0.0082) and between VDR

S1P4 and the frequency of MHC class II positive cells (P=0.0007).

A finer subdivision of the genotypic classes can be achieved by considering two

markers simultaneously. Two markers from different blocks will yield a total of 4

different haplotype groups and hence 10 different genotypic subgroups. Such an analysis

is shown for the VDR gene (Figure 3.1). Combining the genotypic groups that occur at

frequencies of less than 10, significant associations are observed for the CD8/CD3 ratio in

addition to MHC class II as already observed for one of the single marker genotypes. It is

noteworthy that there is no common association pattern for these two phenotypes. Hence

some genotypes have differential effects on the frequency of MHC class II positive cells

and on the CD8/CD3 ratio.

3.4.3 Gene interaction

Gene interaction was observed for all three genes under analysis, affecting TCR1,

TCR2, MHC class II and CD4 (Table 3.5). An example is the TCR1 counts for

combinations of marker genotypes of S1P3 in the Cyp24 gene and SIP 15 in the DBP

gene (Figure 3.2). It shows that the contrast between the genotypic classes A/A and G/A

in the vitamin D binding protein is only significant different when the S1P3 marker

genotypes in the Cyp24 gene is A/A, but not when it is G/A. The probability distributions

for these four genotypes indicate not only differences in the mean, but also in the

variance, perhaps reflecting the presence of additional phenotypically different genotype

subgroups or differences in the buffering capacity of the genotype classes towards genetic

variations in other genes (Figure 3.3).

3.4.4 Correlation with production traits

The number of cells in different PBMC classes was not correlated with body

weight measurements, rate of egg-laying or sexual maturity (data not shown). However,

correlations were significant for egg specific gravity (SPG) and the ratio between egg

weight and specific gravity (an approximate measure of egg calcium) (Table 3.6).

Contrary to expectation, these traits were not associated with any of the markers analyzed

43

Chapter 3

here. An exception may be an interactive effect (P=0.05) of the marker genotypes DBP

S1P4 and VDR S1P4 on the ratio EWT/SPG, a marker combination that was not

associated with any of the PBMC counts.

3.5 DISCUSSION

The main purposes for identifying markers associated with quantitative traits in

chickens are (1) to gain information about gene function and (2) to apply selection at the

DNA level. In the former case the main focus is on genes that have strong effects on the

trait of interest. In a randomly mated population, individuals with such genes are outliers

of the trait distribution. When the focus is on marker assisted selection, genes that are

frequent but have relatively minor phenotypic effects are equally important. Selection for

such genes is less likely to destroy the fabric of gene interaction that had been established

by long-term historical selection. Association analysis, that is the association of markers

with traits in a randomly propagated population, is an approach geared towards

identifying such markers.

In a randomly mated population, associations will only be observed for markers

that are closely linked to the putative trait mutation. It is therefore logical to analyze

markers in candidate gene, i.e. genes that are biologically relevant for the trait of interest.

An obvious source are genes that affect similar traits in other species. A search for such

markers may be conducted in elements of a gene that are suspected to affect gene

function, such as exons or elements that regulate gene expression. However, our

understanding of gene regulation is incomplete, and the choice of regions for marker

analysis is far from obvious. Indeed, in our analysis we found genomic regions with no

apparent function that had low variability, indicating evolutionary constraints. We

therefore focused our analysis on intronic sequences that are more variable. Sequence

differences enable us to delineate the major haplotypes (blocks of SNP) that segregated in

the population of interest. Putative quantitative trait mutations are expected to co-

segregate with one of these haplotypes.

For the delineation of genotypes we decided to use direct sequencing of PCR

amplified genomic segments. It has the disadvantage over subcloning that indel-

heterozygotes are difficult to read and that double-heterozygotes are not resolved. Despite

44

Chapter 3

these drawbacks, direct sequencing of PCR products is less labor intensive, it doubles the

sample size and blocks of co-segregating markers can still be discerned.

Despite domestication and intensive selection, chickens have a surprisingly

polymorphic genome whose nucleotide diversity is about 5-10 times greater than that of

the human genome (International Chicken Polymorphism Consortium, 2004; Tishkoff

and Verrelli, 2003). Further, for all three genes analyzed here (and for genes previously

analyzed), we found that the Red Jungle fowl haplotype as defined by the NIH database,

was still present in the sample of 40 White Leghorn genomes. It indicates that in contrast

to humans, chickens have not gone through a genetic bottle neck and that the intensive

selection since domestication of the chickens has not led to a loss in genetic diversity.

Such a conclusion has also been reached on the basis of genome wide comparison of the

SNP spectra in a wide variety of chicken breeds (International Chicken Genome

Sequencing Consortium, 2004). Selection may actually have contributed to the

maintenance of genetic diversity through selective advantage of hybrids or gene

interaction (Kuhnlein et al., 1989).

However, in contrast to the average nucleotide diversity, the number of haplotypes

varied considerably, ranging from 3 for ornithine decarboxylase (ODC) (Parsanejad et al,

2004) to more than 15 for the VDR receptor. It indicates that there was selection pressure

on some of the genes. Maintenance of the nucleotide diversity but reduction in the

number of haplotypes may reflect that some haplotypes may be phenotypically equivalent

despite being genetically distant from each other.

The significance of associations is critically dependent on the sample size.

Because of the extensive genotypic variation, the number of observations within

genotypic classes defined by haplotypes may be small. It is therefore necessary to

consider only one or two markers at a time. A single marker will subdivide the data set

into three groups corresponding to the three marker genotypes, while two markers will

yield 9 groups. By analyzing markers diagnostic for different sets of haplotypes, the

partitioning that yields the largest contrast can be determined and the haplotype that

harbors a putative quantitative trait mutation can be identified. Similar considerations

apply to the analysis of gene interaction, where in most cases only two markers can be

included in the statistical analysis.

45

Chapter 3

Associations were observed for most PBMC surface antigens and/or ratios,

reflecting the general effect of VD on the proportion of cell types of the immune system.

The pattern of associations varied with different genotypes, indicating qualitatively

different responses to different genetic variants. Whether this is a reflection of differences

in the regulation of VD or whether the enzymes studied here may have alternative

functions is unknown. Such alternative functions are known for DBP, a protein that

regulates VD transport, is involved in actin metabolism and serves as a chemokine.

We observed the most significant effects of genetic variants on the proportions of

MHC class II and TCR1 (Ty8) positive cells. The majority of MHC class II positive

leukocytes in peripheral blood are cells of the monocytes/macrophages lineage and B-

cells. VD may affect the former cell type, since there was no significant effect on the

proportion of the LYB antigen presenting cells. Little is known about the biological

function of TCR1 positive cells in chickens. They are predominantly CD4", CD8" and the

y8 receptors which are less diverse than the aP receptors of the TCR2 cells (Chen et al,

1988; Dieterlen-Lievre, 1994). They have regulatory, cytotoxic and possibly also antigen

presenting functions. In the murine species and mammals they have been shown to have

antimicrobial and antitumor properties. In our genetic analysis, the DBP appears to be a

determinant of the proportion of TCR1 positive cells. The molecular mechanism of this

relationship is unknown, but may be related to the chemotactic properties of the protein

rather than to its involvement in VD metabolism.

The complexity of the cross-talk between cells of the immune system renders it

difficult to predict the outcome of genetic selection for different marker genotypes in

terms of disease resistance. As an example, selection for the marker A3 of the VDR gene

(Figure 3.1) would optimize the number of MHC class II positive cells, but will lead to an

overall reduction of the CD8/CD3 ratio in the strain of interest. Hence this genotype may

promote the humoral immune response while reducing the cell mediated immune

response. The effect of such variations on the resistance to pathogens has to await

experimental selection and challenge tests.

46

Chapter 3

3.6 ACKNOWLEDGMENTS

This work was supported by grants from the Natural Sciences and Engineering

Council of Canada, the Poultry Industry Research Council of Canada and Shaver Poultry

Research Farms Ltd.

47

Table 3.1 Primers used to amplify segments of the VDR, DBP and Cyp24 genes.

VDR Segment 1 F: gctgggaggagaaaggagtgtt

R: aacgcacgcacttctcagga

VDR Segment 2 F: gctggggagggaagattgagag

R: agcttctggatcatcttggcgt

DBP Segment 1 F: taagaaaggtcactggacgg

R: cctgcagcaaagtccttcg

DBP Segment 2 F: agaggatgcgcggctgagatgt

R: ctctgttcccatttgctgtcg

Cyp24 Segment 1 F: tccaactccctgctttcttcc

R: gtggtggtttcctcagaagc

Cyp24 Segment 2 F: ggtaagatgtggctgtgggt

R: tgagcagattgtgtggcagg

48

Chapter 3

Table 3.2 Distribution of the polymorphisms.

Gene and Chromosome

location

VDR Chr.Un

DBP Chr.4*

Cyp24 Chr. 20

Start of sequence

SI: 2841

S2 24727

SI: 20712134

S2:20709442

Sl:n.d.d

S2: n.d.

Intron

DNA sequenced

468 bp

241 bp

530 bp

448 bp

617 bp

384 bp

Number of SNP or indel

12

2

19

0

19

7

Exon

DNA sequenced

79bpa

223 bp

250 bp

350 bp

97 bp

245 bp

SNP or indel

1

0

lb

0

0

3 C

Unique Genotypes

13

4

9

Blocks

11

5

7

a Non-coding exon

Synonymous codon change for leucine (TTG<-^CTG) c Two mutations were codon changes for glutamine and isoleucine, respectively. A third

mutation led to a change of histidine to leucine

The genome sequence needs to be clarified. Exons and introns were determined by

hybridization with mRNA sequence (GenBank accession number AF428109). The

assembly of the Red Jungle Fowl genomic sequence for this region needs to be revised.

The cDNA and our two sequences hybridize to contig NW-06081.1 (chromosome

location unknown) and NW-606635.1 (chromosome 20).

* contig NW-0603457.1

49

Chapter 3

Table 3.3 Identification of groups of co-segregating markers in the genes encoding the

DBPa.

Marker

P-2 P-l P5 P7 P8 P9

PlOa Pl l P12 P19

Genotype and number of observations (N)

#1

(N=16)

C C G C C G G G A G

#2

(N=2)

C C G C C G G G A G

#3

(N=l)

C C G C C G G G A G

#4

(N=l)

T/C T/C A/G T/C T/C A/G A/G A/G A/C A/G

RJb

T T A T T A A A C A

Block

PI P2 P3 P4 P10

T A T A T

T/C A/G T/C A/G T/C

T/C A/G T/C A/G T/C

T/C A/G T/C A/G T/C

C G C G C

2 2 2 2 2

P6 P14

T inc

T in

T/G in/del

T in

T in

3 3

P13 A A/G G A/G G 4

P15 T T/C C T T 5

a Two segments of the gene were sequenced in 20 individuals. Only one of the segments

was polymorphic (see Table 1). The different genotypes were identified (columns) and

the markers sorted on the basis of zygocity at the individual markers (rows). The markers

formed 3 groups with 10, 5 and 2 markers whose alleles' co segregated, respectively, and

two groups of single markers that did not co-segregate with any of the other markers.

Pair-wise comparison of markers indicated that with the exception of PI5 all were at

maximal linkage disequilibrium (i.e. one of the four possible combinations was missing).

PI5 was not at maximal disequilibrium with markers in group 2. The genotypes at PI and

50

Chapter 3

PI5, for example, can only be explained if of all four allelic combinations (i.e. T-T, T-C,

C-C and C-T).

Marker genotype of the red jungle fowl according to the NCBI database. Genotype #4

can be explained as a heterozygote between alleles of RJ allele and genotype #1 c Insertion of ATTTC

51

Chapter 3

Table 3.4 Association of single and pairwise combinations of marker genotypes with cell

surface antigens on leukocytes a.

Cyp24 -S1P1

Cyp24 -S1P3

DBP-S1P4

DBP-S1P15

VDR-S1P3

VDR-S5P2

Vitamin D 24-hydroxylase Cyp24-S1P1 TCR1/ TCR2

Cyp24-S1P3 CD8/CD3

n.s.

Vitamin D binding protein DBP-S1P4 n.s.

n.s.

n.s.

DBP-SlP15b

TCR2

TCR1

TCR1/CD3

n.s.

TCR1

TCR1/CD3

TCR1/TCR2

Vitamin D receptor

VDR-S1P3

CD4

TCR1/CD3

TCR1

TCR1/CD3

n.s.

MHC class II

VDR-S5P2 CD4

MHC class II

CD4/CD3 n.s.

n.s.

n.s.

CD8/CD3

n.s.

a The surface antigens analyzed were CD3, CD4, CD8, LyB and MHC class II. The

analysis included the direct FACS counts and the ratios CD4/CD3, CD8/CD3,

TCR1/CD3, TCR2/CD3 and TCR1/TCR2. Analysis was by GLM. Single marker effects

or significant marker interaction within a gene are indicated in italics. Significant

interactive terms that were significant (P<0.05) are indicated in regular script.

The rare genotype A/A was omitted from the analysis

52

Chapter 3

Table 3.5 Spearman rank correlation between FACS counts.

CD3

CD4

CD8

TCR1

TCR2

MHCII

LyB

CD3

—

CD4

0 37***

—

CD8

0.30***

0.18*

—

TCR1

0 41 ***

-0.30***

-0.04

—

TCR2

0 44***

0.75***

0.33***

—0 37***

—

MHCII

-0.16*

0.17*

0.07

-0.11

0.08

—

LYB

-0.43***

-0.36***

-0.13

-0.24**

-0.26**

0 37***

—

*P<0.05; **P<0.01; ***P<0.001

N=182

53

Chapter 3

Table 3.6 Correlation between egg quality traits and leukocyte cell surface antigens.

Surface antigen

CD3

CD4a

CD8

TCR1

TCR2

TCR1/CD3

TCR2/CD3

TCR1/TCR2

MHCII

LYB

Spearman-rank correlation coefficient

and significance (N=145)

EW

-0.09

0.06

0.05

-0.16*

0.09

-0.01

0.17*

-0.17*

0.17*

0.07

SPG

-0.24**

-0.07

-0.04

-0.16*

-0.03

-0.07

0.11

-0.08

0.10

0.22**

Shell

densityb

-0.23**

0.03

0.00

-0.26**

0.09

-0.19*

0.24**

-0.22**

0.22**

0.20*

* PO.05 **P<0.01 a No significant associations were observed for the ratios CD4/CD3 and CD8/CD3 bThe shell density is approximated as (SPG minus 1) • (EW) (Parsanejad et al, 2004).

54

Chapter 3

9.0

8.0

b 7.0

CO

to

% 6.0 O X

5.0

4.0

I

I I _1 T_ A1/A1 A1/A3 A1/A2 A3/A3 A2/A3 other

VDR genotype

0.4

.o •i—<

03 i—

CO

8 0.3 oo O O

0.2

I I

A1/A1 A1/A3 A1/A2 A3/A3 A2/A3 other

VDR genotype

55

Chapter 3

Figure 3.1 Association of VDR genotypes with the MHC class II count and the CD8/CD3

ratio. The two markers S1P4 and S2P2 in the VDR gene defined four different haplotype

groups (Al: A-G; A2: G-G; A3: G-A; A4: A-A). These haplotypes in turn define 10

different genotypes. The haplotype frequencies estimated by the EM algorithm (Hill,

1974) were Al: 0.43; A2: 0.18; A3: 0.34; and A4: 0.05. Two genotypes (A1/A3 and

A2/A4) are not distinguishable. However, the expected frequency of the genotype A2/A4

was only 0.02 as compared to 0.29 for the genotype A1/A3. These two genotypes were

therefore designated as A1/A3. Rare genotypes (<10) are grouped as "other". They

comprise A2/A2, A1/A4 and A3/A4. The genotype A4/A4 was not observed in the data

set of 166 individuals. ANOVA indicate the MHC class II counts (upper panel) and

CD8/CD3 ratio (lower panel) differed significantly at P=0.0007 and 0.015, respectively.

Comparison of groups showed a higher MHC class II counts for genotypes A3/A3

than for any other any other genotypic class. It indicates that A3 is a recessive haplotype

for a high MHC class II titer. For the CD8/CD3 ratio significant contrasts were

A1/A2>A1/A1, A1/A3 or A3/A3 as well as A1/A3 < A1/A2, A2/A3 or "other".

56

Chapter 3

28.0

_ 25.0 j

O

o CD

19.0

16.0

I

I I

A/A A/A G/A G/A G/G Cyp24 S1P3 genotype A/A G/A A/A G/A A/A DBP S1P15 genotype

Figure 3.2 Mean of TCR1 counts for different genotype combinations of the marker S1P3

in the Cyp24 gene and S1P15 in the DBP gene. Three genotypic combinations that were

not observed or only represented once (G/G-A/A; G/G-G/A, G/A-G/G) were omitted

from the analysis. The numbers of observation in the order listed on the ordinate were 38,

12, 36, 14 and 11. Contrasts were significant at P=0.023. Pair wise contrasts indicated

that the GAGA genotypes had a TCR1 count that was significantly higher than in all

other genotypic classes.

57

Cyp24S1P3=G/A

40.0

30.0

cE o h-co 20.0 o cc

10.0

4 A ^

±£D

A A

DBPS1P15 A G A O AA

cP 43X0

0 .0 H — i — i — i — i — i — i — r * - i — i — i — i — i — i — i — i 0.0 0.3 0.7 1.0

Percentile

40.0

30.0

a: o w 20.0 CO

LL

10.0

0.0 0.0

Cyp24S1P3=A/A

DBPS1P15 © AA A GA

0.3 0.7 Percentile

58

Chapter 3

Figure 3.3 Interactive effect between the DBP gene and the Cyp24 gene on the TCR1

ratio. Upper panel: Percentile distribution of the DBP SIP 15 genotypes G/A and A/A

when the Cyp24 S1P3 genotype is G/A. The two distributions are different (Kolmogorov-

Smirnoff test for unequal distributions, P=0.009). Differences are more pronounced at the

lower end of the distribution, resulting in an unequal variance (Variance-ratio equal

variance test, P=0.045). Lower panel: Percentile distributions for the DBP S1P15

genotypes G/A and A/A when the Cyp24 S1P3 genotype is A/A. The two distributions do

not differ significantly.

59

Chapter 3

3.7 REFERENCES

Agoston E.S., Hatcher M.A., Kensler T. W. and Posner G. H. (2006) Vitamin D analogs

as anti-carcinogenic agents Anticancer Agents Med. Chem ., 6: 53-71

Dieterlen-Lievre (1994) Hemopoiesis during avian ontogeny. Poultry Sci. Rev., 5: 273-

305

Chen, C.L., Cihak J., Losch U. and Cooper M.D. (1988) Differential expression of two

T-cell receptors, TCR1 and TCR2 on chicken lymphocytes. Eur. J. Immunol., 18:

539-43

Gomme, P. and Bertolini, T. (2004) Therapeutic potential of vitamin D-binding protein.

Trends Biotech., 22: 340-345

Gowe, R.S., Fan-full, R.W, McMillan, I. and Schmidt, G.S. (1993) A strategy for

maintaining high fertility and hatchability in a multi-trait egg-stock selection

program. Poultry Sci., 72:1433-1448

Gowe, R.S., Robertson, A. and Latter, B.D.H. (1959) Environment and poultry breeding

problems . 5. The design of poultry control strains. Poultry Sci., 38:462-471

Griebel, P.J., Qualtiere, L., Davis, W.C., Lawman, M.J., Babiuk, L.A. (1987) Bovine

peripheral blood leukocyte subpopulation dynamics following a primary bovine

herpes virus-1 infection. Viral Immunol., 1: 267-286

Hill, W. G. (1974) Estimation of linkage disequilibrium in randomly mating populations.

Heredity, 33:229-239

Hintze, J. (2004) NCSS and Pass. Number Cruncher Statistical Systems. Kaysville, Utaj.

www.NCSS.com

International Chicken Polymorphism Consortium (2004) A genetic variation map for

chicken with 2.8 million single-nucleotide polymorphisms. Nature, 432: 717-721

Kuhnlein, U., Zadworny D., Dawe Y., Fairfull R.W., and Gavora J.S. (1990). Assessment

of inbreeding by DNA fingerprinting: Development of a calibration curve using

defined strains of chickens. Genetics, 125: 161-165

Kuhnlein, U., Sabour J., Gavora J.S., Fairfull R.W. and Bernon D.E. (1989) Influence of

selection for egg production and MD resistance on the incidence of endogenous

viral genes in White Leghorns. Poultry Sci., 68:1161-1167

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Lessard, M., Yang, W.C., Elliot, G. S., Deslaurier, N., Brisson, G.J., Van Vleet, J.F. and

Schultz, R.D. (1993) Suppressive effect of serum from pigs and dogs fed a diet

deficient in vitamin E and selenium on lymphocyte proliferation. Vet. Res., 24:

291-303

Lewis, P. O., and Zaykin, D. (2001) Genetic Data Analysis: Computer program for the

analysis of allelic data. Version 1.0 (dl6c). Free program distributed by the

authors over the internet from http://lewis.eeb.uconn.edu/lewishome/

software.html

Lieu, P. T., Stenger, S., Li, H., and Wenzel, L., Tan, B.H., Krutzik, S., Ochoa, M.T.,

Schauber, J., Wu, K., Meinken, Ch., Kamen, D.L., Wagner, M., Bals, R.,

Steinmeyer, A., ZUgel, U., Gallo, R.L., Eisenberg, D., Hewison, M., Hollis,

B.W., Adams, J.S., Bloom, B.R. and Modlin, R.L. (2006) Toll-like receptor

triggering of a vitamin D-mediated human antimicrobial response. Science, 311:

1770-1773

Lillehoj, H.S., Lillehoj, E.P., Weinstock, D. and Schat, K.A. (1988) Functional and

biochemical characterization of avian T-lymphocyte antigens identified by

monoclonal antibodies. Eur. J. Immunol., 18: 2059-2065

Masuda, S., Byford, V., Arabian, A., Sakai, Y., Demay, M.B., St.-Arnaud R. and

Glenville, J. (2004) Altered pharmacokinetics of la,25-dihydroxyvitamin D3 an

25-hydroxyvitaminD-24-hydroxylase (Cyp24al) Endocrinology, 146: 825-834

Parsanejad, R., Praslickova, D., Zadworny, D. and Kiihnlein, U. (2004) Ornithine

decarboxylase: haplotype structure and trait associations in White Leghorn

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Parsanejad, R., Torkamanzehi, A., Zadworny, D. and Kuhnlein, U. (2003) Alleles of

cytosolic phosphoenolpyruvate carboxykinase (PEPCK): trait association and

interaction with mitochondrial PEPCK in a strain of White Leghorn chickens.

Poultry Sci., 82:1708-15

Parsanejad, R., Zadworny, D., and Kunhlein, U. (2002) Genetic variability of the

cytosolic phosphoenolpyruvate carboxykinase gene in White Leghorn chickens.

Poult Sci., 81:1668-70

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Uitterlinden, A. G., Fang, Y., Van Meurs, J.B., Pols, H.A., Van Leeuven, J.P. (2004)

Genetics and biology of vitamin D receptor polymorphisms. Gene, 338: 143-156

Ren, S., Nguyen, L., Wu, S., Encinas, C., Adams, J.S., and Hewison, M. (2005)

Alternative splicing if vitamin D-24-hydroxylase. J. Biol. Chem., 280: 20604-

20611

Speeckaert, M., Huang, G., Delanghe, J. R. and Taes, Y. E. (2006) Biological and clinical

aspects of the vitamin D binding protein (Gc-globulin) and its polymorphism.

Clin. Chim, Acta, 372: 33-42

Tishkoff, S., and Verelli, B.C. (2003) Patterns of human genetic diversity: Implications

for human evolutionary diversity. Annu. Rev. Hum. Genet., 4: 293-340

Van Etten , E. and Mathieu, C. (2005) Immunoregulation by 1,25-dihydroxyvitamin D3:

basic concepts. J. Steroid Biochem. Mol. Biol.., 97: 93-101

Wee-Chian, L., Hanauer, S. B. and Li, Y. C. (2005) Mechanisms of disease: vitamin D

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2:308-315

62

Chapter 4

CONNECTIVE STATEMENT II

Based on previous research that indicated an association of variants in the GH gene, the

GHR gene and the chemokine MIP-3a with Marek's disease resistance, we wanted to

conduct DNA based selection in commercial White Leghorn chickens. In contrast to

previous studies, we used vaccinated chickens to make our study more relevant to the

commercial situation. This study also provided us with a database to analyze the

association of markers in other genes with disease resistance.

63

Chapter 4

CHAPTER 4

Effect of Marker Assisted Selection on Indicators of Marek's Disease in

a Vaccinated Commercial White Leghorn Strain

Dana Praslickova1, Shayan Sharif2, Aimie J. Sarson2, Mohamed Faizal Abdul-Careem2,

David Zadworny , Al Kulenkamp , George Ansah and Urs Kuhnlein

'Dept. of Animal Science, McGill University, 21111 Lakeshore Rd., Ste. Anne de

Bellevue, Qc, Canada, H9X3V9 2 Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph,

On, Canada, NIG 2W1 3Shaver Poultry Breeding Farms Ltd., 500 Franklin Boulevard, Cambridge, On, Canada,

N1R 8G6


Urs Kuhnlein

Tel.: (514) 398 7799

Fax:(514)398 7964


64


Chapter 4

4.1 ABSTRACT

A commercial strain of chickens was selected for markers located in the GH gene,

the GH receptor gene and the gene for the chemokine MIP-3a . Female offspring of the

selected and a control population were vaccinated with HVT at hatch and challenged by

intraperitoneal injection of 250 PFU of the RB-1B strain of the MD virus at 5 days of

age. Two challenge tests were conducted in hatches collected three month apart and the

course of MD monitored for 8 weeks. The MD indicators measured were the viral titers in

feather-tips, mortality, presence and organ distribution of MD lesions, body weight,

spleen weight and bursa weight. Analysis by ANOVA or non-parametric statistics

showed significant interaction between population and trial. Specifically, in trial 1 the

viral titer in the selected population was 2 fold lower than in the control, while in trial 2

the situation was reversed. Similar observations were made for other MD associated

parameters. Further, the organ distribution indicated a different course of the disease in

the two trials. The results indicate that the effect of genes on MD may depend on

environmental factors.

Keywords: Marek's disease, disease resistance, marker associated selection, survival,

distribution of MD lesion, bursa weight

65

Chapter 4

4.2 INTRODUCTION

Marek's disease (MD) is a highly contagious, re-emerging and economically

important disease in the poultry industry. Marek's disease is caused by an avian

herpesvirus. It is characterized by paralysis, as a result of lymphoid infiltration into

peripheral nerves or inflammation of the brain, lymphomas in various organs including

the skin, immunosuppression, and blindness accompanied by non-specific signs such as

weight loss (Zelnik, 2004). The economic impact of MD on the world poultry industry is

thought to be in range of US$1-2 billion annually (Morrow and Fehler, 2004).

Control of MD is based on effective vaccination, proper biosecurity and selection

for genetic resistance. But MD herpesvirus (MDV) can evolve towards greater virulence

and the occurrence of MD in vaccinated flocks indicates that MDV can bypass current

methods of disease control (Gimeno, 2004).

Genetic resistance to MD has been known for more than 60 years (Calnek, 1985).

Resistance has been usually assessed based on the mortality or the development of

lesions. Asmundson and Biely (1932) first demonstrated significant differences among

families. Cole (1968) extended this knowledge by comparing susceptibility and/or

resistance of different commercial families challenged by MDV. Later viral titration by

plaque assays or the polymerase chain reactions in various organs has been added as an

indicator for the presence of the disease (Baigent et al, 2005).

The first genes that were shown to affect MD resistance were genes involved in

the immune response. Hansen et al. (1967) found an association of resistance with blood

group B haplotypes (Keller and Sevoian, 1983; Calnek and Witter, 1997). The blood

group B locus is closely linked to the major histocompatibility locus (MHC) that

regulates the processing and presentation of antigens (Drof, 1981; Briles, et al, 1983;

Kaufman and Venugopal, 1998). Haplotypes B13, B15, and B19 were associated with

susceptibility, whereas the B21 haplotype was associated with resistance to MD (Bacon et

al, 2001). Selection for B haplotypes has been used in the poultry industry and several

mechanisms have been proposed to explain the association between B-complex variation

and resistance to MD (Kaufman and Salomonsen, 1997). However, since MHC genes are

specific for peptides, it is feared that such selection may reduce the repertoire of antigen

recognition and render chickens more susceptible to other infectious diseases.

66

Chapter 4

In addition to MHC, other genes may have a strong influence on disease resistance

as exemplified by the two inbred lines 63 and I2 that differ in MD resistance, but are

homozygous for the same B haplotype. Analysis of F2 crosses between these two lines

revealed the presence of 14 chromosomal regions that were associated with MD

resistance (Vallejo et al; 1998; Yonash et al, 1999). Taking into account that the genetic

effects were measured against the relatively narrow genetic background determined by

these two inbred lines, it is likely that that the number of genes that affect MD resistance

in non-inbred strains is quite large. More recently, the strategy for gene mapping has

shifted towards the more pragmatic association analysis of variations in candidate genes

with traits of interest. Such candidate genes can be chosen on the basis of position,

biological properties, co-selection with disease resistance, and differential expression in

strains with contrasting susceptibility, or association with disease resistance in other

species.

Even when the causative mutations or genes have not been identified, selection for

linked markers may improve resistance. Here we report the outcome of a marker selection

experiment in a commercial line of chickens, following a commercial breeding strategy.

The challenge was carried out in vaccinated chickens to mimic the normal commercial

situation, and MD susceptibility was assessed from mortality, necropsy observations and

the measurement of the viral titers in feather-pulp. The selected markers were located in

the genes encoding growth hormone (GH), growth hormone receptor (GHR) and

chemokine macrophage inflammatory protein-3a (MIP-3a). The GH gene had been

implicated in MD resistance, and immune responsiveness in several laboratories

(Kuhnlein et al, 1997; Linher et al, 2000; Liu et al, 2001). Similar to alleles of the GH

gene, alleles of the GHR gene are co-selected with selection for MD resistance and are

associated with differences in the immune responsiveness (Feng et al, 1998; Kuhnlein et

al, 2003). CCL20 is a chemokine that attracts activated T-cells and B-cells and has been

implicated in the control of Salmonella dissemination in mice (Williams, 2006; Kaiser et

al, 2005; Fahy et al, 2004). A preliminary MD challenge experiment in a closely related

commercial population indicated that the three alleles that were chosen for selection were

associated with a reduced titer of MD virus in the spleen and/or thymus (Masilamani,

2003).

67

Chapter 4

4.3 MATERIALS AND METHODS

4.3.1 Strains of chickens and selection strategy

All strains used were White Leghorn strains of Shaver Poultry Breeding Farms

Ltd. What is referred to as the commercial line is the offspring of three pure lines using

the mating strategy outlined in Figure 4.1. Two grandparent lines were used to produce

the dams which where mated with sires from a third pure line to yield the commercial

dams.

Selection for the desired markers was carried out in two generations on the dam

side (line 2 and 3 and 23) and over one generation in the sire line 1 (Figure 4.1). In the

final cross, 10 sires were crossed with 10 females each to produce the offspring used in

the challenge experiment. In each cross, selection was based on the genotypes of 250

individuals of each parental line. The standard commercial line was used as a control

population. To produce this population, twelve pools of semen of four males were

prepared and 17 females were inseminated with each pool. The offspring of the

commercial line were reared without tracking parentage.

The challenge tests (trials) were conducted in populations hatched three months

apart. In the selected population (S) the same sires and dams were used. Hence the

offspring were full-sibs. In the control population the same females were used in the two

hatches, but the semen pools were prepared from different sires of the same generation as

in the first challenge test. Hence, the offspring were half-sibs.

4.3.2 Marker selection

The restriction fragment polymorphisms (RFLP) alleles selected for were GH

Sacl+ , GHR HindHl+ , and MIP-3a Hin6l- (Kuhnlein et al., 1998; Feng et al., 1998;

Masilamani, 2003). While RFLP measurements were used during selection, the final

progeny was genotyped by primer extension (Genome Quebec, FP-SBE platform,

Molecular Devices, Sunnyvale, CA; or SNP Stream® Genotyping System, Beckman TM

Coulter ). The genotypic frequencies in the selected and control population are shown in

Table 4.1.

68

Chapter 4

4.3.3 Challenge

Chickens were hatched at Shaver Breeding Farms Ltd. (Cambridge, ON),

vaccinated with the herpes virus of turkeys (HVT) using the dose recommended by the

supplier (Merial Canada Inc., Baie d'Urfe, QC), and immediately shipped to the

University of Guelph. At 5 days of age, the chickens were challenged by intraperitoneal

injection with 250 PFU of the MD virus strain RB1B. MDV strain RB1B (passage 9) was

provided by Dr. K.A. Schat (Cornell University, NY, USA) (Schat et al, 1982).

In each of the two trials 100 selected and 100 non-selected female chickens were

randomly assigned to 3 isolation units. The chickens were observed daily and those who

showed signs of disease such as wing droopiness, huddling or limping due to paralysis

were euthanized. The experiments were terminated at 8 weeks post-infection. All

chickens, including those that that died or had to be euthanized during the experiment

were subjected to necropsy.

4.3.4 Apramycin treatment

In the first trial seventeen chickens, nine of the selected and eight of the non-

selected group, died before or within 48 hours of challenge. Necropsy indicated

septicemia and tissue swaps revealed the presence of Escherichia coli. The isolated strain

was sensitive to apramycin and the chickens were subsequently treated with this

antibiotic. After the beginning of the treatment two more chickens, one from each group,

died from septicemia during the first week post infection. In the second trial, similar

symptoms (acytes, airsaculitis) were observed in two chickens from each group that died

between 21 and 25 dpi. The chickens were subsequently also treated with apramycin

although no Escherichia coli infection was observed.

4.3.5 Viral titers in feather tip extracts

Feathers were plucked at weekly intervals post-infection and stored frozen at -

80°C. For DNA extraction, the tips at the bottom of the feather were cut into small pieces

with sterile scissors and placed in 1.5 ml tubes with 400 ul of extraction buffer containing

2% 2-mercaptoethanol, 10 mM Tris-HCL at pH 8.0, 100 mM NaCl, 10 mM EDTA at pH

8.0 and 0.5% SDS. After an incubation for 30 minutes at 50°C, proteinase K (Gibco BRL)

69

Chapter 4

was added to a final concentration of 200 ug/ml and the incubation at 50°C continued

overnight. DNA was extracted with phenol:chloroform:isoamyl alcohol (25:24:1)

(BioShop® Canada Inc.) and precipitated with an equal volume of ice-cold 100% ethanol.

The pellets were rinsed with 500 ul 70% ethanol, air-dried and dissolved in 300 ul of

deionized water. The DNA concentrations were measured using the NanoDrop ND-1000

Spectrophotometer (Nanodrop® Technologies). The DNA concentration was adjusted to

lOOng/ul and the samples were stored at -20 C. Viral titers were determined by

competitive PCR as described previously (Kuhnlein et al., 2006). We adhered to this

method since it was reliable and less costly than the real-time PCR methodologies that

have been developed in the mean time (Baigent et al, 2005).

4.3.6 Statistical analysis

Survivorship plots, hazardplots and associated statistics were generated using the

Kaplan-Meier limit estimate. Viral titers were exponentially distributed and were either

analyzed by non-parametric statistics, or parametrically after log transformation. All

analyses and graphics were carried out using the NCSS 2004 software (Hintze, 2004).

Analysis by GLM using the trial as a random variable indicated that the effect of selection

on parameters of Marek's disease was not significant. However, subsequent analysis

revealed that the progression of the disease differed in the two trials. The trial was

subsequently treated as fixed effect and the analysis focused on a comparison of the

relationship between indicators the four groups defined by selection and trial. There was

no significant effect of housing on the viral titers or the tumor frequency. Sire information

was only available for the selected population. Using the trial as a fixed variable and the

sires nested within the trial indicates again that SI is significantly smaller than S2.

4.4 RESULTS

4.4.1 Efficacy of vaccination

A pretrial was conducted to verify that our viral stock and challenge conditions

were adequate. In particular, since we had chosen the time course of viral proliferation in

feather pulp as an endpoint we wanted to avoid excess mortality. The pretrial also enabled

70

Chapter 4

us to test the protective effect of vaccination on mortality and viral proliferation in feather

pulp.

Forty chickens were divided into four groups, group A (vaccination/challenge),

group B (no vaccination/challenge), group C (vaccination/ no challenge) and group D (no

vaccination/ no challenge). As expected no mortality was observed in groups C and D. In

group B, signs of Marek's disease were observed starting from 25 dpi and at 41 dpi all

chickens had either died or had to be euthanized (Figure 4.2). Necropsy revealed

multifocal tumors in various organs in all 10 chickens. In group A, one chicken died at 2

dpi without displaying any MD lesions. Two additional chickens died in the 5l and 6*

week, respectively. Necropsy revealed multifocal tumors in both of these and in one

additional chicken among the survivors at 56 dpi. Hence, vaccination was very effective

in reducing MD mortality.

Viral titers was measured in extracts from feather tips taken on a weekly basis.

The vaccinated group revealed a bell shaped curve with a peak at 3 weeks post-infection

and a declining phase between week 3 and 4. In the non-vaccinated chickens the high

mortality precluded reliable assessment of the viral titers at 3 and 4 dpi. We therefore

integrated the viral titers over the first three weeks as a measure of early viral

proliferation (Figure 4.3). Vaccination reduced the early viral load by a factor of two. The

viral titer in the vaccinated control group C was 50 times lower than in group B and

presumably represented background amplification of the HVT used for vaccination. No

positive samples were detected in the unvaccinated control group.

4.4.2 Effect of selection and trial on viral titers

The time dependence curve of the viral titers was bell shaped in both trials and

populations, with the maximal titer observed at 21dpi (Figure 4.4). However, the relative

response of the two populations in the two trials was reversed. In trial 1 the selected

population (S) had a consistently lower titer than the control population (U), while in trial

2 the situation was reversed.

For statistical evaluation we summed the viral titer up to 21 dpi, the starting

point of mortality (see below). Similar to the titers at individual time-points, the

distribution of this measure of the viral load was exponentially distributed and could be

71

Chapter 4

normalized by log transformation. Analysis by GLM revealed that the control population

(U) had similar viral loads in both trials, while the viral load in the selected population

(S) differed by a factor of 3.8 between the trials. The same response was observed for the

total viral load of the survivors at five weeks post infection (Figure 4.5; Table 4.2).

4.4.3 Survival analysis

In contrast to the viral titers, the survival curves in the two trials differed for the

control population, but were similar for the selected population. (Figure 4.6). Cumulative

mortalities for population U at the end of the trials were 40% and 22%, respectively,

while the cumulative mortalities for the population S were 22% and 23%, respectively

(chi-sq=9.46, df=3, P=0.023). The death rate curves had distinct shapes that appeared to

be characteristic of the population and were similar in both trials. In the population U the

death rate increased until 30 dpi, was followed by a decrease, and then again an increase

towards the end of the experiment. In contrast, the death rate for the selected population

(S) increased steadily with the time post-infection in both trials.

4.4.4 Necropsy analysis

Necropsy revealed that 82% of the chickens that died or were euthanized had

tumors ranging from the size of pinpoints to several cm in diameter. Among the survivors

the percentage of chickens that had tumor-like lesions ranged from 18% to 31%. The

frequency of chickens with tumor-like lesions did not differ significantly among the two

populations and the two trials (chi-square test). The same was true when discoloration or

enlargement of organs were included in the analysis.

The most frequent lesions (36%) were observed in the spleen, ranging from an

enlargement and discoloration to pinpoint lesions and tumors. The frequency of lesions in

other organs were 28% in the kidney, 26% in the ovary, 25% in the liver, 18% in the

proventriculus, 13% in the muscle and 6.4% in the heart.

Although the frequency of chickens with lesions was similar in the two trials; the

distribution of lesions among organs differed significantly (Table 4.3). Specifically, in

trial 1 there were significantly more lesions found in the muscle, the proventriculus and

the heart. No differences were found when the two populations were compared.

72

Chapter 4

4.4.5 Effect on body weight, spleen weight and bursal weight

Among the survivors, there was a significant effect of the presence of lesions on

the body weight, the bursal weight and the spleen weight. Chickens that displayed lesion

had a reduced body weight and bursal weight. In contrast, the spleen weight was higher in

chickens with lesions, presumably reflecting infiltration of the spleen by transformed cells

or B-cell proliferation (Table 4.4). Analysis of the influence of population and trial on

these three parameters revealed a significant effect of the trial on the bursal weigh

(P<10~6), a marginally significant interactive effect on body weight (P=0.05) and no effect

on the spleen weight.

The three variables are confounded, but a rather specific role has been assigned to

the atrophy of the bursa as an indicator of viral replication. Since the bursa is also

expected to be influenced by the body weight, we reanalyzed the influence of the

population and trial on the bursal weight with the body weight as a covariant. It indicated

significant effects of the trial and the interaction between trial and population (Figure

4.7). A comparison of the mean of the bursal weights and the viral titers in the four

different groups indicates that the two measures are equivalent (Figure 4.8).

4.5 DISCUSSION

Quantitatively, MD resistance may be defined as the probability of a chicken to

survive when exposed to MD virus. It is dependent on environmental and genetic factors

and their interactions. The genetics of survival to MD is therefore expected to be complex

and involve many different genes. In addition, the shape of survival curve (i.e. the hazard

rate) is difficult to assess and relate to genetic and environmental variations. It is therefore

customary to analyze other manifestation of MD, such as survival to a fixed amount of

time, the frequency of MD lesions, viral proliferation, or changes in the body weight or

organs. Viral proliferation in the epithelium of feather-tips where infectious virus is

formed is arguably the most important. Lack of formation of infectious virus would entail

that an infected chicken is unable to transmit the virus, such curtailing the spread of the

virus within a flock.

73

Chapter 4

Of these parameters, survival, MD lesions and viral proliferation were correlated,

with the highest correlation being observed between the survival and the presence of MD

(lesions data not shown). It reflects that all chickens that died before the end of the

experiment had MD lesions, suggesting that MD lesions were the ultimate cause of death.

Whether some of the surviving chickens that had MD lesions would have recovered from

MD is unknown.

The chickens in this challenge experiment were still in the growing phase. Hence

analysis of the relationship between MD and body weight, spleen weight and bursa

weight required age matched chickens. Since body weight was only measured at the end

of the experiment, the analysis was restricted to the survivors. Among these, chickens

with MD lesions had a higher viral load, a higher spleen weight and a reduced body

weight and bursal weight. It indicated that weight measurement were also indicators of

MD. In the case of body weight, it would have been worthwhile to extend the

measurement of body weight gain over the entire period of the experiment.

The distribution of lesions in the two trials differed indicating a different course of

the disease. Differences were also evident from measurements of most other parameters

of MD. In particular, there were significant differences in the viral titer in feather tips, the

survival, and the body and organ weights. The reason for the lack of reproducibility is

unknown. The observation of inflammatory lesions early in trial 1 and at about 3 weeks

post infection in trial 2 and subsequent treatment with apramycin is a possible factor.

Other possibilities are differences in the exposure to stress during shipment of the

chickens prior to exposure or parental age differences. No untoward symptoms were

observed in the parents or in littermates that were maintained at the breeding facility.

It is disquieting that in the two trials the relative order of incidence of MD in the

two populations was reversed. Regardless of the MD associated parameter that was

measured, the selected population S was more resistant than population U, while in trial 2

the control population U was more resistant. Markers that had been identified in a single

challenge test as being associated with disease resistant may under certain environmental

situations confer susceptibility.

In its early stage of infection, MDV replicates cytolytically in B and T-cells, cells

that also determine the efficacy of HVT vaccination. Some genes may affect both, the

74

Chapter 4

immune response and viral replication and lead to an unstable response to MDV

infection.

75

Chapter 4

Table 4.1 Influence of selection on the genotype distribution

Marker

MIP-3a Hindi

GH

Sacl

GHR Hindm b

Selected

Control

Selected

Control

Selection

Control

Frequency of genotypes +/+ 0

0.319

1

0.658

0.822

0.582

+/-0.368

0.529

0

0.312

—

-

- / -

0.6323

0.152

0

0

0.178

0.418

3 Genotypes selected for are indicated in bold

The GHR gene in female chickens is haploid.

Table 4.2 Comparison of the cumulative viral titer at 3 and 5 weeks post-infectiona.

Cumulative

viral load

toa

3 weeks

5 weeks

Trial 1

Selected

(SI)

3.43b

5.13

Control

(Ul)

6.97

9.63

Trial 2

Selected

(S2)

13.24

18.33

Control

(U2)

7.98

9.38

Significant contrasts0

SK(U1,U2)<S2

SK(U1,U2)<S2

a For analysis the weekly viral titers (virus/cell) were summed to 3 and 5 weeks,

respectively. The data was normalized by log transformation values and analyzed by

GLM using population (P) and trial (T) as independent variables. The variables T and the

interaction PxT were significant (P<10~5). b Back-transformed means of the log-transformed values.

°Contrast were analyzed for significance using the Tukey-Kramer multiple comparison

test.

76

Chapter 4

Table 4.3 Frequency of lesions among chickens for various tissues in trial 1 and 2a.

Tissue

Spleen

Liver

Muscle

Proventriculus

Ovary

Kidney

Heart

Other

All tissues

Lesion Frequencyb

Trial 1

27%

27%

19%

26%

29%

30%

12%

9%

45%

Trial 2

33%

23%

9%

12%

24%

27%

2%

13%

46%

Chi-

Square

1.89

0.69

8.33

13.55

1.29

0.44

16.17

0.89

0.01

P-valuec

n.s.

n.s.

4x10"3

2x10"4

n.s.

n.s.

6x10"5

n.s.

n.s.

a The selected and non-selected population did not differ.

Percentage of chickens with one or more lesions. c The cut-off of significance after Bonferroni adjustment is 6x10

Table 4.4 Effect of lesions on viral load, body, spleen and bursa weight in surviving

chickens

Viral load to 5 weeks

Body weight (g)

Bursal weight (g)

Spleen weight (g)

Lesions

Present (N=63)

17.0±1.1

619 ±7

2.02 ±0.14

3.48 ±0.12

Absent (N=226)

6.1 ±1.2

709 ± 14

2.99 ± 0.07

2.06 + 0.06

P-value

<10"6

<10"6

<10"6

<10"6

Chapter 4

Sire line

1

Dam line

2

A S \

123

/

3

A 23

?

Figure 4.1 Mating strategy to produce the commercial strain 123. Selection for desired

genotypes was conducted in strains 1, 2, 3 and 23.

78

Chapter 4

1.000-j

0.800-

0.600-

0.400-

0.200-

O.OOOH , r~, r—i r—r—r—T—! 1 i r—, 1 r—i ! 1 1

0.0 15.0 30.0 45.0 60.0 Days Post-Infection

Figure 4.2 Protective effect of vaccination on MD mortality. The survival curves of group

A (vaccinated/challenged) and group B (not vaccinated/challenged) differed significantly

(log-rank test, P=10"4).

A B

Chapter 4

CD O

Q . TJ

CM

O

T 3 CO O

CO

>

2.5n

1.6-

0.8-

-0.1-

* •

A B C

-1.0-0.0

~l I I I I I I I I I I i I I I [ I

0.3 0.5 Percentile

0.8 1.0

Figure 4.3 Effect of vaccination on the viral load integrated over the first 3 weeks post

infection. A percentile plot of the log-transformed sum of viral titers over the first three

weeks is shown. The groups are A: challenged/no vaccination; B, challenge/ vaccination

and C: no challenge/vaccination. The means were mutually different from each other

(Bonferroni multiple comparison test).

80

Chapter 4

£ iZ

Med

ian

Vir

al"

5

4.5 -

4 -

3.5

3 -

2.5

2 -

1.5 -

1 -

0.5 -

0 -

•Trial 1

2 3 4 5

Week Post-Infection

Tite

r V

iral

r

Med

ia

9

8

7

6

5

4

3

2-

0

• — • •

• ~ • " — -

zi '.'

1

__

fr

•:.

%.

f— • -

:

1 I 1

'•>••;:

' '''•'.;

'-•rk

>-:':

,*;.'•••! > ^

V Ve

— " "Trial 2 ~

- - • • • - • ~ —

- —

| " " ~ " ~ " "

I .. _ ~ ~ ~ .. ™ _ , .

1 I I <

''*.;?

u '/

i 4 5 6

ek Pos t-l ifection

Figure 4.4 Time course of viral proliferation. The median viral titer, expressed as viral

genome equivalent, is plotted in dependence of time post-infection.

81

Chapter 4

CD

o

CM

o

-I—»

CO

o CD

>

0.5

Percentile

Figure 4.5 Percentile distribution of the viral load to 3 weeks in dependence of trial and

population.

82

Chapter 4

v>

Sur

vivo

i

1 .UUU'

0.900-

0.800-

0.700

0.6001

0.500-

"•\_. h *- - n-

>^7~L,

-

0.025!

S1 -— S2

0.0 15.0 30.0 45.0 Days Post-Infection

60.0

15.0 30.0 45.0 Days Post-Infection

1.000

0.900

£ 0.800

GO 0.700

0.600

0.500

0.020H

U2 U1

Rat

e ar

d

to

0.016

0.012

0.008

0.0 15.0 30.0 45.0

Days Post-Infection 60.0

0.004

0.000 15.0 30.0 45.0

Days Post-Infection

Figure 4.6 Survival curve of the two populations S and U in trial 1 and 2. The four

survival curves differ significantly at P=0.018 (LOGRANK test). Pair-wise comparisons

indicate that survival curve for Ul (population U in trial 1) differed significantly from the

other three survival curves, while the differences between U2, SI and S2 were not

significant. The hazard rate functions appear to have shapes characteristic of the two

populations 1 and 2.

83

Chapter 4

X3 O

CO

CD <n

O

IS a:

0.0 0.5 Percentile

Figure 4.7 Percentile distribution of the bursa-body weight ratio among the survivors of

the challenge experiment. SI, and S2 designates the population S in trail 1 and trial 2; Ul

and U2 designate the population U in trial 1 and 2. GLM analysis of the bursa weight

with population (P) and trial (T) as independent variables and the body weight as a co-

variant indicated significant effects for T (P=10~6), the interaction PxT (P=0.0003) and the

covariant(P=l(T6).

84

Chapter 4

0.5 0.7 0.9

Viral Load (log)

1.1 1.3

Figure 4.8 Relationship between the mean bursa weight and mean of the log transformed

viral load to 35 dpi among the survivors of the challenge experiment. Linear regression of

the mean bursa weight on the mean viral titer was significant at P=0.011. The Spearman

rank-correlation between individual measurements was -0.349 (N=243,P<10"6).

85

4.6 REFERENCES

Chapter 4

Asmundson, V.S., and Biely, J. (1932) Inheritance of resistance to paralysis (neuro-

lymphomatosis gallinarum) I. Differences in susceptibility. Can. J. Res., 6:171-

176

Bacon L.D., Hunt, H.D. and Cheng, H.H. (2001) Genetic resistance to Marek's disease

Curr. Top. Microbiol. Immun., 255: 121-141

Baigent, S.J., Petherbridge. L.J., Howes, K., Smith, L.P., Currie, R.J.W. and Nair, V.K.

(2005) Absolute quantification of Marek's disease virus genome copy number in

chicken feather and lymphocyte samples using real-time PCR. J. Virol. Meth.,

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Briles ,W.E., Briles, R.W., Tafs, R.E. and Stone, H.A. (1983) Resistance to a malignant

lymphoma in chickens is mapped to subregion of major histocompatibility (B)

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Calnek, B.W. (1985) Genetic resistance. In: Marek's disease—scientific basis and

methods of control. L.N.Payne ed. Martinus Nijhoff, Boston, pp. 293-329

Calnek, B.W. and Witter, R.L. (1997) Marek's disease. In: Diseases of Poultry, 10th ed.

B.W. Calnek and R.L. Witter eds. Iowa State University Press, Ames, pp. 369-413

Cole, R.K. (1968) Studies on genetic resistance to Marek's disease. Avian Dis., 12: 9-28

Drof, M.E. (1981) The role of the major histocompactibility complex in immunology.

Garland Press, New York, NY

Fahy, O. L., Townley, A.L., Coates, N.J., Ckark-Lewis, I. and McColl, S.R. (2004)

Control of Salmonella dissemination in vivo by macrophage inflammatory protein

(MIP)-3a/CCL20. Lab. Invest., 84: 1501-1511

Feng, X.P., Kuhnlein, U., Fairfull, R.W., Aggrey, S.E., Yao, Y. and Zadworny, D.

(1998) A genetic marker in the growth hormone receptor gene associated with

body weight in chickens. J. Hered., 89: 355-359

Fredericksen, T.N., Longenecker, B.M., Pazderka, F., Gilmour, D.G. and Ruth, R.F.

(1977) A T-cell antigen system of chickens: Ly-4 and Marek's disease.


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Gimeno, I. M. (2004) Future strategies for controlling Marek's disease. Marek's disease:

In: Marek's disease: an evolving problem. F. Davison and V. Nair, eds. Elsevier,

London, UK, pp. 186-199

Hansen, M.P., van Zandt, J.N., and Law, G.R.J. (1967) Differences in susceptibility to

Marek's disease in chickens carrying two different B locus blood group alleles.

Poultry Sci., 46: 1268


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Kaiser, P., Poh, T.Y , Rothwell, L.S., and Avery, A. (2005) A genomic analysis of

chicken cytokines and chemokines. J. Interferon & Cytikine Res., 25: 467-484

Kaufman, J., and Salomonsen, J. (1997) The "minimal essential MHC" revisited: both

peptide-binding and cell surface expression level of MHC Molecules are

polymorphisms selected by pathogens in chickens. Hereditas, 127: 67-73

Kaufman, J.F. and Venugopal, K. (1998) The importance of MHC for Rous sarcoma

virus and Marek's disease virus - some Payne - full considerations. Avian Pathol.,

27: S 82-87

Keller L.H. and Sevoian M. (1983) Studies of histocompactibility and immune response

of chickens selected for resistance and susceptibility to Marek's disease. Avian

Dis., 27: 7-20

Kuhnlein, U., Spencer, J.L., Chan, M., Praslickova, D., Linher, K., Kulenkamp, A. and


viral genome proliferation in feather Tips. Avian Dis., 50: 173-178

Kuhnlein, U., Ni L., Weigend, N.L.S., Gavora J.S., Fairfull W. and Zadworny D (1997)

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Kuhnlein, U., Aggrey, S.E. and Zadworny,D. (2003) Progress and prospects in resistance

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aggrey, eds. CABI Publishing, Wallingford, UK, pp. 283-292

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Li, S., Zadworay, D., Aggrey, S.E. and Kiihnlein ,U. (1998) Mitochondrial PEPCK: A

highly polymorphic gene with alleles co-selected with Marek's disease resistance

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Linher, K., Aggrey, S.E., Spencer, J.L., Zadworny, D. and Kiihnlein, U. (2000) Effect of

selection for markers in the growth hormone and growth hormone on early

viremia in chickens infected with Marek's disease virus. In: Proceedings of the

v6th international symposium on Marek's disease. Montreal, pp. 80-85

Liu, H.C., Kung, H.J., Fulton, J.E., Morgan, R.W. and Cheng, H.H. (2001) Growth

hormone interacts with the Marek's disease virus SORF2 protein and is associated

with disease resistance in chicken. Proc. Natl. Acad. Sci. USA, 98: 9203-9208

Masilamani T.J. (2003) Identification of genetic markers associated with Marek's disease

in chickens. M.Sc. Thesis. McGill University

Morrow, C. and Fehler, F. (2004) Marek's disease: a worldwide problem. In: Marek's

disease: an evolving problem. F. Davison and V. Nair, eds. Elsevier, London, UK,

pp. 49-61

Stone, H.A. (1975) Use of Highly inbred chickens in research. U. S. Department of

Agriculture, ARS, Washington DC, Technician Bulletin, 1514: 1-22

Thoday, J.M. (1961) Location of polygenes. Nature, 191: 363-370

Vallejo, R. L., Bacon, L.D., Liu, H.C., Witter, R.L., Groenen, M.A., Hillel, J. and

Cheng, H.H. (1998) Genetic mapping of quantitative trait loci affecting

susceptibility to Marek's disease virus induced tumors in F2 intercross chickens.

Genetics, 148: 349-360

Williams, I. R. (2006) CCR6 and CCL20: partners in intestinal immunity and

lymphorganogenesis. Ann. N.Y. Acad. Sci., 1072: 52-61

Yonash, N., Bacon, L.D., Witter, R.L. and Cheng, H.H. (1999) Higher resolution

mapping and identification of quantitative trait loci (QTL) affecting susceptibility

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88

Chapter 5

CONNECTIVE STATEMENT III

In the first manuscript of this thesis we found that one of three markers in the vitamin D

receptor gene was associated with the expression of the MHC class II antigen on

peripheral blood leukocytes. In this manuscript we used the database for MD

susceptibility that we had described in the previous chapter to analyze whether the three

markers in the VDR gene were associated with MD resistance. Only the marker that had

been found to be associated with MHC class II expression levels was associated with

resistance. It indicates that MHC class II expression may be the link between vitamin D

metabolism and MD resistance.

89

Chapter 5

CHAPTER 5

Association of a marker in the Vitamin D receptor gene with Marek's

disease resistance in poultry

Dana Praslickova1, Shayan Sharif2, Aimie J. Sarson2, Mohamed Faizal Abdul-Careem2,

David Zadworny , Al Kulenkamp , George Ansah and Urs Kuhnlein

!Dept. of Animal Science, McGill University, 21111 Lakeshore Rd., Ste. Anne de

Bellevue, Qc, Canada, H9X3V9 2 Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph,

On, Canada, NIG 2W1 3Shaver Poultry Breeding Farms Ltd., 500 Franklin Boulevard, Cambridge, On, Canada,

N1R 8G6


Urs Kuhnlein

Tel: (514) 398 7799

Fax:(514)398 7964


90


Chapter 5

5.1 ABSTRACT

A genetic marker in the vitamin D receptor gene that had previously been

associated with changes in the proportion of MHC class II antigen positive peripheral

blood cells (PBC) was analyzed for association with Marek's disease resistance. The

database consisted of 400 commercial White Leghorn chickens that were vaccinated with

herpes turkey virus and challenged by intraperitoneal injection of the virulent Marek's

disease virus (MD) RB1B. Viral proliferation in feather tips was determined at weekly

intervals for eight weeks, mortality was recorded and necropsy analyses preformed in all

chickens. The marker had an additive effect on viral load (integration of the viral titer

over time) (P=3xl0~3) with the two homozygotes differing by a factor of 2. Consistent

with resistance to MD, the genotype with the lowest viral load also had the lowest score

for MD lesions and the lowest mortality. There was no effect on the tissue distribution of

MD lesions. The genotype associated with the highest proportion of MHC class II

positive PBCs was associated with the highest degree of resistance.

Keywords: Vitamin D, vitamin D receptor, viral proliferation, Marek's disease

resistance, MHC class II expression

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Chapter 5

5.2 INTRODUCTION

Vitamin D is an essential nutrient that in addition to its classical role in calcium

and phosphate metabolism affects the proliferation, differentiation and function of many

different cell types, including the cells of the immune system. Epidemiological studies

and studies in mice support the importance of Vitamin D in the susceptibility to colorectal

cancer (Lamprecht and Lipkin, 2003), autoimmune diseases such as insulin dependent

diabetes mellitus (Zella and DeLuca, 2003), multiple sclerosis (Ascherio and Munger,

2007), rheumatoid arthritis (RA) (Cantorna, 2000), and Crohn's disease (Simmons et al,

2000). In addition, the vitamin D status is thought to modulate the susceptibility to

infectious diseases such as pulmonary tuberculosis (Selvaraj et al, 2003; Wilbur et al,

2006; Liu et al, 2006), influenza (Cannel et al, 2006), hepatitis B virus (Suneetha et al,

2006) and leprosy (Roy et ah, 1999). The association of the vitamin D status with the

incidence of autoimmune and infectious diseases in man prompted us to search for

variants in genes of the vitamin D metabolism that affect the immune response and

disease resistance in chickens.

In a previous study (see manuscript #1) we studied genetic variation in three genes

of vitamin D metabolism in White Leghorn strain. The three genes analyzed were the

vitamin D binding protein (DBP), the vitamin D receptor (VDR) and 25-hydroxyvitamin

D-24-hydroxylase (Cyp24). DBP is the main transporter of vitamin D to target cells,

VDR is the receptor that mediates the effect of vitamin D on gene transcription and

Cyp24 a major regulatory enzyme that inactivates vitamin D by hydroxylation (Dusso et

al, 2005; Omdahl et al, 2002).

Non-redundant markers in these genes were identified in a non-inbred strain and

tested for association with the proportion of peripheral leukocytes classified on the basis

of the cell surface markers CD3, CD4, CD8, MHC class II and lyB (manuscript #1). The

most significant effect was found for a marker in the VDR that affected the proportion of

MHC class II positive cells. MHCII mediates the display of antigens on antigen

presenting cells, thus stimulating effector cells of the immune system. MHC class II

expression may be an important modulator of MD, since it is up-regulated in various cell

types in response to viral infection (Niikura et al, 2007). It prompted us to analyze

whether markers of the vitamin D receptor affect MDV proliferation. The database used

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Chapter 5

to test for such an association was a commercial White Leghorn strain that had been

intraperitoneally infected with MDV and has been described previously (manuscript #2).

5.3 MATERIALS AND MEHTODS

5.3.1 Strains of chickens and challenge test

We used a database of 400 female commercial White Leghorn chickens from two

different populations that had been intraperitoneally infected with MDV. The origin of

these two populations and the challenge protocol had been described previously

(manuscript #2). Briefly, the first population (S) was generated by mating two lines of

chickens that had been selected for markers in the GHR , the GH and the chemokine

MIP-3a. Ten sires were mated to ten females each. The second population (U) was the

standard commercial cross of the non-selected parental strains used to generate

population S. It was generated by pooling semen from 4 males per pool and inseminating

17 females with each semen pool.

Two challenge tests were conducted in two hatches spaced three month apart. For

the population S the same parents in both hatches. For the generation U different pools of

semen were used, but the inseminated females were the same. For the challenge, 100

female chickens of each strain were hatched, vaccinated with HTV, banded, intermingled

and transported from the hatchery to the University of Guelph. They were housed

intermingled and challenged at 5 days of age.

5.3.2 DNA extraction and viral titration

Feather samples were collected from the wings of the chickens on 7, 14, 21, 28,

35, 42, 49, 56 dpi and shipped from the University of Guelph to our laboratory for

analysis. Extraction of the DNA from feather tips was carried out using a protocol

adapted from Kuhnlein et al. (2006). Feather tips were cut into small pieces with sterile

scissors and placed into 1.5 ml tubes containing 400 ul of extraction buffer (2% 2-

mercaptoethanol, 10 mM Tris HCL at pH 8.0, lOOmM NaCl, 10 mM EDTA at pH 8.0

and 0.5% SDS). After an incubation period of 30 minutes proteinase K (Gibco BRL) was

added to a final concentration of 200 (J-g/ml, and the incubation was continued at 50°C for

16 hours. DNA was extracted with phenol:chloroform:isoamyl alcohol (25:24:1)

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Chapter 5

(BioShop), precipitated with an equal volume of ice-cold 100% ethanol and rinsed with

500 ul 70% ethanol. The samples were air-dried and the DNA dissolved in 300 \xl of

deionized water. The DNA concentration was measured by spectrophotometry

(NanoDrop® ND-1000 Spectrophotometer). The samples were diluted to 100 ng/ul and

2ul per each reaction were used in the competitive quantitative polymerase chain reaction

(PCR). The competitive PCR to quantitate viral DNA has been described (Kuhnlein et ah,

2006).

5.3.3 Genetic analysis of the VDR gene

The VDR gene had been analyzed in strain 7, a non-inbred White Leghorn strain

that had been generated by mating 4 North American commercial strains in 1955 and was

propagated by pedigreed random mating without selection using 100 sires mated to 2

dams each. Genetic variations were assessed by sequencing two segments of 20 offspring

from different sire families. The locations of the two segments and the polymorphisms

within the VDR gene are shown in Figure 5.1. The 14 SNP in these two segments

required at least 10 taggers for complete characterization of the haplotypes. For budgetary

reasons we restricted our analysis to the three taggers VDR S1P4, VDR SI PI 2 and VDR

S2P2. The marker VDR S1P4 was associated with variations in the number of peripheral

blood cells expressing the MHC class II antigen.

These three markers also segregated in the commercial population that was

subjected to the challenge test. They were genotyped by the McGill University and

Quebec Genome Innovation Center by fluorescence polarization detection of single base

extension (FP-SBE) using a Analyst HT reader (Molecular Devices, Sunnyvale, CA)

and/or by the GenomeLab™ SNP Stream® Genotyping System (Beckman Coulter ).

5.3.4 Statistical analysis

The statistical evaluation and graphical illustration were conducted with the NCSS

software (Hintze, 2004). Association analyses were conducted by using general linear

model procedures (GLM). Survival and hazard rates were analyzed using the Kaplan-

Meier procedure.

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Chapter 5

5.4 RESULTS

5.4.1 Association with viral proliferation

The time course of viral proliferation in feathertips was bell-shaped with a peak

observed at 21 dpi with the on-set of mortality (Kuhnlein et al, 2006). The profiles for

the genotypes of the marker VDR S1P4 are shown in Figure 5.2. At each time point the

lowest median viral titer was observed for the genotype AA, followed by AG and GG.

The profiles for the markers VDR SIP 12 and VDR S2P2 were similar for all genotypes

(data not shown).

For statistical analysis we summed the weekly viral load prior to the onset of

mortality (the viral load to 21 dpi) and to 35 dpi (viral load to 35 dpi). The latter measure

includes only the chickens that were still alive at 35 dpi. Significant single variable

effects were observed for the VDR S1P4 genotype and the trial (Table 5.1). The

interaction term between the population and the trial was also significant as reported

previously (manuscript #2). It reflects that the two populations were differentially

affected in the two trials. However, the interactions of the VDR genotypes with both trial

and population were not significant, indicating that the effects of the VDR genotype were

independent of the population and trial. The mean viral loads for the different VDR S1P4

genotypes are shown in Table 5.2. A comparison of the means indicates additivity for the

VDR locus with the viral load differing between the homozygotes by a factor of 2.

The two other markers in the VDR gene, VDR SIP 12 and VDR S2P2 had no

effect on the proportion of leukocyte subtypes, and in the previous analysis they also had

no significant effect on the viral titer in feather tips (data not shown).

5.4.2 Association with MD lesions, mortality and weight of the bursa

Other indicators of MD are the frequency of chickens with MD lesions, the

cumulative mortality and the bursa weight. These parameters were not significantly

dependent on the VDR S1P4 genotype (Table 5.2). However, the ranking by the three

genotypes was consistent with the ranking of the viral titers. The magnitude of the means

again indicated an additive effect of the VDR polymorphism.

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Chapter 5

The frequency of chickens with lesions categorized by tissue is shown in Figure

5.3. For each tissue the frequency of chickens that had lesions was lowest for the

genotype AA, followed by genotype AG and GG. Hence, the ranking of the genotypic

classes on the basis of the presence of one or more lesions is concordant. The ranking of

the tissues by the genotypic classes is also concordant, indicating that the tissue

distribution of lesions is not significantly influenced by the VDR receptor genotype.

An analysis of the time course of mortality suggests that the VDR S1P4 genotype

may affect the time dependence of the rate of mortality (Figure 5.4). In genotype AA

chickens the mortality rate peaked at 28 dpi. This peak was shifted by one week in

genotype GG chickens. The mortality rate curve in genotype AG chickens was between

the curves for the two homozygotes.

5.5 DISCUSSION

In the present study we screened a population of 400 White Leghorns chickens for

the association of Marek's disease resistance with three polymorphisms in the VDR gene.

Only one of the markers, VDR S1P4 that had previously been found to be associated with

the frequency of MHC class II positively expressing leukocytes in peripheral blood

leukocytes had a significant effect. The effect of this marker on MD resistance was

additive, similar to the effect on MHC class II expression that had been measured in a

different strain of White Leghorn chickens.

MD resistance was assessed using three different indicators, viral titers in feather

pulp, survival to 56 dpi, the frequency of MD lesions and the weight of the bursa. A

significant effect of the VDR marker VDR S1P4 on MD resistance was only observed for

the viral titer in feather tips. However, the other indicators behaved concordantly as

expected from the relationship of mortality, frequency of lesions and viral titer in feather

tips (manuscript #2).

The ranking of the number of lesions on the basis of the three genotypes was the

same in each of 8 groups of tissues and was concordant with the effect of VDR S1P4

genotype on the viral titer (AA<AG<GG). Similarly, the ranking of lesions was

consistent for each genotype, indicating that there were no tissue specific effects of the

VDR genotype on the distribution of lesions.

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Chapter 5

The VDR may affect the level of cell transformation and of viral proliferation

independently. In humans mutations in the VDR gene leading to the hereditary vitamin D

resistant rickets (HVDRR), have been shown to be associated with hair-loss (alopecia)

(Malloy et al, 1999). Similarly, VDR-null mice display alopecia, presumably due to a

defect in keratinocyte stem cell function that is essential for hair follicle homeostasis

leading to the absence of the initiation of new hair growth cycles (Clanferroti et al, 2007;

Demay et al, 2007). Viral proliferation of MDV in the epithelial cells of feather tips may

be dependant on a normal progression of cell differentiation in feather follicles. In

particular, the transient viral proliferation we observed in our challenge tests may not

reflect the course of the disease, but may be related to the age dependent development of

feather follicles.

Vitamin D has also antiproliferative actions (Bouillon et al, 2006). VDR-KO

mice are more prone to develop tumor when exposed to oncogenes or carcinogens and

epidemiological studies indicate that there is an inverse relationship between UV-B

exposure and the incidence of colorerectal, breast and prostate cancer. Hence, VDR may

affect tumor formation and survival independently of its effect on viral proliferation.

Alternatively to a pleiotropic action on several manifestations of MD, l,25(OH)2D

may exert its effect on MD via its impact on the immune system. Virus proliferation in

feather tips is thought to be mediated by sustained contact of epithelial cells with infected

B and T lymphocytes that infiltrate the feather follicles. The infiltration rate and hence the

number of infected lymphocytes may therefore be rate limiting for viral titers in feather

tips. Cytolytically infected lymphocytes are also the precursors of latently infected B and

T cells that may infiltrate different tissues and give rise to the proliferative lesions

characteristic of MD.

Vitamin D affects many cells of the cognate and innate immune system (Griffin et

al, 2003). In general it exerts an inhibitory effect on the immune system by attenuating

the differentiation and proliferation of cells of the immune system. Specifically, it has

been reported to reduce the surface expression of MHC class II and other co-stimulatory

ligands on dendritic cells, and to induce a shift of the immune response of Thl profile to

the Th2 profile of the immune system (Chen et al, 2007; Overbergh et al, 2000). The

association of autoimmune diseases with vitamin D deficiency is thought to be due to a

97

Chapter 5

relatively high Thl response that leads to the activation of CTLs and subsequent tissue

damage. However, an inhibitory effect of l,25(OH)2D on the B-cell maturation,

proliferation and IgE production has also been reported (Chen et al, 2007). An example

of the effect of vitamin D on the innate immune system is the upregulation of the VDR

and vitamin D-l hydroxylase in response to activation of Toll-like receptors, leading to

the induction of the antimicrobial peptide cathelicidin (Liu et al, 2006).

MD resistance is affected by the innate immune system as well as the Thl and

Th2 branches of the cognate immune system (Davison and Kaiser, 2004). A general

attenuation of the response of the immune system by vitamin D would be expected to

increase the susceptibility to MD. Indeed, most viruses down regulate the immune system

as part of their strategy to escape immune surveillance. The most important pathways are

the inhibition of the expression of MHC class II genes by blocking the pathway that

induces the expression of the MHC class II transactivator and the inhibition of the MHC

class II antigen presenting pathway (Hegde et al, 2003). Surprisingly, Marek's disease

virus does not subscribe to this strategy. To the contrary, it up-regulates the MHC class II

cell surface expression in response to MDV infection, apparently by an INF-y mediated

pathway (Niikura et al, 2007; Gimeno et al, 2001).

The VDR S1P4 marker that we have found to be associated with MD resistance

has previously found to be associated with the proportion of MHC class II expressing

cells in peripheral blood leukocyte. Paradoxically, the chickens of the genotype

associated with the highest proportion of MHC class II expressing lymphocytes were the

most resistant to MD (Figure 5.5). Since the association of the VDR marker with the

MHC class II positive peripheral leukocytes, it is expected to reflect the proportion of

leukocytes that constitutively express MHC class II antigen. Whether the VDR

polymorphism has an effect on the induction of MHC class II antigen expression by viral

infection remains to be determined. Further, it has to be considered that our challenge test

was conducted in chickens vaccinated with HVT, an attenuated virus of the Mardi virus

family. The association of the VDR polymorphism with susceptibility to MD may

therefore reflect an effect of VDR on the response to vaccination (Ivanov et al, 2006).

98

Chapter 5

Table 5.1 GLM analysis of the dependence of the integrated viral titers on trial,

population and VDR S1P4 genotype.

Source

VDR

Population (P)

Trial (T)

VDRxP

VDRxT

PxT

Residual

Viral load to 21 dpi (log)

Mean

square3

1.64

0.02

8.80

0.33

0.32

5.49

0.20

P-value

3 -10"4

0.73

<10'6

0.19

0.20

<10'6

Viral load to 35 dpi (log)

Mean

square

2.71

0.18

4.01

0.68

0.01

7.03

0.25

P-value

2 -10"5

0.40

7 -10"5

0.06

0.96

<10"6

a The parameters VDR, P and T and their interactions accounted for 22% of total sum of

squares. VDR alone accounted for 3.7% of the total sum of squares.

The parameters VDR, P and T and their interactions accounted for 20% of total sum of

squares. VDR alone accounted for 5.6 % of the total sum of squares

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Chapter 5

Table 5.2 Mean viral load for different VDR S1P4 genotypes

Indicators of MD

Viral load to 21 dpib

Viral load to 35 dpi

Cumulative mortality (%)c

Chickens with proliferative lesions (%)c

Frequency of proliferative lesions/chicken

Bursa weight (g)c

VDR S1P4 genotype3

AA (N=48)

4.69 ±1.18

5.00 ±1.21

19.3

27.3

0.473

2.89 ±0.18

AG(N=175)

6.49 ±1.09

9.62 ±1.10

24.5

38.8

0.776

2.80 ±0.10

GG(N=135)

9.07 ±.1.10

13.20± 1.12

30.6

43.0

0.915

2.70 ±0.12

a The total number of chickens that had been genotyped and assayed is indicated.

The mean and standard error were computed from the log transformed values of the sum

of the viral titers measured at 7, 14 and 21 dpi (viral load to 21 dpi) and day 7, 14, 21, 28

and 35 dpi (viral load to 35 dpi). The values indicated were back-transformed. c Not significant, but concordant with the ranking by viral load.

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Chapter 5

Exon 1 Exon 3 Exon 2 Exon 4

+ 2000bp 0 Segmentl

- H -

P4 P5 P7 PI P2 P3 \ \ ,b /P8P9 P10 Pll P12

J iL 50bp A Exon 2 A 5 4 1

Exon 5 Exon 6 Exon 7

-H Segment 2

sna-E X O n 6 # Exon 7

464

Figure 5.1 Map of the VDR gene. The arrows indicate the position of the markers VDR

S1P4, VDR S1P12 and VDR S2P2 that were analyzed. The association with the viral load

was significant for P4 in segment 1.

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Chapter 5

-AA

-AG

-GG

14 21 28

Days post infection

Figure 5.2 Time course of viral titers in feather tips for different VDR S1P4 genotypes.

The mean viral titers (viral genome equivalent/ cellular genome equivalent) from the

population U in trial 1 are plotted for each week post-infection. The profiles for the

population U in trial 2 and the population S in both trials were similar, with the genotype

AA being associated with the lowest and GG with the highest titers.

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Chapter 5

40 T

£ 30

to

o 'to

20 4

5 w c 0)

J *

o

O

10 4

IS r\f\

• AG

• GG

Spleen Liver Muscle Prov Ovary Kidney Heart Misc

Figure 5.3 Tissue distribution of proliferative and inflammatory lesions. The bars

represent the frequency of chickens that had proliferative lesions in the tissues indicated.

The ranking of the tissues by the genotypes was concordant (Kendall coefficient of

concordance W=l, P=3T0"5). Similarly, the ranking of the three genotypes by the tissues

was significant (W=l, P=0.006).

103

Chapter 5

AA AG GG


60.0


Figure 5.4 Survival and hazard rate for different VDR S1P4 genotypes. The genotypes

AA and GG are associated with the lowest and highest viral loads, respectively.

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Chapter 5

5 6 7

MHC class II titer (%)

• Mortality

A Viral load

O Lesions/chicken

Figure 5.5 Relationship between the proportion of MHC class II positive peripheral

leukocytes and mortality, the viral load to 21 dpi, and the frequency of lesions/chicken.

The values were plotted for each of the genotypes of the three markers in the VDR gene.

105

Chapter 5

5.6 REFERENCES

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Part II: Noninfectious factors. Ann Neurol., 61: 504-13

Association of vitamin D receptor gene variants of BsmI, Apal and Fokl polymorphisms

with susceptibility or resistance to pulmonary tuberculosis. Curr. Science, 12:

1564-1568

Bouillon, R., Eelen, G., Verlinden, L., Mathieu, C, Carmeliet, G., Verstuyf, A. (2006)

Vitamin D and cancer. J. Steroid Biochem. Mol. Biol., 102: 156-62

Cannell, J.J., Vieth, R., Umhau, J.C., Holick, M.F., Grant, W.B., Madronich, S., Garland,

C.F., Giovannucci, E.(2006) Epidemic influenza and vitamin D. Epidemiol Infect.,

134: 1129-40

Cantorna, M.T. (2000) Vitamin D and Autoimmunity: Is Vitamin D Status and

Enviromental Factor Affecting Autoimmune Disease Prevalence? P.S.E.B.M.,

233: 230-233

Cianferotti, L., Cox, M., Skorija, K., Demay, M.B. (2007) Vitamin D receptor is essential

for normal keratinocyte stem cell function. Proc. Natl. Acad. Sci. USA, 104:

9428-33.

Chen, S., Sims, G.P., Chen, X.X., Gu, Y.Y., Chen, S. and Lipsky, P.E. (2007) Modulatory

effects of 1,25-dihydroxyvitamin D3 on human B cell differentiation. J. Immunol.,

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Davison, F. and Kaiser, P. (2004) Immunity to Marek's disease. In: Marek's disease, an

evolving problem. F. Davison F. and V. Nair eds. Elsevier Academic Press.

Amsterdam, pp. 126-141

Demay, M.B., MacDonald, P.N., Skorija, K., Dowd, D.R., Cianferotti, L. and Cox, M.

(2007) Role of the vitamin D receptor in hair follicle biology. J. Steroid Biochem.

Mol. Biol, 103:344-346

Dusso, A.S., Brown, A.J. and Slatopolsky, E. (2005) Vitamin D. Am. J. Physiol. Renal.

Physiol., 289: F8-F28

Gimeno, I.M., Witter, R.L., Hunt, H.D., Lee, L.F., Reddy, S.M. and Neumann, U. (2001)

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and major histocompatibility complex antigen expression. Vet. Pathol., 38: 491-

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Gowe, R.S., Fairfull, R.W., McMillan, I. and Schmidt G.S. (1993) A strategy for

maintaining high fertility and hatchability in a multi-trait egg-stock selection

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Hegde, N.R., Chevalier, M.S. and Johnson, D.C. (2003) Viral inhibition of MHC class II

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Griffin, M.D., Xing, N. and Kumar, R.(2003) Vitamin D and its analogs as regulators of

immune activation and antigen presentation. Annu. Rev .Nutr., 23: 117-45


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Ivanov, A.P., Dragunsky, E.M., Chumakov, K.M . (2006) 1,25-dihydroxyvitamin d3

enhances systemic and mucosal immune responses to inactivated poliovirus

vaccine in mice. J. Infect. Dis., 193: 598-600

Kiihnlein, U., Spencer, J.L., Chan, M., Praslickova, D., Linher, K., Kulenkamp, A. and


viral genome proliferation in feather tips. Avian Dis., 50: 173-178

Lamprecht, S.A. and Lipkin, M.(2003) Chemoprevention of colon cancer by calcium,

vitamin D and folate: Molecular mechanisms. Nature reviews, 3: 601-614

Liu, P.T, Stenger, S., Li, H., Wenzel, L., Tan, B.H., Krutzik, S.R., Ochoa, M.T.,

Schauber, J., Wu, K., Meinken, C, Kamen, D.L., Wagner, M., Bals, R.,

Steinmeyer, A., ZUgel, U., Gallo, R.L., Eisenberg, D., Hewison, M., Hollis,

B.W., Adams, J.S., Bloom, B.R. and Modlin, R.L. (2006) Toll-like receptor

triggering of a vitamin D-mediated human antimicrobial response. Science, 311:

1770-1773

Malloy, P.J., Pike, J.W. and Feldman, D. (1999) The vitamin D receptor and the

syndrome of hereditary 1,25-dihydroxyvitamin D-resistant rickets. Endocr. Rev.,

20: 156-88.

Niikura, M., Kim, T., Hunt, H.D., Burnside, J., Morgan, R.W., Dodgson, J.B. and Cheng,

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Omdahl, J.L., Morris, H.A. and May, B.K. (2002) Hydroxylase enzymes of the vitamin D

pathway: expresion, function, and regulation. Annu. Rew. Nutr. 22: 139-166

Overbergh, L., Decallonne, B., Waer, M., Rutgeerts, O., Valckx, D., Casteels, K.M.,

Laureys, J., Bouillon, R. and Mathieu, C. (2000) lalpha,25-dihydroxyvitamin D3

induces an autoantigen-specific T-helper 1/T-helper 2 immune shift in NOD mice

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Roy, S., Frodsham, A., Saha, B., Hazra, S.K., Mascie-Taylor, C.G. and Hill, A.V. (1999)

Association of vitamin D receptor genotype with leprosy. J. Infect. Dis., 179: 187-

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Selvaraj, P., Chandra, G., Kurian, S.M., Reetha, A.M. and Narayanan, P.R. (2003)

Simmons, J.D., Mullighan, C, Welsh, K.I. and Jewell, D.P. (2000) Vitamin D receptor

gene polymorphism: association with Crohn's disease susceptibility. Gut, 47: 211-

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Suneetha, P.V., Sarin, S.K., Goyal, A., Kumar, G.T., Shukla, D.K. and Hissar, S. (2006)

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CHAPTER 6

GENERAL CONCLUSIONS

The primary interest of the poultry breeding industry is to develop healthy

chickens that can produce eggs and meat economically and in a sustainable fashion.

Among the factors that disrupt sustainable production are viral diseases, including

Marek's disease, which drives researchers to look for ways to enhance existing control.

Viral diseases are not only causing losses due to direct effects, but also because they

weaken the immune system and render chickens susceptible to other diseases.

An adjunct to vaccination and proper management is the selection for genes that

improve the disease resistance. Efforts in the past decade have been focused on

identifying such genes. The general approach was to construct a genetic map of the

chicken genome and to map loci by conducting segregation analyses. Map construction

has culminated in the sequencing of the entire genome. However, identification loci that

affect resistance using segregation analysis have to some extent been disappointing. This

is mainly due to an unforeseen genetic diversity of the chicken genome, which is still

present despite extensive selection since the domestication of the chicken. Our analysis of

the VD receptor is a case in point. A second problem is that disease resistance appears to

be a quantitative trait, determined by many partially redundant pathways. It is therefore

difficult to conduct mapping of loci with sufficient precision to identify genes and to

generalize findings from the analysis of inbred lines or families to the population at large.

To this one has to add the tremendous costs in whole genome scans.

The same is to some extent true in human genetic studies. The paradigm has

therefore shifted to analyzing the association between traits and genetic variations in

candidate genes, i.e. genes that are thought to be involved in a trait on the basis of their

biological properties and gene location (if data is available). It has led to an explosion of

data relating genetic variations with human genetic disorders. With the availability of the

chicken genomic sequence, the same approach can be applied to chickens.

The limited resources available in poultry research require a judiciary approach

when selecting candidate genes. One possibility is to concentrate on genes that had

preciously associated with disease resistance in human and/or mice studies. The argument

109

is that if genetic variations that affect disease resistance have survived human and/or

mouse evolution, they may also have survived evolution of the chicken. We therefore

chose to analyze markers in three genes of the vitamin D metabolism. Vitamin D

metabolism has been shown to be an important modulator of the innate and cognitive

immune system and several of its genes have been shown to be associated with disease

resistance in humans.

In the first manuscript we identified sequence variations in the three genes of the

vitamin D pathway (DBP, VDR, Cyp24), and their association with disease resistance in

vaccinated, Strain 7 of White Leghorn chickens. Two randomly selected sections for

each of the following genes: DBP, VDR and Cyp24 were sequenced to establish genetic

variations and to determine the blocks of the co-segregating SNPs. The analysis

demonstrated the high degree of genetic variability still present in White Leghorn

chickens despite the high degree of selection. Of particular interest is the VDR gene that,

similar to the human gene, shows a low degree of linkage disequilibrium between

markers, indicative of a high degree of historical recombination.

Two to three non redundant markers in each gene were analyzed for associations

with peripheral blood mononuclear cells (PBMC). There was significant association

between a marker in the VDR gene and a marker in the DBP gene and the proportion the

MHC class II and TCR1 positive cells, respectively. None of these markers affected egg

production traits. There was significant interaction between markers, indicating that they

are part of the same pathway.

In the second manuscript we conducted DNA based selection for RFLP markers in

the GHR, GH1, and MIP-3a. These markers were chosen as candidate genes on the basis

of their function and/or co-selection with selection for resistance to MD virus. Marker

assisted selection, apart from selection for MHC class II haplotypes, has not yet been

conducted in poultry. Besides testing the usefulness of a putative resistance marker in

breeding, it provides us with a database for future marker identification.

Two populations were created, a selected population and a non-selected control

population. The challenge was conducted in two hatches, three months apart. The strain to

develop the selected and non-selected population was a commercial White Leghorn strain

and was vaccinated to mimic normal commercial conditions. As an endpoint of resistance

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we measured a series of parameters that included the viral load in feather tips, the

survival, the frequency of MDV induced tumors and the atrophy of the bursa.

The outcome was clear. In the first trial the selected population was more resistant

than the non-selected population, indicating that the marker based selection for resistance

was successful. In the second trial the outcome was reversed with the same clarity. What

went wrong? The chickens were hatched and vaccinated at Shaver Poultry Farms Ltd., a

company that has developed and marketed poultry strains since many years. The chickens

in each of the two trials were intermingled immediately after hatch and the parents

showed no signs of ill health or reduced production.

Necropsy indicated that the course of the disease differed in the two trials.

Specifically, the tissue distribution of the MD lesions in the two trials differed

significantly. Every living organism can develop different manifestations of the disease

with different magnitudes of infection. This depends on the virulence of the virus, the

quality of the vaccine batch, the environment, the genetic background, the parental age,

the body condition and the infection by other pathogen. During trial 1, nine of the S and

eight of the U chickens died before or within 48 hours post infection. The necropsy

analysis indicated septicemia and tissue swabs confirmed the presence of infection with

Escherichia coli sensitive to apramycin. The chickens were therefore immediately treated

with apramycin. In trial 2, ascites and airsaculitis were observed in two chickens from

each group; these chickens died within 2 1 - 2 5 dpi. Again apramycin treatment was

commenced, but Escherichia coli was not detected.

The comparison of the selected and non-selected population in two trials revealed

contradicting results, both at a high level of significance. It may reflect that in one of the

trials the two populations were exposed to different extraneous factors or that the two

populations differed in their response to a common factor, reflecting a destabilizing effect

of selection. Although we cannot distinguish between these two possibilities, our results

indicate that it is important to test the effect of marker assisted selection under a variety of

conditions before applying it at the industrial level.

The challenge experiment provided us with an excellent database consisting of

two populations tested for resistance to MD in two different trials. We used this database

to test whether or not the marker genotypes of the vitamin D receptor gene that we had

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analyzed in the first chapter were associated with MD resistance. Among the three

markers only the one that had been found to affect the proportion of peripheral blood

leukocytes expressing the MHC class II antigen was found to be associated with MD

resistance. The marker associated with the presence of high levels of MHC class II

expressing cells was associated with high resistance to MDV. It consistently reduced the

viral load, mortality, lesion frequency and bursal atrophy independent of population and

trial. Hence, selection for this marker may lead to a reproducible improvement of MD

resistance.

In summary, we showed that genetic variance in the genes used in our study have

an effect on the susceptibility of chickens to MDV. By having built a valuable database of

the DNA from two, vaccinated, and challenged populations, we have created a possibility

not only to study the effect of the genetic markers that were used for the selection, but

also to look for new candidate genes to be used for future selections.

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APPENDIX

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Association of Markers in The

Documents

Transcript of Association of Markers in The