A Genetic Study of MS by Fine Dissection of Rat Chromosome 10

with MOG Induced EAE

MARINA BJÖRK NILSSON

Master’s Degree ProjectStockholm, Sweden 2004

TRITA-NA-E04075

Numerisk analys och datalogi Department of Numerical AnalysisKTH and Computer Science100 44 Stockholm Royal Institute of Technology

SE-100 44 Stockholm, Sweden

MARINA BJÖRK NILSSON

TRITA-NA-E04075

Master’s Thesis in Biomedical Engineering (20 credits)at the School of Computer Science and Engineering,

Royal Institute of Technology year 2004Supervisor at Nada was Erik Fransén

Examiner was Anders Lansner

A Genetic Study of MS by Fine Dissectionof Rat Chromosome 10 with MOG Induced EAE

Abstract Multiple Sclerosis (MS) is a chronic inflammatory disease of the Central Nervous System (CNS) and it is characterized by inflammation and demyelination. The destruction of the myelin sheaths surrounding the nerve cells is causing neurological deficits. Several factors may contribute to the development of MS, such as environmental factors and genetic inheritance. Since it is a complex disease there is no single mode of inheritance causing the development of MS. The genes regulating MS have been very difficult to identify with linkage analysis due to a number of factors, such as genetic heterogeneity, environmental influences and small sample sizes. This is the reason for introducing an animal model to reduce and control certain factors contributing to the disease. The model used is Experimental Autoimmune Encephalomyelitis (EAE) induced in rats with myelin oligodendrocyte glycoprotein (MOG). The MOG-EAE is a chronic relapsing inflammatory disease that mimics the course of MS in humans. The aim of this project has been to fine dissect a region on rat chromosome 10, previously designated Eae18b to the level allowing an identification of single genes. The work presented in this study was performed using an Advanced Intercross Line (AIL) between disease susceptible DA and disease resistant PVG.1AV1 rat strains. The rats were intercrossed for ten generations that leads to an increase in the number of recombinations between the generations and gives an increase in the resolution of the genetic map. Subsequently, MOG-EAE has been induced in 800 F10 animals and disease phenotypes followed for 35 days. Mapping of the genome region regulating the disease was then performed by linkage analysis in which phenotype has been coupled to the genotype. The reduction of the confidence interval of the region of interest is t/2, where t is the number of generations, resulting in a much higher resolution than in previous generations. The confidence interval of Eae18b in seventh generation (F7) was 3Mb and contained approximately 10 confirmed rat genes. More importantly, syntenic loci on human chromosome 17q11 regulate MS suggesting that Eae18b define genes/pathways of importance for the disease in both rats and humans. Theoretically, the F10 AIL should give a 5-fold reduction in the confidence interval, allowing selection of candidate genes.

En genetisk studie av MS genom findissekering av råttans kromosom 10 med MOG-

inducerad EAE

Sammanfattning Multipel Skleros (MS) är en kronisk neurologisk sjukdom i Centrala Nervsystemet (CNS), och karakteriseras av inflammation och nedbrytning av nervceller i CNS. Nedbrytningen av myelinet som omger nervcellerna ger neurologiska skador. Många olika faktorer bidrar till utvecklingen av MS, som exempelvis miljö och även genetiskt nedärvbara faktorer. Eftersom det är en komplex sjukdom är det inte endast en ärftlig faktor som bidrar till utvecklingen av sjukdomen. Generna som reglerar MS har hittills varit svåra att identifiera med kopplingsanalys, linkage analysis, på grund av ett antal faktorer som genetisk heterogenecitet, miljöfaktorers inverkan och den otillräckliga storleken på proverna. Detta är orsaken till att en djurmodell är implementerad, för att kunna reducera och kontrollera speciella faktorer som bidrar till utvecklandet av MS. Modellen som används i denna studie är Experimental Autoimmune Encephalomyelitis (EAE) bestående av råttor som immuniserats med myelin oligodendrocyte protein (MOG). MOG - EAE är en kroniskt inflammatorisk sjukdom med ett skovvis förlopp som efterliknar MS hos människor på ett mycket bra sätt. Målet med denna rapport har varit findissekering av råttans kromosom 10, mer specifikt en region Eae18b, definierad i tidigare studier och om möjligt identifikationen av en enda sjukdomsreglerande gen. Arbetet här är utfört i en Advanced Intercross Line (AIL) mellan mottagliga DA och resistenta PVG.1AV1 råttstammar. Råttorna är korsade i 10 generationer, vilket ger en ökning i antalet rekombinationer mellan generationerna och också en ökning i upplösningen av den genetiska kartan. Reduceringen av regionen av intresse är t/2 för varje generation, vilket ger en mycket högre upplösning än tidigare generationer. Teoretiskt ska F10 AIL resultera i en femfaldig reduktion av konfidensintervallet jämfört med F2 (andra generationen). Konfidensintervallet i F7 (sjunde generationen) för Eae18b var 3Mb och innehöll ungefär 10 konfirmerade gener hos råttan. Analysen av materialet är gjord med kopplingsanalys baserat på det publika råttgenomet [27]. Av yttersta vikt är också att motsvarande loci på människans kromosom 17q11 reglerar MS, vilket kan innebära att Eae18b reglerar gener/sjukdomsvägar som är av vikt för sjukdomen i både råttor och människor. Teoretiskt bör F10 AIL motsvara en femfaldig minskning av konfidensintervallet, vilket kan medföra ett urval av möjliga kandidatgener.

Preface During my final studies at the Royal Institute of Technology in Stockholm I completed my Master of Science degree in Computer Science with courses in Biomedical Engineering Science. The courses were mainly given at the Karolinska Institute in Stockholm. This gave me a splendid opportunity to combine two fields of interest, Computer Science and Medicine, which from the beginning of my studies always has been a goal for me personally. The opportunity to use the technical knowledge for a medical purpose. Since I have a strong personal interest for the field of genetics, I decided to write my final thesis on this subject. I also wanted to work with one of the major inherited diseases in this field and Multiple Sclerosis was both of these things integrated. After contact with Professor Tomas Olsson at the Centre for Molecular Medicine at the Karolinska Institute department for Clinical Neuroscience, Neuroimmunology Unit I was offered the possibility to work on Multiple Sclerosis in an genetic approach. The task was to fine dissect rat chromosome 10 and a specific region on this chromosome designated Eae18b. The animal model used at the department is Experimental Autoimmune Encephalomyelitis that closely resembles Multiple Sclerosis in Humans. From the genetic material gained from EAE the task was to narrow down the region on chromosome 10 – which further might give such high resolution that one might identify specific genes controlling these diseases. I was thrilled to be given this opportunity and of course I accepted.

Acknowledgements This thesis is a result of a work that started several years ago. There are so many people that I really would like to thank for their love and support that has made it possible for me to keep my goal clear and to stay on this path. I remember an interview with a Swedish actor several years ago; he said ”so I thought what is the highest mountain for me to climb, a challenge for me, which would contribute to my development?” For him it was to sing, for me it was to study for a Master of Science degree, and here I am, I finally have climbed this mountain and what a journey! First of all I would like to thank my dear supervisor at Karolinska - Maja Jagodic. I do not know how I should express it - but without your support, extensive knowledge and great understanding this surely would not have been possible to accomplish. Professor Tomas Olsson who gave me the opportunity to work on this thesis and extend my genetic knowledge- and also important for me, showed great support both professionally and personally. Monica Marta - where would I have been without you? You not only gave me support professionally on MS - but also on a personal so important plane - you were sent to me I am sure of it. Maria Swanberg - thank you for listening, sharing many important thoughts with me – life is a journey that we undertake, meeting you made it easier. To Margarita and Hedvig - thanks for everything, inspiring talks and lunches. To Britt, Johan, Sheng, Jian - your support in the lab goes beyond saying for me - I would not have survived genotyping without you! To all the other in Tomas Olssons group - thanks for support and for always being so nice to me! At KTH I would like to thank my supervisor Erik Fransén for supporting me and guiding me through this work pointing out the important steps, so I stayed on track and also for the commenting on my report. Anders Lansner, the examiner of this work - thanks. I also thank every teacher that I have met and studied for during the years at KTH - thank you all for sharing your great knowledge with me. At Luleå University there are many teachers that I really would like to thank for great courses- and for introducing me into the world of technology - to all of you thanks. My student friends from Luleå, thank you all for sharing though times and hard work with me- especially Anna Hedman, for being the best possible friend imaginable and Maria Wikström. You are super! At KTH - thank you all I have encountered through different courses - I learnt a lot from you all. Ingrid - thank you for becoming a dear friend throughout these years. Alexandra and Johanna - you simply are great! To all my personal friends that have encouraged me throughout this work- especially “Torsdagsgänget” - Lars-Åke, Maria, Pierre, Camilla, “Nogge”, Nicke, Cillan, Karolina - our conversations where truly inspiring! And the guys that had gone through the same education and knew were the difficulties in this education were, you made me look past those and continue - thank you all. There are several people that contribute to my daily existence in a wide variety of ways- thanks to all of my friends who have stuck with me, although my studies have occupied me completely during certain periods, you know who you are. I have enjoyed talking about other issues - trust me. My most dearest and valuable friend Monica Portugal - I cannot express what your support have meant to me - where would I be without you? And least but most important - to my family - thanks for all your love and support throughout these years - my mother and father, and my brother for inspiring me and being a role model for me. I do not think I would have been here today if you did not drag all those technical things into our home. Finally, to my dear husband - you made this possible and always supported me throughout everything - I cannot express in words how much this has meant to me, we have gone through an unbelievable journey together - but we made it in every possible way - I love you. To all the people that are in someway involved in my life - you are a part of my day - and thank you all!

Contents 1 Introduction .................................................................................................................................................. 1

1.1 Genetic regulation of Multiple Sclerosis ........................................................................................... 1 1.2 Goals.................................................................................................................................................. 1 1.3 Outline of this thesis .......................................................................................................................... 1

2 Background .................................................................................................................................................. 2 2.1 Multiple Sclerosis .............................................................................................................................. 2 2.2 Autoimmunity.................................................................................................................................... 2 2.3 Development of MS – genetic inheritance ........................................................................................ 3 2.4 Environmental factors........................................................................................................................ 3 2.5 Geographical distribution .................................................................................................................. 4 2.6 Treatment of MS................................................................................................................................ 4 2.7 The hunt for genes controlling MS – and why use EAE as a model ................................................. 4 2.8 Experimental autoimmune encephalomyelitis - EAE........................................................................ 4 2.9 EAE and genetics in rats.................................................................................................................... 5 2.10 MOG induced EAE ........................................................................................................................... 5

3 Theory .......................................................................................................................................................... 6 3.1 Genetics ............................................................................................................................................. 6 3.2 Key concepts ..................................................................................................................................... 6 3.3 Meiosis .............................................................................................................................................. 7

3.3.1 Significance of meiosis ........................................................................................................... 8 3.4 Chromosome structure....................................................................................................................... 8 3.5 Genotype and phenotype ................................................................................................................... 9 3.6 Linkage............................................................................................................................................ 10 3.7 Test crosses in linkage studies......................................................................................................... 11 3.8 Genetic maps ................................................................................................................................... 11

3.8.1 Resolution of the genetic map............................................................................................... 13 3.9 Markers as a genetic tool for study.................................................................................................. 13 3.10 Multifactorial traits – and Quantitative Trait Loci........................................................................... 13 3.11 Backcross......................................................................................................................................... 14 3.12 Congenic strains .............................................................................................................................. 16 3.13 Advanced Intercross Lines .............................................................................................................. 17

3.13.1 Proportions of recombinants in an AIL................................................................................. 18 3.13.2 Confidence intervals in an AIL............................................................................................. 18

4 Materials and Methods ............................................................................................................................... 20 4.1 Genotyping – techniques used ......................................................................................................... 20

4.1.1 PCR-Polymerase Chain Reaction ......................................................................................... 20 4.1.2 Labelling of DNA ................................................................................................................. 21 4.1.3 Autoradiography ................................................................................................................... 21 4.1.4 Radioisotope labelling of nucleotide precursors ................................................................... 21 4.1.5 Electrophoresis...................................................................................................................... 21 4.1.6 Polyacrylamide gels .............................................................................................................. 21

4.2 Statistics........................................................................................................................................... 22 4.2.1 Methods for mapping QTL´s ................................................................................................ 22 4.2.2 LOD-scores –Logarithm of the odds favouring linkage ....................................................... 22 4.2.3 The null hypothesis ............................................................................................................... 23 4.2.4 Permutation tests................................................................................................................... 23 4.2.5 Different statistical methods ................................................................................................. 23 4.2.6 The R/qtl software ................................................................................................................ 24 4.2.7 The Hidden Markov Model .................................................................................................. 25

5 Methodology .............................................................................................................................................. 26 5.1 Linkage studies in EAE ................................................................................................................... 27 5.2 Phenotyping in F10 AIL .................................................................................................................. 27

5.2.1 The use of AIL in F10........................................................................................................... 27

5.2.2 The origin of the F10 AIL- previous generations ................................................................. 27 5.2.3 The rats used in this model ................................................................................................... 28

5.3 Genotyping in F10 AIL ................................................................................................................... 28 5.4 Linkage in F10................................................................................................................................. 28

5.4.1 Genome-wide LOD thresholds ............................................................................................. 28

6 Results ........................................................................................................................................................ 29 6.1 Genes in Eae18b.............................................................................................................................. 38 6.2 Cytokines......................................................................................................................................... 39 6.3 Chemokines ..................................................................................................................................... 39 6.4 Comparison of generation F7 and F10 in Eae18b ........................................................................... 40

7 Summary .................................................................................................................................................... 42 7.1 Goals fulfilled? ................................................................................................................................ 42 7.2 Future work ..................................................................................................................................... 43 7.3 Conclusions ..................................................................................................................................... 43

8 References .................................................................................................................................................. 44

List of figures Figure 1: The destruction of the myelin sheaths surrounding the nerve cell is causing disruption in the signals

sent from the cell soma through the axons [21]............................................................................................. 3 Figure 2: The different steps of Meiosis [18].......................................................................................................... 7 Figure 3: A summary of the relationship between genes and chromosomes. The loci (singular locus) are the

positions along the length of a chromosome where particular genes are found. ........................................... 8 Figure 4: Producing the F1 generation by the intercross between red fruiters (RR) and yellow fruiters (rr).......... 9 Figure 5: Producing the F2 generation.................................................................................................................... 9 Figure 6: Crossing over between chromosomes and the exchange of genetic material [19]................................. 10 Figure 7: Genotypes and phenotypes of a test cross involving two unlinked genes with the resulting ratio 1:1:1:1

following the Mendelian laws in contrast to linked genes which interact and produces offspring that deviates from this ratio. ............................................................................................................................... 11

Figure 8: Determination of the relative position of genes A, B, C and D, using data from Table 1. .................... 12 Figure 9: A backcross experiment......................................................................................................................... 15 Figure 10: Different types of crosses. ................................................................................................................... 16 Figure 11: The construction of an AIL................................................................................................................. 17 Figure 12: State chart of the Markov model [9]. ................................................................................................... 25 Figure 13: Flow-chart over the steps involved in this project. .............................................................................. 26 Figure 14: Summary of F10 AIL. ......................................................................................................................... 29 Figure 15: Distribution of used markers in physical map (left) and in linkage map (right). ................................. 31 Figure 16: Summary of all phenotypes analyzed in this study.............................................................................. 32 Figure 17: The maximum LOD score distribution of F10. ................................................................................... 33 Figure 18: Analysis performed with sex and weight as covariates. ...................................................................... 34 Figure 19: Result from the analysis performed with sex taken into account......................................................... 35 Figure 20: Highest LOD score and size of Eae18b in F10.................................................................................... 36 Figure 21: The differences between the F7 generation and F10 generation in the physical map in Mb. ............. 40 Figure 22: The differences between generation F7 and F10 in the linkage map in cM. ....................................... 41 Figure 23: Difference between the generations F7 and F10 in the publicly available rat genome sequence. ....... 41

List of tables Table 1: Map distance calculated from a series of testcrosses. ............................................................................. 12 Table 2: 95 percentile............................................................................................................................................ 28 Table 3: The eight markers used in this analysis and their physical position in the genome in Mb, and the genetic

map distance in cM...................................................................................................................................... 31 Table 4: LOD scores calculated with Max as phenotype, and three different models/methods............................ 33 Table 5: Max LOD score for the different scans performed with sex and weight as covariates. .......................... 34 Table 6: Positions for the Eae18b peak in Mb. Analysis performed with max as phenotype and method Haley-

Knott............................................................................................................................................................ 37 Table 7: List of genes that are present in Eae18b both in rat and in humans, between 69.7Mb and 71.35Mb in rat

[27]. ............................................................................................................................................................. 38

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1 Introduction 1.1 Genetic regulation of Multiple Sclerosis Multiple Sclerosis (MS) is a chronic inflammatory disease that affects the Central Nervous System (CNS). This causes the disruption of the myelin sheaths surrounding the nerve cells and the development of a broad variety of symptoms among patients. It is a complex disease with many possible factors that may contribute to the development of MS. Today there are 2.500.000 people that are diagnosed around the world, and there is no available cure at the moment. Since MS is a complex disease where several factors contribute to the disease development, there is a need for a suitable model to be able to detect genetic regulation of MS. Here this is obtained by the usage of an animal model in rats in which an MS like disease – Experimental Autoimmune Encephalomyelitis (EAE) is induced. This model is then evaluated with both physical and theoretical methods. The material obtained from the animal model is then analyzed using linkage analysis and data were compared with the findings in human MS material. This gives a possibility to identify the genes that may contribute to the development of MS, and the goal is to be able to construct target specific therapies in the future.

1.2 Goals The work in this project is performed at the Neuroimmunology Unit at the Centre for Molecular Medicine, Karolinska Institute. It is a part of their research in the genetic field with the aim of finding specific genes involved in the regulation and development of MS. This work is a part of the dissection of rat chromosome 10 and a specific region, designated Eae18b in the previous study. Here the work has been performed in an AIL in the tenth generation, the F10. The main goals of this project are:

• Reproduction of the region Eae18b in rat chromosome 10 in the F10 generation Since this region was previously described in the seventh generation, the F7, of the same AIL [17], one would like to reproduce this region in F10.

• Fine dissection of Eae18b, and reduction of the region size One advantage of the possible reduction of this region in size is to be able to find possible candidate genes involved in the EAE development. Previous work on this region has defined Eae18b to be ∼ 3Mb big.

• Identification of genes involved in Eae18b As mentioned above the ultimate goal is to identify genes in Eae18b.

• Synteny between identified rat QTL and human MS QTL With the usage of publicly available rat and human genome sequences, show synteny between the regions in rat regulating EAE and the regions in humans regulating MS.

1.3 Outline of this thesis This thesis is divided into five major chapters. The first chapter is a brief introduction to the work. The second chapter gives the reader a background to Multiple Sclerosis and also how the EAE model used correlates to MS. Third chapter is theory, describing the underlying theories, models and techniques to the work performed. If the reader is very familiar with the principles in genetics, you might skip this or use it as a dictionary. The fourth chapter describes Methods and materials. The fifth chapter is methodology, here a description of the model used is illustrated, and it is also described more in detail. Sixth chapter contains the results obtained in this thesis. And finally the seventh chapter is a summary and a brief comment on future work.

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2 Background 2.1 Multiple Sclerosis Multiple sclerosis is a chronic inflammatory disease of the nervous system with the disruption of the myelin sheaths surrounding the nerves. It is one of the most common diseases of the Central Nervous System and about 2,500 000 people around the world is diagnosed today. The clinical course of the disease varies widely among patients and includes blurred vision, weak limbs, tingling sensations, unsteadiness and fatigue. Today there is no cure for MS.

Multiple Sclerosis (MS) is a chronic inflammatory disease of the Central Nervous System (CNS) and it causes destruction of myelin sheaths that serves as protection for the nerve cells and also enhances transportation of nerve signals. Patients with MS display a broad variety of neurological symptoms and signs, and those also vary according to time and conditions. MS is a complex disease and the factors effecting the development of the disease are both genetic predisposition and environmental influences. The course of the disease varies among patients it is categorized into different categories:

• Relapsing /remitting MS The most common form of MS (90% of the patients) where the patient goes to periods of relapsing/remitting MS. The episodic appearance of neurological symptoms is called relapses. The course of the RR MS is followed by either fully recover from the myelin damage or partially recoverage.

• Primary MS Where the disease course is continuously and gradually leading to a loss of certain functions.

• Secondary MS The relapsing/remitting disease course often transforms into secondary MS that is characterized by RR leading to gradual loss of nerve functions.

• Subacute MS The most uncommon form of MS, begins in the same way as RR MS but after several years there are few symptoms remaining.

The mean onset of the disease according to age is 28 years and females are more affected by the disease by a factor of 2:1.

2.2 Autoimmunity When our own defence immune system starts to react against it self this is referred to as autoimmunity. It is believed that MS may be an autoimmune disease where the inflammation and demyelination of the nerves may be caused by the body’s own defence system.

Autoimmunity is when our immune system is reacting against our own body tissues. It has not yet been proven that MS is an autoimmune disease, but there are some strong evidence pointing in that direction [15]. There are two processes active in MS, one is minor inflammation in the myelin sheaths, and the other is when the myelin sheaths are demyelinated. Axons and astrocytes are also affected. The development of these processes differs among individuals and can also occur in many different places in the CNS. Both demyelination and the process of axonal damage are caused by the presence of T-cells and macrophages that normally is a part of our defence mechanisms against foreign viruses and infections.

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The myelin sheaths are protecting the nerve cells and also transferring nerve signals. In the case of MS the myelin sheaths are attacked by the bodies own defence system and broken down in a process called demyelination which leads to the disruption of the nerve signals reaching their final destination along the axons. When the inflammation causing this demyelination is diminishing the nervous system is able to restore some of the myelin sheaths affected – remyelination. Remains of the inflammation are expressed in plaques on the nerve cells. There is also a gradual disruption of the nerve cells that causes the nerve cells to die.

Figure 1: The destruction of the myelin sheaths surrounding the nerve cell is causing disruption in the signals sent from the cell soma through the axons [21].

2.3 Development of MS – genetic inheritance Several factors are believed to contribute to the development of MS such as genetic inheritance and environmental factors.

The genetic inheritance of MS is not clearly understood. There are some connections in developing the disease if some other family members are affected and this is called genetic predisposition. But even if the genetic predisposition at a certain individual is relatively high it does not necessarily leads to the development of the disease. ∼70% in developing MS is believed to be due to environmental factors and ∼30% in genetic inheritance [11]. Evidence for genetic predisposition for MS is for instance the λR [12]. R is representing the ratio for recurrence risk for type R relatives. These are then compared to population prevalence. λR is used as an estimation of the degree of genetic factors influencing the development of the disease. In MS λsibs (sibs representing siblings) is ≈ 20-40 this is supporting genetic influence in MS since a λsibs ≈ ≥ 1.5 is necessary to motivate further linkage studies for identification of genes involved in MS. There are also additional studies made that support evidence for genetic influence in MS.

2.4 Environmental factors It has been concluded that environmental factors play an important role in developing MS. Environmental factors leading to the development of the disease can be for instance different types of viruses. These viruses could be the cause of the immune system reacting on it self and in turn the development of MS. Other possible environmental factors could be climate and diet also affecting the disease course.

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2.5 Geographical distribution MS is distributed geographically in a non-random way [15]. The classical grading divides areas geographically in high, medium and low risk groups.

• High risk areas Prevalence < 30/100 000, Northern and central Europe, Italy, middle part of North America, south eastern Australia, New Zealand.

• Medium risk areas Prevalence < 5-29/100 000, Southern Europe, Southern United States, northern Australia, nethermost Scandinavia, much of the north Mediterranean basin, white South Africa.

• Low risk areas Prevalence < 5/100 000, some parts of Africa and Asia, the Caribbean and Mexico.

These areas differences can be explained by the environmental and genetic factors. There are some populations that are relatively resistant against MS, for instance here in Sweden the Lapps. This in spite the fact that they live in a high-risk areas. Epidemics of MS are explaining this fact as a cause of environmental factors.

2.6 Treatment of MS There is no existing treatment today that fully reverses the disease development. There are some MS treatments but so far they only modify the course of MS, and reduce the symptoms. The available treatments today are Interferon-β 1a, Interferon -β 1b and Glatiramer acetate [11]. They have all shown some reduction in the frequency of the relapses, and also in development of new lesions. This effect varies among patients and there is quite obviously a great need for more efficient treatments in MS.

2.7 The hunt for genes controlling MS – and why use EAE as a model To be able to define specific genes that control the development of MS there is a need for a suitable model. Here EAE in rats is used as an autoimmune disease that mimics the disease course of MS in humans. The results from the rat model are then linked via analysis to the human material for MS and this creates the possibility to localize specific genes that may control the development of MS.

Because of the complexity of MS and the many factors contributing to the development of the disease there is a need for a model that closely resembles MS to be able to localize the genes controlling the genetic inheritance. The aim with localizing these genes is to be able to produce some target specific treatments of MS. For close resemblance to MS the animal model used for linkage studies should display properties such as MHC (Major Histocompability Complex) dependency, have a chronic relapsing/remitting disease course and exhibit an MS-like histopathology.

2.8 Experimental autoimmune encephalomyelitis - EAE Experimental autoimmune encephalomyelitis (EAE) is an animal model of autoimmune disease that shows many similarities and closely resembles to the disease course of MS in humans [12]. It is an organ specific inflammatory disease and can be induced into several species such as rats, guinea pigs, and non-human primates. The immunisation can be performed in several different ways according to specific protocols. Here EAE was induced by immunisation with an incoulum containing myelin ologodendorcyte glycoprotein and adjuvant. EAE is characterized by acute ascending paresis that affects the tail, hint legs and some times the front legs. This usually occurs 10-20 days after the immunisation of the animal. Loss of weight is also a clinical sign of EAE. The protocol used for immunisation of the animal usually determines the course of EAE; host factors in the animal induced also take part in the degree of myelination of the animal. The EAE disease course can be monophasic, chronic/relapsing or acute lethal.

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2.9 EAE and genetics in rats Inbred rat and mouse strains can be used as models for EAE and these strains enable the control of genetic influences. The differences in EAE susceptibility are a demonstration of the genetic predisposition for EAE. The rat genome and the disease course of EAE in rat and the resemblance to MS has enabled linkage studies to be performed and genome regions to be identified which show genetic predisposition to EAE. The identification of the susceptibility genes for EAE should allow disclosure of important disease pathways and since EAE mimics MS this could be of usage in the disease pathways of MS. The rat genome resources are increasing which enables qualitative linkage studies to be performed it has a coverage of ∼90% of the estimated 2.8 Gb genome [27].

2.10 MOG induced EAE Myelin Oligodendrocyte Glycoprotein (MOG) has been used together with Freund´s adjuvant to induce EAE in the animal model [17]. An adjuvant primarily stimulates the innate immune system, and the effect in the animal model may reflect bacterial and viral effects on the immune responses in humans. EAE was induced in two different inbred rat strains, DA and PVG. The DA rat is susceptible and has a clinical disease expression and CNS pathology that closely resembles MS. MOG induced EAE is associated with focal CNS lesions that resembles lesions in MS. The cells involved in developing the disease are T cells and the susceptibility to MOG is MHC (Major Histocompability Complex) dependent. When a comparison between different inbred rat strains were made susceptibility to MOG induced EAE was positively correlated to antigen specific IFN-γ producing cells, number of α-MOG secreting B-cells and α-MOG IgG levels, and were controlled by MHC and non-MHC genes [12].

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3 Theory In this section the underlying theory of genetics is described from a single cell to key concepts such as the structure of the chromosomes that carries our genetic material and also describing phenotypes and genotypes essential for this project.

3.1 Genetics Our life starts as a single fertilized egg, which develops through division and differentiation- ending up in an adult consisting of about hundred trillion cells [2]. Each cell is specialized for a particular function, for instance a nerve cell contracts and enables the passage of a nerve impulse. The information for these specific functions is stored in each cell – more specific in our genes. The human consists of approximately ∼30.000 genes – all of them stored within each single nucleated cell.

3.2 Key concepts Our entire organism is build from hundred trillions of cells – which in turn holds the specific information that guides our development into us all being specifically developed with our own personally code. This code that holds the information is stored inside each cell – in humans, which are eukaryotic, the information is stored in the nucleus of the cell. This information – the genetic information is called DNA – deoxyribose nucleic acid. RNA is another nucleic acid that transfers information from nucleus to cytoplasm. Nucleic acids are together with proteins, carbohydrates, and lipids the four major biomolecular classes in living organisms. In each nucleus the DNA is stored and distributed between chromosomes. Humans consists of 46 chromosomes all containing a linear DNA molecule in a complex with protein. The different regions of a chromosome are representing different hereditary units or genes. Chromosomes are dividing during the process of mitosis and meiosis. Mitosis is when the genetic material is exactly duplicated and distributed to two new nuclei during normal cell growth. Meiosis is a process when the genetic material is divided into two parts during the forming of gametes prior to sexual reproduction. The building blocks of DNA consists of four chemical nucleic acids all of them represented in a specific order these nucleic acids form a gene and when the information in the gene is expressed it leads to the production of a specific protein. The coded information within the DNA is decode d and expressed during a process called the central dogma. The central dogma consists of three important phases beginning with the RNA that transcribes the information from the DNA called transcription. The RNA molecule then associates with a cellular organelle the ribosome and this is where it directs the synthesis of the protein during a process called translation. The proteins are of great importance since they direct many different roles within our organisms. Some are enzymes catalyzing reactions others perform structural functions such as immunological nervous or hormonal.

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3.3 Meiosis Meiosis is a process when the genetic material is divided into two parts during the forming of gametes prior to sexual reproduction. This division and exchange of DNA is the principle for the differentiation in genetic material.

Each chromosome is a double structure called chromatids and these are in turn held together at the centre of the chromosome called the centromere [2][7]. During meiosis there is a production of nucleis consisting of half the number of chromosomes from the parental ones. Meiosis consists of two divisions, meiosis 1 and meiosis 2. Meiosis 1 is the reduction phase where the two homologous chromosomes first pair with one another and then separate resulting in two separate daughter nuclei each containing one chromosome. Sister chromatids remain together and completion of meiosis 1 results in daughter cells each containing one a single member of each chromosome pair, consisting of two sister chromatids. Meiosis 2 resembles mitosis that is cell division, when the sister chromatids separate and segregate into different daughter cells. Completion of meiosis 2 results in four haploid daughter cells, each containing only one copy of each chromosome. Pairing of chromosomes during meiosis is called synapsis and each homologous pair is called bivalent. The close association between the homologous chromosome pairing enables chromatids to be exchanged at a point called chiasmata. The exchanges between the chromatids leads to the exchange of the genetic material in the chromosome and results in genetic differentiated offspring.

Figure 2: The different steps of Meiosis [18].

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3.3.1 Significance of meiosis Meiosis enables the consistency of chromosome numbers between generations, prior to fertilization it results in four haploid cells and when these fuse a diploid zygote is produced [2]. Meiosis ensures variability among progeny leading to the production of new combinations of alleles. There are three processes that ensure that the genetic material is mixed:

• Independent assortment of chromosomes at Metaphase1 in which two or more possible alleles at one locus is found with which allele at another locus on a different chromosome.

• Swapping of pieces of homologous chromosomes during prophase1. Resulting in new combinations of alleles on the same chromosome.

• Random fertilization of gametes.

3.4 Chromosome structure Chromosomes in somatic cells can be divided into matching pairs of chromosomes referred to as homologous pairs. This result in that in humans the 46 chromosomes of a human somatic cell represents two sets of chromosomes each containing 23 different chromosomes and these originates from the paternal set from the father and the other from the maternal set originating from the mother. These homologous pairs have the same size, shape and function. Each chromosome withholds specific set of genes that characterize different traits. Each particular gene will always be found at a specific locus position on a given chromosome. Each gene consists of two copies one from the paternal and one from the maternal both carrying information and these are the alleles. The alleles in turn can be expressed in different ways. A homozygote is an individual that possesses two identical alleles and when the alleles are different the individual is heterozygous. The homozygous and heterozygous can also be expressed in a dominant or a recessive way.

R

dd

cC

BB

aA

r

Maternal chromosome

Paternal chromosome

Alleles of same gene

Different genes

Homozygous dominant alleles

Heterozygous alleles

Homozygous recessive alleles

Loci

Figure 3: A summary of the relationship between genes and chromosomes. The loci (singular locus) are the positions along the length of a chromosome where particular genes are found.

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3.5 Genotype and phenotype Genotype is describing the gene for a particular trait, and phenotype is how this gene is expressed in a physical manner.

Genotype is referring to a commonly used term in genetics and describing the particular gene that is associated with a given trait [2]. The phenotype represents how the genes are expressed in a physical manner and can be represented as for instance blood pressure, weight and other physical appearances. This leads to the fact that the genotype is determining the phenotype of an individual.

Parental genotype

Possible gametes

F1 genotype

F1 phenotype

Red fruiters Yellow fruiters

RR rr

R r

Rr

Red fruiters

Parental phenotype

Figure 4: Producing the F1 generation by the intercross between red fruiters (RR) and yellow fruiters (rr).

Parental genotype

Possible gametes

F1 genotype

F1 phenotype

Red fruiters Red fruiters

Rr Rr

R and r R and r

Rr

75% Red fruiters

Parental phenotype

RR Rr rr

25% Yellow fruiters

Figure 5: Producing the F2 generation.

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3.6 Linkage Linkage is when two traits are inherited together because the loci of the genes controlling them are situated on the same chromosome. This is of great importance for the investigation of specific genes and how these are inherited between strains causing recombinant offspring and non-recombinant offspring. The recombinant offspring is essential since the calculation of genetic maps and distances between genes is based on those.

The discovery of gene linkage is a result from the discovery made by William Baneson and Reginald Punett in 1905[2]. They performed a series of studies with the aim of investigating inheritance patterns of sweet peas. Mendelian technique was used to produce the first generation F1 and then they interbred to produce F2 plants. The breeding gave the expected F2 ratios but some deviated in a strikingly way from the expected result. Some of the four possible F2 phenotypes occurred more frequently than the other and the conclusion was that the greater production of two of the four possible gametes by the F1 plants was due to the inheritance according to dominant and recessive alleles. The dominant alleles and recessive alleles seemed to be inherited in a physically coupled manner and the result was that they were inherited together in most cases. This was the discovery of linkage- when two traits are inherited together because the loci of the genes that control them are situated on the same chromosome. Thomas Morgan later confirmed these findings in the early 1900´s. During meiosis 1 when the homologous chromosomes pair (prophase 1, see Figure 2) the chromosomes can exchange segments. This exchange is called crossing over and genes from one homologous have crossed over to the other chromosome a procedure referred to as breakage and rejoining of the two chromatids involved. This takes place at a cross over point called the chiasma on the chromosomes and the result from this crossing is referred to as a new offspring called non-parental of recombinant combination of alleles. Chiasmata occur randomly along the length of a chromosome but only in a proportion of the cells undergoing meiosis will a chiasmata occur. The frequency for chiasmata to occur depends on distance between genes, if the genes are closely linked fewer chiasmata occurs. The resulting gametes from the chiasmata, recombinant gametes will vary according to the parental ones. This all depending on the loci that is investigated, the further apart the two loci are the more recombinants will be produced. This frequency of recombinant offspring is of great importance when creating relative distances between genes and their loci and also the creation of chromosomal maps.

Figure 6: Crossing over between chromosomes and the exchange of genetic material [19].

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3.7 Test crosses in linkage studies Testcrosses are performed to display all gametes involved in the cross, and also most important if the genes that are involved in the cross are linked or not. The greater deviation from the Mendelian ratio the closer the genes are together.

Linkage studies is a tool for measuring the distance between genes and if they are in some way linked to each other and together control some specific character in the individual. A test cross can be very informative since it displays all the gametes that are involved in the cross, and also provides information whether the genes involved in the cross are linked or not [2]. A test cross is performed in the way that the F1 heterozygote generation is crossed with at tester homozygote generation with all recessive alleles. If the offspring would follow the Mendelian laws it would produce offspring’s in the ratio of 1:1:1:1 that is one gamete for every possible combination. This is also the case when genes interacting in the cross are not linked to each other. However when two genes controlling a character is interacting, the test cross will reveal this by producing four phenotypes with a different ratio according to the Mendelian one. If the genes are linked, the four phenotypes can have ratios that are for instance 74:1:74:1 showing that the parental phenotypes greatly outnumber the recombinant ones. This fact is used when distances between genes are calculated. The greater the deviation is from the Mendelian ratio, the closer the genes are together. This leads to less opportunity for crossing over to occur and the production of recombinants.

Parental genotypes

Possible gametes

Offspring genotypes

Offspring phenotypes

Ratio

F1 heterozygote Tester homozygote

AaBb aabb

AB Ab aB ab ab

AaBb Aabb aaBb aabb

one two three four

1 111

Figure 7: Genotypes and phenotypes of a test cross involving two unlinked genes with the resulting ratio 1:1:1:1 following the Mendelian laws in contrast to linked genes which interact and produces offspring that deviates from this ratio.

3.8 Genetic maps This section describes how the distances between genes are calculated based on the number of recombinants in the offspring and the conversion of these into map units.

Recombinants can be exemplified in the following way: suppose you have a person that is heterozygous at two loci and it is represented as A1A2 and B1B2.Then if the alleles A1 and B1 comes from one parent and A2 and B 2 from the other parent, you might get offspring that is either non-recombinant that is A1B1 or A2B2. You might also get offspring with the combination of alleles from both parents that are A1B2 or A2B1 and these are recombinants. If a test cross is performed and indicates that two genes are linked in some way, this information can be used to calculate the distance between the genes involved. For this calculation the number of recombinants is used creating a map distance [2].

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Calculation of the % of recombinant, the recombinant fraction, is calculated according to the formula:

progeny ofnumber totaltsrecombinan ofnumber tsrecombinan of % =

The % of recombinant is directly converted into a distance that is expressed in map units or centiMorgans, cM [2]. 1cM is defined as the distance between two genes that produces a recombination frequency of 1% in test crosses that is 1cM corresponds to 1% probability of cross over between two loci on the same chromosome. The physical conversion of 1 cM is that it corresponds to a DNA segment of approximately 106 nucleotide pairs.

Table 1: Map distance calculated from a series of testcrosses.

Genes involved in the testcross

Distances (cM) between genes

A/a x B/b 3.0 B/b x C/c 2.0 A/a x C/c 4.5 A/a x D/d 12.5 C/c x D/d 9.0

A DCB

3cM

12.5cM

4.5cM

9cM2cM

Figure 8: Determination of the relative position of genes A, B, C and D, using data from Table 1.

Here an example is produced between genes A, B, C and D with a test cross, leading to the production of map unit distances between these genes. Notably is that these distances are not additive along the chromosome. A single recombination event produces two recombinants and two non-recombinant chromatids. If the loci are well separated there may occur several crossing over between them, but the different types of crossovers always produce 50% recombinants in average, meaning that the recombination fraction never exceeds 0.5 however far apart the loci are. Because recombination fraction never exceeds 0.5, it is not additive along the chromosome.

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For this purpose map functions have been developed, and they represent a mathematical relationship that converts the observed recombination fraction between two loci to genetic map distance. For instance if crossovers would occur randomly along a bivalent and had no influence on each other the mapping function would be Haldane´s [28]:

( )Θ−−= 21ln21W

or

( )we 21ln21 −−=Θ

w is map distance and θ recombination fraction. However the crossovers do not occur randomly, since one forming of a chiasma inhibits a second nearby. This is called interference and there exists several mapping functions for calculation of varying degrees of interference, one of them is Kosambi´s mapping function [28]:

Θ−Θ+

=2121ln174w

or

11

21

4

4

+−

=Θ w

w

ee

3.8.1 Resolution of the genetic map The number of recombinations is an important limitation in the resolution of the genetic map. For instance, if there are no recombinations in the region of interest, then there is no possibility to detect the gene of interest. Important features of the parental strains used, is that they differ both at the phenotypic level and the genotypic level, necessary to be able to trace recombination events. To overcome this problem a larger population is needed. To increase the resolution of the genetic map, there is a need for improved statistical methodology.

3.9 Markers as a genetic tool for study Markers are essential for analyzing DNA material since they provide the investigator of predefined information which can be used as “flags” in the DNA material enabling the investigator to narrow down the region of interest, chop this region out and amplify it for analysis.

The use of markers has greatly improved the analysis of DNA. A marker is a site in the DNA that consists of repeats of some nucleic acids, and these sites have no specific effect on the individual’s phenotype [28]. They are usually situated in the non-coding regions of the chromosomes. Crosses are then performed with individuals that are heterozygous and the specific marker. Since the region fort the marker involved is known, and the mapping is a known gene with a phenotypic known effect, it is possible to conclude the position of the gene. When this information is known there are some different ways to continue the analysis, one might chop out the relevant DNA segment, clone the targeted sequence for further analysis, or determine the DNA sequence and determine the gene of interest from a molecular approach. Microsatellite markers are mostly (CA)n repeats and are used as markers and together with PCR analysis this provides a useful tool for defining the genes requested.

3.10 Multifactorial traits – and Quantitative Trait Loci Quantitative Trait Locis are locis in the genome that have several genes interacting in case of developing a disease. When the factors involved in developing MS reach a certain threshold this leads to the onset of MS. The interaction between these factors is complex and can be due to several relationships between them.

Many complex human diseases are developed according to complex relationships between the genetic and environmental factors. In MS for instance the disease is a product of several traits that when they reach a certain threshold is a level for disease onset. An individual can, according to this, either express the disease or not. There is a gradation of susceptibility of developing the disease. The underlying principle of this is that a number of different factors environmental and genetic influence the tendency of developing the disease. An individual that inherits a certain combination of alleles and also is exposed to some unfavourable environmental factors can develop the disease. Quantitative Trait Loci (QTL) is identified as the principle when there is more than one gene involved in quantitatively varying a phenotype [13]. Mapping of these QTLs is called QTL mapping and it

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is difficult according to a number of reasons. There can be several numbers of QTLs influencing and this is called polygenecity, and epistasis, the interaction between different QTLs is also a problem. These are all factors that contribute to building up the threshold for developing the disease. To be able to localize a Quantitative Trait Loci- several statistical methods are required. Many interesting traits such as blood pressure, survival time after infection are quantitative, often a result from many genes interacting, and also affected of environmental factors. If two inbred strains show consistent phenotypic differences –despite the same environmental factors – you can conclude that there is a genetic influence to consider. The phenotypic differences can be established by the use of several experimental crosses between strains

3.11 Backcross The use of different types of crosses between strains is important for establishing phenotypic differences. If two strains show phenotypic differences despite the same environmental factors one can conclude genetic influence. To be able to dissect a region of interest in the genome backcrosses and congenic strains are produced.

A backcross is performed when the investigator chooses two inbred strains – for example A and B – being the parental strains [2][13]. These two strains are then crossed – and one obtains the first filial – called F1. The F1 generation receive one copy from each chromosome from the parental strain that leads to heterozygous animals in F1. Then the F1 individuals are crossed with one of the parental strains. For example if an individual from the F1 is intercrossed with the parental strain B – the progeny receives one chromosome from F1 and one chromosome from B. This results in the progeny having a genotype at each locus that is BB or AB. The backcross is performed in a number of series, usually around 100, and it determines the phenotypic value for each individual. Each phenotype then corresponds to a specific number. Then each individual is genotyped with a number of genetic markers that are spaced at a certain distance through the genome of interest. Generally about 10-20 cM apart – where cM is the unit of genetic distance, and corresponds to the number of recombinations, 1 cM corresponding to a recombination factor of about 1%. Further for each marker and each individual it is observed if the F1 consists of the A or B allele or is heterozygous. This investigation then leads to specification of a genetic map, which specifies the order of the markers – and it will be based on the data obtained from the phenotyping and genotyping.

15

A backcross experiment begins with two inbred strains that differ in the trait of interest (e.g., the response to an invasive procedure; the numbers on the mice indicate phenotype values). The two strains are crossed to produce the F1 generation, which is then crossed back to one of the parental strains to obtain the backcross generation. The backcross generation exhibits genetic variation. The objective of the experiment is to identify genomic regions for which genotype predicts phenotype.

X

AA BB

AB

80

25605535

40

20

X

Figure 9: A backcross experiment.

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3.12 Congenic strains Inbred strains are used to establish the genetic influence in different diseases. The strains are homozygous at each locus and differ both phenotypically and genotypically when compared to each other, and this is of great importance for tracing the recombinations To determine if the QTL identified in F2 or BC cross is of interest for developing the disease, congenic strains are bred. The procedure is backcrossing of the fragment of interest from one of the parental strains onto a background consisting of the other parental genome. Then two heterozygous rats for the allele of interest are intercrossed, and these rats are parentals to the homozygous founders of the congenic strain. Here the DA is susceptible for developing EAE and the PVG is resistant. The congenic strains used were constructed by backcrossing fragment from PVG onto the background consisting of DA, and this is done for several generations. The size of the congenic fragment was ~50cM. If this congenic strain shows some difference in developing the disease, for instance that it drops below the threshold for developing the disease, it has an influence on disease onset and is of interest for further analysis. You might also construct a reciprocal; here it would consist of a PVG fragment inserted on to DA background for double-checking the fragment of interest. Further analysis of this QTL is then obtained by constructing recombinants, and efforts to narrow the congenic fragments down into smaller fragments and of course in the end, be able to localize single genes. In Figure 10, the congenic strain is constructed by the insertion of a resistant fragment on a susceptible background. F2 is backcrossed with one of the parental strains.

Figure 10: Different types of crosses.

x

Susceptible Resistant Congenic strain

x F1 x

Backcross Intercross

F2

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3.13 Advanced Intercross Lines An AIL is obtained by the breeding of two inbred rat strains for several generations resulting in an increase in recombinations and an increase in the resolution of the map. This gives a t/2 reduction of the interval of the region of interest for each generation compared to traditional F2 intercross.

One way of increasing the resolution of the genetic map is to increase the number of recombinations. This can be obtained by using an Advanced Intercross Line, AIL [1]. An AIL is obtained by the breeding of two inbred strains for several generations. They are randomly bred, avoiding sister brother mating, and this results in an increased number of recombinations and they are also accumulated so that the recombinations are more dense. The probability for recombinations is increased between any two loci. The requirements for an AIL are that the number of individuals should not fall below 100 individuals per generation. In Figure 11, random mating for several generations are resulting in an AIL with very dense DNA.

Figure 11: The construction of an AIL.

xF2

Advanced intercross line

F7 … F10 Random mating

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3.13.1 Proportions of recombinants in an AIL The proportions of recombinants in an AIL can be calculated according to [1]:

)1(21

21)1(

211 1

21

21 rrrrrrrrR ttttt −+=−−+−= −−− (1)

R= proportion of recombination in the F2 generation. rt = expected proportion of recombinant haplotypes between two loci in the Ft generation in an AIL. Rt-1= proportion of recombinant haplotypes in the previous generation.

21 )1(

21

−− trr = The net increase in recombinant haplotypes as a result of recombination in the double

heterozygotes consisting of the two original haplotypes (for instance AB/ab wich produces new recombinants).

212

1−trr = Recombination in double heterozygotes consisting of two recombinant haplotypes. (for instance

Ab/aB which regenerates the original parental haplotypes) From equation (1) rt can be derived as a function of r:

2)21(2)1(1 rrR

t

t−−−−

= (2)

Equation (2) can be approximated using a first order’s Taylor expansion:

2t

trR = (3)

So that,

trR t2

= (4)

3.13.2 Confidence intervals in an AIL A confidence interval is the distance from any given QTL to one end of the confidence interval, which width is determined in units of recombinations. The reduction of this interval is important since its narrows down the region of interest and allows fine dissection of the region and the possible identification of specific genes.

QTL mapping accuracy is expressed as the confidence interval, with a certain confidence level of QTL map location. For an F2 generation from a pair of inbred lines, phenotyped and genotyped with a given marker spacing the confidence interval depends on several parameters such as: length of chromosome, QTL location relative to chromosome ends, marker spacing, population size and standardized gene effect on the QTL. The width of the confidence interval is defined in units of recombinations. The number of recombinations is then translated into cM using certain mapping functions Haldane´s mapping function for instance.

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For the reduction of the confidence interval in an AIL to be obtained certain criteria must be met: The parameters mentioned above which influence the confidence interval in the QTL map location generation must be identical to those in the F2 generation, the proportion of the recombinants must be binomically distributed. Given a confidence interval C, in the Ft generation, the corresponding confidence interval on the scale of the F2 generation, C´ can be approximated according to [1]:

2/tCC =′

C = the confidence interval, the distance from a given QTL to one end of the confidence interval in proportion of recombination units C´ = corresponding confidence interval in F2 The conclusion is that the confidence interval is reduced by a factor t/2 for each generation. To obtain the corresponding confidence interval M´ in cM, C´ is transformed to cM using Haldane´s mapping function, and doubled to represent the total confidence interval length. The power of an AIL can be expressed in the comparative results between the confidence intervals between generations. For instance an F2 interval of size 20cM is reduced to 3.7cM after eight additional generations that is to generation F10, which gives a much higher resolution. But after another additional 10 generations it is reduced to 1.8 cM showing that those generations display a moderate and linear reduction of the confidence interval.

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4 Materials and Methods 4.1 Genotyping – techniques used 4.1.1 PCR-Polymerase Chain Reaction To be able to quantify genetic material- DNA there is a need for efficient tools. Here PCR technique is used and it enables the amplification of the DNA in a very effective way with an exponentially increase of the DNA. The advantage is of course the possibility to work with small amounts and still be able to amplify this material many times enabling many trials to be performed.

Polymerase Chain reaction (PCR) is a method used to isolate large amounts of a single DNA molecule. PCR was developed in 1988 by Kary Mullins [7]. Provided that some sequence of the DNA molecule is known, PCR can amplify this molecule in a most effective way, with reactions carried out entirely in vitro. A DNA polymerase is used for the amplification-it repeatedly replicates a defined DNA segment. The number of DNA molecules that are amplified increases exponentially, doubling with each round of amplification- this leads to the fact that a small piece of DNA can lead to a substantial amount of DNA as the result of the PCR. A single DNA molecule that is amplified through 30 cycles of replication – yields 230progeny molecules. This amount of DNA can easily be visualized as a discrete band of specific size when submitted to agarose or polycarylamide gel electrophoresis. The starting material for the PCR can be either cloned DNA fragment or a mixture of DNA molecules – for instance in this project were we used the genomic rat DNA. One specific region from this mixture of DNA can be amplified provided that the nucleotide sequences surrounding this region are known; this yields that primers can be designed. These primers (amplimers), and DNA precursors (the four deoxyncleoside triphosphates; dATP, dCTP, dGTP and dTTP) are then used to initiate the starting point for the PCR amplification. The primers are usually chemically synthesized oligonucleotides that contain 15 to 20 bases of DNA. Two primers are used to initiate the synthesis – one from each direction of the DNA strand- forward primer and reverse primer. The reaction is then started with the heating of the DNA strand to 95°C- this leads to the separation of DNA strands. Then the temperature is lowered and the primers are able to pair with the complementary sequences on the template strands. The PCR is a chain reaction because newly synthesized DNA strands will act as templates for further DNA synthesis in subsequent cycles. In one cycle of amplification two new molecules are synthesized from one template molecule- one round of amplification then give a twofold increase in DNA molecules. This procedure of cycles can be described in three steps:

1. Denaturation – typically about 93-95°C for human genomic DNA. 2. Reannealing at temperatures usually from about 50°C to 70°C. 3. DNA synthesis – typically at about 70-75° C.

The multiple heating and cooling cycles that are involved in PCR is carried out by heating blocks called thermocyclers. The DNA polymerase that is used are heat stable enzymes from bacteria- these bacteria live in hot spring with temperatures around 75°C. These polymerases are also stable at high temperatures that enable them to be used in the PCR to separate the strands of the DNA. For example the widely used taq DNA polymerase is obtained from Thermus aquaticus and is thermostable up to 94°C. RNA sequences can also be used for amplification by this method, if reverse transcriptase is used to synthesize a cDNA copy prior to PCR amplification. If enough of the gene sequence is known so that primers can be designed, then PCR amplification is an extremely powerful method of obtaining large amounts of DNA. For example defined DNA sequences up to several kilobases can be readily amplified from total genomic DNA, or a single cDNA can be amplified from total cell RNA. These amplified DNA segments can then be further analyzed, for example to detect genes within a specific region of interest.

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4.1.2 Labelling of DNA The DNA can be labelled in different ways, in vitro, by nick translation, randomly or as in this case end-labelled. The forward primer is end-labelled using polynucleotide kinase. The label isotope is 33P at the ATP, and the polynucleotide kinase catalyses an exchange reaction with the 5´- terminal phosphates. When using this technique to label double-stranded DNA, the fragment carrying this label, here the forward primer is generated by cleavage at internal restriction sites, resulting in two different sized fragments.

Isotopic labelling The labelling of nucleic acid can be performed by the incorporation of nucleotides that contains radioisotopes [28]. The radioisotopes that are commonly used for this purpose are 33P, 32P, 35S or 3H. These isotopes are then detected by the usage of autoradiography. The signal that is being emitted from the radioisotope depends on the emitted radiation from the isotope, and also on the time for exposure to the isotope. High- energy β- particles are preferred since they afford a high degree of sensitivity, and also permits a shorter time for exposure, are being used here to detect the nucleic acid molecules. The disadvantage is that it is relatively unstable, and therefore in medical examinations when a tissue for example is being examined, it is preferred to use a particle with less energetic emission. 33P is as mentioned a β- particle, with a half-life of 25.5 days and an energy level of emission of 0.248 Mev (compared to, for instance, 3H which has an energy level of emission of 0.019 Mev).

4.1.3 Autoradiography Autoradiography is a method used for localizing and recording a radiolabelled compound within a sample [6]. Here the sample consists of rat DNA that is embedded within a polyacrylamide gel. The photographic emulsions consist of silver halide crystals in suspension in a clear gelatinous phase. Following passage through the emulsion of the β- particle emitted by P33, the Ag+ ions are converted into Ag atoms. The resulting latent image is then converted by the development of the film.

4.1.4 Radioisotope labelling of nucleotide precursors Traditionally dideoxy sequencing methods have employed using radioisotope labelling; the dNTP mix contains a proportion of radiolabelled nucleotides, which are incorporated within the growing DNA chain. Following electrophoresis, the gel is dried and an autoradiographic film is placed in contact with the dried gel. After a suitable exposure time, the film is then developed, with the resulting characteristic bands with dark pattern

4.1.5 Electrophoresis Electrophoresis is a method used for the separation of DNA molecules through an electric field. This technique is used in genotyping and in this case to be able to distinguish between the different strains of rats according to their DNA and the results from the crossing of these strains.

Electrophoresis is a common method used in genotyping of DNA samples [6]. With this method molecules are separated on the basis on the rates of their migration in an electric field, according to their sizes. A gel, in this case a gel consisting of polycaclrylamide is loaded between two glasses, and these glasses are then placed between two buffer compartments that contain electrodes. The DNA sample is then being pipetted into predefined slots in the gel. The electric field is turned on, and the samples separate through the gel. The nucleic acids are negatively charged (according to their phosphate backbone) and migrate towards the positive electron that is placed in the lower compartment. The result is that the samples go through the gel, which acts as a sieve, and the larger molecules are moving slowing through the gel compared to the smaller fragments.

4.1.6 Polyacrylamide gels There are two basic materials that are being used to make the gels needed for electrophoresis: agarose and polyacrylamide. Here polyacrylamide is being used. Raymond and Weintraub introduced this technique in 1959[6]. The advantage of using polyacrylamide gel is that it can be prepared so that it provides different electrophoretic conditions. The pore size for the gel may be varied to create the best conditions possible for the nucleic acids that will separate in the gel, according to their molecular composition. The percentage of polyacrylamide is also being controlled; in this case a 6% polacrylamide gel was used. The percentage of polyacrylamide is used for defining the best conditions possible for the pores usually ranging in size from 5 to 2000 kd. Polyacrylamide gels offer sharply defined bands, which enable a more accurate and precise reading when autoradiography is being used.

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4.2 Statistics In this section the statistical methods used in this study are presented. The LOD score is explained and this is of extreme importance since further analyses depend on the LOD scores obtained, it measures the strength of evidence for a QTL in the region analyzed. If a QTL is found with significant LOD score one can proceed in the fine mapping of this area.

4.2.1 Methods for mapping QTL´s The aim in mapping a complex disorder is to find chromosomal regions that are shared between affected individuals and those that are not shared between affected and unaffected. This is accomplished in practical by the genotype and phenotype data. When one performs a linkage analysis- that is converts the data stored theoretically in the databases in Mb (10^6) bases of nucleotide pairs, to our linkage map represented in cM, the approach is the following [15]:

1. Scan entire genome with dense markers 2. Calculate linkage statistic S(x) at each position x along the genome. 3. Identify regions where the linkage performed deviates in a significant way from what the expected

deviation would be using independent assortment Interval mapping is a method developed by Lander and Botstein and it takes advantage of using the typed markers as a genetic map. Each location in the genome is posited, one at a time, and it assumes a presence of a single-QTL at this location. If the marker genotype data is known, one can calculate the probability that an individual has genotype AA, at a certain QTL. The QTL probabilities depend only on the genotypes at the nearest flanking marker that is genotyped [13].

4.2.2 LOD-scores –Logarithm of the odds favouring linkage The LOD score is a very significant tool in mapping QTLs since it defines the probability of a QTL [13]. It measures the strength of evidence for the presence of a QTL at the location z, compared to there being no segregating QTL in the cross. Larger LOD scores correspond to greater evidence of the presence of a QTL, and it is calculated at each position of the genome. The LOD score is calculated according to:

10log)( =zLOD likelihood ratio comparing the hypothesis of a QTL at position z versus that of no QTL

=σµ

σµµˆ,ˆ,QTL no|Pr(

)ˆ,ˆ,ˆ,at QTL|Pr(log 10

10 yzy zzz

zzz σµµ ˆ,ˆ,ˆ 10 are the MLEs(Maximum Likelihood Estimates) assuming a single QTL at position z No QTL model: The phenotypes are independent and identically distributed (iid) ),( 2σµN

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4.2.3 The null hypothesis How would one conclude that the observed peak actually is a QTL? As mentioned earlier the larger the LOD score the larger evidence for a QTL. But how large is large? This is achieved by hypothesis testing [16]. The thesis that this estimated QTL effect is based on is the null-hypothesis. That is that there are no segregating QTLs in the cross. The probability of obtaining a LOD score as large or larger than which was observed if there were no QTLs is called the p-value. Larger LOD scores give small p-values, and very small p-values indicate that either the null-hypothesis is false (that is there is a QTL) or a rare event occurred. The null distribution of the maximum LOD score depends on the type of cross (backcross or intercross), the size of the genome, the number and spacing of genetic markers, the amount and pattern of missing genotype information and true phenotype distribution. For instance LOD score of 3 indicates that chance of obtaining the observed data, given that there is a QTL at the specified position, is 1,000 times more likely than if there are no QTLs.

4.2.4 Permutation tests This is another way of estimate the appropriate LOD threshold, and it is based on permutations of the phenotypic data [16]. One permutes the phenotype data, keeping the genotype data intact, performs interval mapping and then identifies the maximum LOD scores across the genome. This process is repeated 1000 times. The observed LOD scores are compared to the 1000 LOD scores obtained from the permuted versions of data. The proportion of these 1000 LOD scores that exceed the actual observed LOD score is reported as an appropriate p-value.

4.2.5 Different statistical methods

The normal model This is the standard model for QTL mapping. This model assumes a normal distributed dataset of phenotypes. The analysis is analogous to linear regression [23].

The Binary model If the phenotypes are of binary character, that is with values of either 0 or 1. The proportions of 1´s in different genotype groups are compared.

The two-part model This model is valid in the case of a spike in phenotypic distribution. For instance if phenotype survival time after an immunisation is being analyzed and many individuals recover and fail to die. Individuals with QTL genotype g have probability pg of having an undefined phenotype (the spike) while if their phenotype is defined, it comes from a normal distribution with mean µg and standard deviation, σ. Three LOD scores are then calculated: LOD (p, µ) a test for hypothesis pg= p and µg=m LOD (p) is for test pg= p while the µg may vary. LOD (µ) is for the test that µg=µ while the pg may vary

The non-parametric model When there are incomplete genotype data the Kruskal-Wallis statistic is modified so that the rank for each individual is weighted by the genotype probabilities, analogous to Haley-Knott regression. Non-parametric models provide a more statistically robust and distribution-free method compared to parametric models [15].

Haley-Knott regression Haley-Knott regression is used. It is a regression method were regression of the phenotypes on the multipoint QTL genotype probabilities are calculated, as described by Haley and Knott (1992).

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Interval mapping This mapping procedure takes one marker at a time, assuming a single QTL model, and posits this as a putative QTL, then it interpolates between the markers using a configurable step length resulting in a continuous curve. A primary advantage with this mapping technique is that it takes into account missing data, and it is possible to analyze the material between the markers. Interval mapping has some advantages when calculating the LOD scores:

• It provides a curve since it calculates the data in between the typed marker data and this is an indication for QTL location.

• Allows for interference for QTLs to position between markers • Provides improved estimates of QTL effects • Appropriated performed interval mapping allows incomplete marker genotype data.

In the calculation of an individual’s QTL genotype probabilities, based on its marker genotype data, one considers the closest typed marker for that individual. If an individual is missing the marker genotype data for a flanking marker, one moves to the next flanking marker for which genotype data is available.

4.2.6 The R/qtl software The analysis in this project was performed by the usage of R/qtl [24], which is an add-on package for the freely available statistical language/software R [26]. R/qtl is an interactive environment for mapping the Quantitative Trait Locis (QTLs) in experimental crosses. R/qtl takes advantage of basic mathematical and statistical functions provided by R, and the main goal with the software is provide the user with complex QTL mapping methods so that the user can focus on modelling rather then computing. An essential part in R/qtl is that it uses the Hidden Markov Model (HMM) [9] for computation of QTLs and missing genotype data. The main HMM algorithm is implemented and it provides allowance for genotyping errors in backcrosses, intercrosses and phase- known four way crosses. The version available at this moment of R/qtl includes facilities for estimating genetic maps, the identification of genotyping errors, single-QTL genome scans and two-dimensional genome scans.

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4.2.7 The Hidden Markov Model The Hidden Markov Model [9] used in the R/qtl software is a statistical model which calculates the probabilities for genotyping errors. The system modelled is assumed to be a Markov process with unknown parameters. The aim of the model is to determine hidden parameters (such as genotyping errors), from the observable parameters (here: the genotyping data). Then the extracted parameters can be used for further calculations, in this case to calculate the LOD scores. In the regular Markov model, the state is directly visible to the observer; therefore the state transition probabilities are the only parameters. The Hidden Markov Model (HMM) adds outputs. Each state has a probability distribution over the possible output tokens obtained

x1 x3x2

a12 a23

a21

y3y2y1

b3b2b1

Figure 12: State chart of the Markov model [9].

x = states of the Markov Model (hidden in HMM) a = Transition probabilities b = output probabilities y = output tokens There are three canonical problems to solve with HMM, and this is obtained by the usage of three different algorithms:

• The forward algorithm [4] - given the model parameters it computes the probability of a particular output sequence.

• The Viterbi algorithm [29] - given the model parameters it finds the most likely sequence of (hidden) states which could have generated a given output sequence.

• The Baum-Welch algorithm [3] - given an output sequence find the most likely set of state transition and output probabilities.

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5 Methodology In this section the methods used in this project are described. The flow-chart represents a schedule over the different steps involved in the process. As represented the genotyping procedure has to been iterated several times to be able to extract data for analysis.

Start

Markers Cross preparationDA x PVG1.AV1

(N=794)

Same markersas in F7

Phenotyping:score

Candidate genes?

Congenic mapping

Synteny mappingCongenic strains

Unresolved

Linkage analysis

Rat’s map1 marker/cM

Linkage analysisLOD-score

Genotype all 794rats with 8markers

SignificantLOD scores? No

Yes

Figure 13: Flow-chart over the steps involved in this project.

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5.1 Linkage studies in EAE Genetic mapping and linkage studies of EAE are performed in this study by the intercross of two different inbred rat strains DA and PVG.1AV1 [17]. The DA rat is susceptible and the PVG1.AV1 rat is resistant to EAE. This guarantees that the genes that regulates EAE separates in the cross being performed. Because of crossing over, breakage and rejoining the animals will inherit different combinations of alleles from the parental strain, this leads to different expression of EAE and different phenotypes. To investigate were the genes are located that controls this expression one correlates phenotype and genotype at different loci.

5.2 Phenotyping in F10 AIL The rats in this model were immunized with recombinant rat MOG (rMOG) by a single intradermal injection in the dorsal base of the tail with 200µl inoculum containing rMOG in saline emulsified with incomplete Freud´s adjuvant [17]. After that the rats were weighed and monitored daily for clinical signs of EAE. The time period for clinical scoring was from day 7-10 post immunization until day of sacrifice at day 31-38 post immunization. The practical procedure for monitoring the rats clinically was then to follow a scoring procedure that consists of a scale divided as follows:

0. No clinical signs of EAE 1. Tail weakness of tail paralysis 2. Hind leg paraparesis or hemiparesis 3. Hind leg paralysis or hemiparalysis 4. Tetraplegy or moribund 5. Death

The dose for AIL animals for disease induction was 20µg/rat of rMOG. Age-matched rats between 8-16 weeks of age were used.

5.2.1 The use of AIL in F10 The problem so far with gene mapping in rodent inflammatory diseases is the resolution of the mapping. The technique has been a whole genome scan, covering the entire genome of for instance the rat genome, in a F2 cross or a backcross. The next step in solving the resolution of the area of interest in the genome has been to fine-map the regions by using congenic strains. The confidence intervals that are obtained in a F2 generation are usually around ∼ 20 cM, and this size corresponds to around ∼ 500 genes of interest in that particular area. Also there are several problems with linkage analysis in F2 crosses such as:

1. A single QTL which is obtained in the analysis may consist of several QTLs 2. Nearby QTLs with opposing effects may not be detected 3. A detected QTL may be incorrect positioned.

Many of these problems can be solved by the usage of an AIL cross. An AIL permits high resolution mapping of the QTL of interest and also allows possible assessment of interregional and intraregional gene interactions. In the F7 generation performed in this model in previous work [17] the region on chromosome 10, known as Eae18 initially around 30Mb big. Then by the usage of an AIL intercross between DA X PVG.1AV1 rats, this region was resolved into two peaks, Eae18a and Eae18b [17]. The AIL consists of randomly intercrossed inbred rat strains for several generations, and the individuals obtained with this technique are genetically unique with a mixture of chromosomal fragments from the parental strains. The number of recombinations is increased and the separation of linked QTLs is possible to perform, if the number of generations is large enough. The result between the F2 generation and the F7 in this model is a 3.5 fold reduction in the confidence interval.

5.2.2 The origin of the F10 AIL- previous generations The F10 AIL is the result of many years of work performed in the research group, to create the generations that proceeded F10.

The AIL used here originated from EAE-susceptible DA and resistant PVG.1AV1 rat strains. These rats share one part in the genome called the RT.1AV1 MHC haplotype, and this is important since the identification of non-MHC genes is possible [17]. The reason that these particular strains were used in this model is that they provide a high rate of polymorphic microsatellite markers, and this means that one can genotype very dense on the genome, and be able to narrow down the regions of interest. The F1 generation was created with two

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breeding pairs with DA female founders, and two breeding pairs of PVG.1AV1 female founders. Then the F2 generation was created from seven couples each of F1 rats with DA and PVG.1AV1 as female founders for each. The F3 generation was created from 50 breeding couples with both male and female founders. The following generations were obtained by random breeding of 50 males and females, consistently avoiding brother-sister mating.

5.2.3 The rats used in this model The rats used to set up an AIL were obtained originally from Zentralinstitut for Versuchstierzucht (Hannover, Germany). After that these rats were bred and kept at the Karolinska Hospital (Stockholm, Sweden). The rats were kept at the animal department in a 12h light/dark cycle, housed in polystyrene cages containing aspen wood shavings. Free access to water and standard rodent chow was also given. The rats were also routinely tested for specific pathogens according to health-monitoring program for rats at the National Veterinary Institute in Uppsala. Approval for the animal model was given by the local ethical committee in Northern Stockholm.

5.3 Genotyping in F10 AIL The genotyping procedure in this experiment consists of the following practical steps: The total number of rats used was 794 and they were all genotyped. This was accomplished by extracting DNA from the tail/ear tip all according to a standard protocol. The region that was analyzed in this project is on rat chromosome 10 and a specific part of this chromosome designated Eae18b [17] that is located between positions 67.789Mb and 71.081 Mb on the chromosome. The ∼ 3Mb(size o the region) was genotyped by the usage of 8 microsatellite markers extending from D10Rat159 positioned at 63,81 Mb to D10Got97 positioned at 71,84 Mb in the region. These markers were all previously used in earlier generations and this gives a nice possibility for comparison between the generations F7 and F10, and the goal is get a higher resolution of this region. The spacing of the markers is ∼1-2cM between the markers. Then Polymerase Chain reaction (PCR) amplification was performed, with the end labelling of the forward primer used with [γ-33] ATP. The PCR products were size fractionated on 6% polyacrylamide gels and visualized by autoradiography. The genotypes were all evaluated manually and then double-checked.

5.4 Linkage in F10 The physical map used to determine the marker order was the publicly available rat genome sequence [27]. The linkage analysis was performed using the R/qtl software [23]. Data were further analyzed by implementation of several statistical models such as non-parametric, Haley-Knott regression, binary model, and the 2-part model [16]. The phenotypes evaluated were maximum score for EAE, cumulative EAE score, duration of EAE, onset of EAE, sex differences for EAE and weight loss. Permutation tests were performed on the data for the detection of significant threshold levels for linkage analysis. The permutation procedure performed on the investigated data is empirical and reflects the characteristic of this particular experiment performed, meaning that it calculates the thresholds with the data obtained in this specific experiment. The permutation threshold can be compared to a genome wide significant threshold calculated by Lander & Botstein [15], were they assumed a very dense marker map, with specific considerations about the distribution of the QTL. Their suggestion to a genome wide significant threshold is a LOD score of 4.3, but this is a very stringent threshold compared to permutation tests which are based on this specific material.

5.4.1 Genome-wide LOD thresholds Number of permutations: 1000

Table 2: 95 percentile.

Inc Max Sum Dur Ons wl0 wl8 bin 1.918985 N/a N/a N/a N/a N/a N/a hk 1.891594 1.985825 1.969063 2.014250 1.882266 1.817092 1.940569 np 1.995006 1.880554 1.946797 1.927396 1.878972 2.033766 1.938772 2p N/a 2.295022 2.398718 2.380356 2.213519 2.372743 2.363703

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6 Results A total of 772 F10 (DAxPVG.1AV1) rats were subjected to MOG-induced EAE. The disease incidence was 29 % (223/772), affecting more females than males in ratio 2:1 (150:73). Genotyping was performed in 794 F10 rats (428 females and 366 males). For 22 of them clinical data were not obtained.

Figure 14: Summary of F10 AIL.

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Figure 14 shows a summary of diagrams over the F10 generation of AIL rats on chromosome 10.

• Missing genotypes The missing genotypes are indicated by black pixels, showing the distribution of the missing data on chromosome 10, for each single marker ( y-axis). 1b is describing the distribution of markers according to the genetic map, that is the public available map on the rat genome.

• Genetic map A histogram over the total number of rats in F10.

• S The distribution of males notated by 0 and females notated by 1.

• INC A histogram over the number of diseased animals, 0= healthy animals and 1= diseased animals.

• SUM A histogram describing the cumulative scoring.

• MAX The distribution of the max LOD score over the time period for the experiment.

• DUR Illustrates the duration of the disease, x-axis indicating the number of days.

• ONS The onset of the disease, x-axis number of days.

• W0 Weight at day 0.

• W8 Weight at day 8.

• LW Lowest weight distributed over the number of animals.

• WL0 Weight loss at day 0, calculated by the formula W0-LW/W0 (mean distribution).

• WL8 Weight loss at day 8, similar to histogram WL0.

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The markers used in the genome are positioned in Mb in the physical map this is according to the rat genome that is available at Ensembl [27]. Then when linkage analysis is performed the material used in this specific study is taken into account, and the number of recombinations is considered and converted into cM.

Figure 15: Distribution of used markers in physical map (left) and in linkage map (right).

Figure 15 is showing the distribution of the eight markers used in this analysis on chromosome 10. To the right is the distribution according to their physical location in the genome, in Mb. To the left distribution of the markers according to the genetic map, that is when linkage is performed and the distances are converted into cM. This is performed by interval mapping. The eight markers that were used are presented in Table 3.

Table 3: The eight markers used in this analysis and their physical position in the genome in Mb, and the genetic map distance in cM.

Marker name Chromosome Position in Mb Position in cM D10Rat159 10 63.181 63.181 D10Rat69 10 64.271 64.010 D10Rat243 10 67.789 67.393 D10Rat29 10 68.857 68.419 D10Rat123 10 69.697 72.715 D10Rat98 10 70.205 78.484 D10Rat58 10 71.081 81.693 D10Got97 10 71.842 89.008

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Figure 16 shows a summary of all the phenotypes analyzed in this study. They are: maximum LOD score, summary, duration of disease, onset of disease, WL0 - average weight loss at day 0, and WL8 - average weight loss at day 8. The analysis is performed with Haley-Knott regression, with the calculated step of 0.5.

Figure 16: Summary of all phenotypes analyzed in this study.

In Figure 16, the LOD scores shows significance in two peaks, the first peak from the right, designated Eae18b, which existed in generation F7, now shows reduction in size, and is positioned between markers D10R123 and D10R58. The positions of those markers are 69.697 and 71.081 resulting in a 1.384Mb region. There is also an additional peak appearing in this analysis designated Eae18c in this study between markers D10R159 and D10R29, at the position 63.181 and 68.857 in Mb. This peak was not visible in the earlier generations analyzed, F7. This could be due to a number of reasons: in F10 there is higher power due to the usage of larger sample set (800 animals compared to 300 in F7), also the induction protocol used for the immunization of the animal was stronger – in F7 the incidence of disease was 15%, here in F10 the incidence for disease is 30%. This peak requires further analysis and fine mapping. What can be concluded from this data is also that the different phenotypes all follow the same distribution, indicating the same disease course.

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Figure 17 shows the LOD score distribution for maximum EAE score in F10, performed by three different methods. Haley-Knott, which is an analysis of the variance, the non-parametric model described earlier, which is robust, but not so sensitive and the 2-part model also described earlier, which can be used in case of a spike distribution of the phenotypes.

Figure 17: The maximum LOD score distribution of F10.

The two peaks described above, Eae18b and Eae18c are apparent. Peak Eae18b´s maximum LOD score is presented in Table 4 showing that there is extremely high significance of this QTL. The result from this analysis show that the differences between the different models are not so big and that 2-part model has the highest LOD score with LOD 7.76 (but also much higher threshold levels due to the introduction of additional 2 df).

Table 4: LOD scores calculated with Max as phenotype, and three different models/methods.

Model/Method Position in genome cM Max LOD score Haley-Knott 83.18100 7.188722 Non-Parametric 82.68100 5.824901 2-part model 83.18100 7.763081

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Figure 18: Analysis performed with sex and weight as covariates.

Table 5: Max LOD score for the different scans performed with sex and weight as covariates.

Max LOD Chromosome Position in cM LOD No covariate 10 83.18100 7.533230 Sex as covariate 10 83.68100 6.930929 Weight as covariate 10 83.18100 7.980271 The result from the scans performed over this region are indicating that the additive effects from the covariates are contributing with small variations to the max LOD score, indicating that there is not so big difference in overall to be taken into account.

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Figure 19 shows the result from the analysis performed with stratification for the animal sex. The diagram shows a significant difference between males and females.

Figure 19: Result from the analysis performed with sex taken into account.

Also the weight loss from day and day 8 is taken into account. The weight loss follows the sex distribution in the diagram. The maximum LOD score for the females is 7.9 compared to the maximum LOD score obtained for the males, which is 1.4. The females were more affected by EAE with a ratio of 2:1 (150:73). This can be the result of that the males were underpowered, and also from the difference between males and females in clinical signs. It is observed that male’s loose weight, and this might be the only detectable phenotypic variance.

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Figure 20 shows the analysis performed on physical map, phenotype max, and Haley-Knott method.

Figure 20: Highest LOD score and size of Eae18b in F10.

The highest LOD score is 7.26. The Eae18b peak extends between 69.7 Mb and 71.35 Mb. This is calculated with a LOD drop, where LOD=LOD-1, defining were 69.7Mb and 71.35 are situated on the y-axis scale.

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This is to be compared with previous generation F7 were the Eae18b peak was located between 67.789Mb and 71.081Mb, the region is narrowed down from ∼3Mb in F7 to 1.65Mb in F10. The positions for Eae18b are presented in Table 6.

Table 6: Positions for the Eae18b peak in Mb. Analysis performed with max as phenotype and method Haley-Knott.

Marker/position Position in Mb LOD R123 69.697 6.343377loc6.6.c10 69.781 6.551533loc6.7.c10 69.881 6.766835loc6.8.c10 69.981 6.932780loc6.9.c10 70.081 7.037961loc7.c10 70.181 7.076469R98 70.205 7.075695loc7.1.c10 70.281 7.155561loc7.2.c10 70.381 7.229544loc7.3.c10 70.481 7.264884loc7.4.c10 70.581 7.259304loc7.5.c10 70.681 7.212199loc7.6.c10 70.781 7.124765loc7.7.c10 70.881 6.999890loc7.8.c10 70.981 6.841837loc7.9.c10 71.081 6.655791R58 71.081 6.655791loc8.c10 71.181 6.548894loc8.1.c10 71.281 6.416363 From the table above one can conclude that the highest LOD score obtained in the F10, region Eae18b, with max as phenotype is 7.26. The region is situated between 69.7Mb and 71.35Mb, in total 1.65Mb.

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6.1 Genes in Eae18b The peak Eae18b with defined limits between 69.7Mb and 71.35Mb is containing several different genes. The values are used in the publicly available rat genome sequence [27] for extracting which genes in the rat and human that are of relevance here in this study. Table 7: List of genes that are present in Eae18b both in rat and in humans, between 69.7Mb and 71.35Mb in rat [27].

Chromosome Name

Start Position (bp)

End Position (bp) Ensembl Gene ID

Human Ensembl Gene ID

Human External ID

Human Chromosome

10 70269886 70271684 ENSRNOG00000007159.2 ENSG00000108688.1 CCL7 17 10 70280989 70336017 ENSRNOG00000000239.2 ENSG00000108691.1 CCL2 17 10 70280989 70336017 ENSRNOG00000000239.2 ENSG00000108688.1 CCL7 17 10 70280989 70336017 ENSRNOG00000000239.2 ENSG00000108700.1 CCL8 17 10 70280989 70336017 ENSRNOG00000000239.2 ENSG00000181374.1 CCL13 17 10 70292784 70297385 ENSRNOG00000007335.2 ENSG00000172156.1 CCL11 17 10 70394294 70396899 ENSRNOG00000021851.1 ENSG00000108702.1 CCL1 17 10 70642212 70653250 ENSRNOG00000007455.2 ENSG00000181291.2 Q8WUF4 17 10 70997397 70998509 ENSRNOG00000007578.2 ENSG00000166729.3 NM_052857 17 10 71016000 71034573 ENSRNOG00000021815.1 ENSG00000005156.1 LIG3 17 10 71039003 71051930 ENSRNOG00000007596.2 ENSG00000092871.4 NM_057178 17 10 71107851 71120920 ENSRNOG00000021780.1 ENSG00000185379.2 RAD51L3 17 10 71122707 71129408 ENSRNOG00000008264.2 ENSG00000073598.1 NM_017559 17 10 71131521 71139192 ENSRNOG00000008287.2 ENSG00000073536.3 NM_018096 17 10 71142544 71169436 ENSRNOG00000009466.2 ENSG00000141161.2 CMYA4 17 10 71182775 71188704 ENSRNOG00000021719.1 ENSG00000166750.1 NM_144975 17

10 71206397 71216495 ENSRNOG00000021412.1 ENSG00000186356.1

http://www.ensembl.org/Rattus_norvegicus/ 17

10 71206397 71216495 ENSRNOG00000021412.1 ENSG00000172716.3 NM_152270 17 10 71206397 71216495 ENSRNOG00000021412.1 ENSG00000154760.2 NM_144682 17 10 71308981 71312911 ENSRNOG00000021357.1 ENSG00000172123.1 NM_018042 17 The genes listed in Table 7 are the ones that are defined in Eae18b [27]. The first genes listed above, in column Human External ID, are cytokine precursors and belong to the cytokine family (CCL). The gene NM 057178 is a ring finger protein fragment. For the following genes in the list there are no descriptions at this time.

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6.2 Cytokines Cytokines are protein molecules that are released by cells when activated by antigen that are involved in cell-to-cell communications. They are newly synthesized polypeptides and have similar effect to hormones on other cells. They are acting as enhancing mediators for immune responses through interaction with specific cell-surface receptors on leucocytes. Different kinds of cytokines are: interleukins (produced by leucocytes), lymphokines (produced by lymphocytes), interferons and tumour necrosis factor. [18] The cytokines are relatively small ∼8-10 [19].

6.3 Chemokines This is a new group of cytokines that are pro-inflammatory and activation inducible. These proteins are mainly chemotactic for different cell types [19]. The chemokines show a 20-50 percent sequence homology among each other at the protein level. They also share common gene structures and tertiary structures. According to the chromosomal location of individual genes, two different subfamilies are distinguished, alpha-chemokines and beta-chemokines. The members of the beta-chemokines or 17q-chemokine family map to human chromosome 17q11-32. The first to cysteine residues are adjacent, and therefore they are called CC-Chemokines. The chemokines that are present in Eae18b are: CCL1, CCL2, CCL7, CCL8, CCL11 and CCL13. More specifically CCL2, CCL7 and CCL8 are monocyte-chemo attractant protein-3 MCP3 [27]. This protein specifically attracts monocytes but not neutrophils. It is produced by a variety of tumour cell lines and regulates protease secretion by macrophages. Furthermore MCP-3 does not posses suppressive activity against immature subsets of myeloid progenitors, stimulated to proliferate by multiple growth factors.

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6.4 Comparison of generation F7 and F10 in Eae18b

Figure 21: The differences between the F7 generation and F10 generation in the physical map in Mb.

Figure 21 shows the differences between the F7 generation and F10 generation in the physical map in Mb. Phenotype is max. Clearly we see a higher LOD score distribution between markers D10R159 and D10R43 in F10. This distance needs to be genotyped more densely, especially between D10R69 and D10R243 to be able to see the distribution in the material. But where peak Eae18b is situated we can conclude that there is a higher LOD score obtained in F10, and also that there is a spike distribution in F7 that resolves in broader peak in F10.

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Figure 22 shows the linkage map between generation F7 and F10, phenotype Max and Haley Knott regression as method. This is calculated according to the material used here in this study and with an estimation of the recombinations produced. Here we can also see that there is a higher LOD score obtained in F10 compared to F7, the spike distribution in F7 is a broader peak in F10. The additional peak that appeared in F10 is showing large LOD significance, and is an object for further analysis and denser genotyping.

Figure 22: The differences between generation F7 and F10 in the linkage map in cM.

Figure 23 describes the difference between the generations F7 and F10 in the publicly available rat genome sequence [27] for the peak Eae18b. The values in Mb are based on the same calculations as mentioned above when comparison is made. The F7 generation stretched between 69.2Mb to 70.6Mb. The F10 was situated between 69.7 and 71.35Mb. The two generations overlap between positions 69.7Mb and 70.6Mb, this showing that this region is reproduced and of significant interest.

Figure 23: Difference between the generations F7 and F10 in the publicly available rat genome sequence.

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7 Summary The work performed within this project is:

• Fine dissection of rat chromosome 10, region Eae18b [17].

• The dissection was performed using an AIL, between two rat strains, susceptible DA and resistant PVG.1AV1.

• The work was performed by phenotyping and genotyping of the F10 AIL rat model, and also by

linkages studies for the analytical part of the work.

• The peak Eae18b was reproduced from F7 and was also visible here in this work in F10.

• The peak Eae18b was genotyped and phenotyped, analyzed by linkage studies and reduced in size from previous F7 generation of ∼ 3Mb to a size of ∼ 1.65 Mb in generation F10.

• The region Eae18b contains ∼ 8 confirmed genes – a chemokine cluster of CCL1, CCL2, CCL7, CCL8,

CCL11 and CCL13. Also there were some additional genes included in the peak, not confirmed yet.

• The maximum LOD scores obtained for Eae18b is ranging from 5.24 to 7.26, showing significance for this QTL and also for further fine mapping of this region.

• The additional QTL was resolved using F10 AIL, designated Eae18c, not visible in earlier generations,

F7. This peak requires further analysis and fine mapping (see Figure 16).

Linkage studies showed syntenic loci with between rat chromosome 10 and chromosome 17 in humans, more specifically loci 17q11, where previous work has identified MS QTLs [17].

7.1 Goals fulfilled? After completion of the experiment and the analytic part, the interesting question is if the goals set for this project are fulfilled:

• Reproduction of the region Eaea18b in rat chromosome 10 in the F10 generation.

This goal is fulfilled; the peak Eae18b was reproduced consisting of a QTL in the F10 AIL generation. Also, the additional QTL, here designated Eae18cc has been identified. This peak show significant and high LOD scores for the QTL and is a good candidate for further fine mapping.

• Fine dissection of the region and reduction of the region in size

Previous size of the region was ∼ 3Mb in F7 generation. Here the size of the region is reduced to ∼1.65Mb in F10 generation. Goal fulfilled.

• Identification of genes involved in Eae18b

The genes in peak Eae18b are listed in Table 7. Thus, this goal is also fulfilled • Synteny between identified rat QTL and human MS QTL

With the usage of the publicly available rat genome map, synteny between genes in QTL Eae18b and human chromosome 17q11 is confirmed. Previous studies have showed that 17q11 is of relevance for MS QTLs in humans [17]. Goal fulfilled.

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7.2 Future work The work done in this project is part of the research performed at the department for Neuroimmunology, Centre for Molecular Medicine Tomas Olssons group. More specifically the region Eae18b in rat chromosome10 has been analysed and fine dissected in F7 by my supervisor Maja Jagodic in her PhD studies. Future work in this region would be to continue linkage studies and fine dissection of both region Eae18b and Eae18c with the construction of different congenic strains and intercrosses. The ultimate goal for this research is to be able to present a candidate gene for regulating MOG-EAE with linkage to candidate genes regulating Multiple Sclerosis. The identification of these genes will improve the understanding of the underlying mechanisms leading to the development of the disease; also it could contribute to the development of more effective treatments for MS patients. With more understanding of the genetic regulation causing the complex MS pattern in the future, one might be able to construct tailor-made therapies. But what looks fairly logical and approachable in theory, can as we all know have some practical limitations. This requires a lot of work and is also very time consuming, since the intercrossing of rat strains and the production of congenic strains is time demanding work. A limitation for constructing congenic strains is also the number of recombinants that is needed for the increased resolution of the genetic map. Since nature is involved in the production of these recombinants this is a limitation that will not be changed. Hopefully this field of science will evolve in the same revolutionary way as previous, and with the genome sequences available and always under construction in combination with more sophisticated software tools for analysis this is a reachable goal.

7.3 Conclusions The aim of this project was to fine-dissect a specific region, Eae18b the rat chromosome 10 in. This work was accomplished with the phenotyping of the animal model according to the previous described procedure in section 5.2, and also by genotyping described in section 5.3, and finally analysis described in section 6. Personal reflections on the work performed:

• What can be said about the phenotyping is that several different people perform it during the time period for the experiment. The human factor is a parameter contributing to the validation of the scoring scale, and how the animal is diagnosed in disease severity. The most accurate phenotype is probably the weight since this is not a phenotype were the person performing the scoring is using some ranking test. Also, although the animals were kept and bred for several generations in the same environment this is also a parameter that must be taken into account, since the environmental factors contributing to the disease is not clearly understood.

• The genotyping procedure described in section 5.3 is also worth mentioning. This is the most time

consuming part of this project, and also the part were the steps had to be reproduced several times, due to my lack of practical lab skills and also to the material and procedures involved in the process. The practical procedure consists of several steps with many possible results that could affect the outcome of the obtained results. For instance are the films manually read and double-checked, this is involving different people following the same procedure, nevertheless there is a human factor to consider. This can be overcome by the usage of more specific techniques available on the market the fluorescence technique that makes the genotyping procedure more precise, and also much less time consuming.

• The spacing of the markers used here could also be denser; this would increase the available data for

analysis, and thus contribute to more precise analysis. This was not done, since this work had to be finished according to the time available. The software tool used for the analysis, R/qtl is always under development, and this could also contribute to more methods and techniques to perform analysis with. Also the ongoing work on the rat and human genome sequences might contribute to more thorough studies.

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8 References [1] A. Darvasi and M. Soller: Advanced Intercross Lines, an Experimental Population for Fine

Genetic Mapping. In Genetics 141: 1199-1207, 1995 [2] Alison Thomas: Introducing Genetics. Nelson Thornes Ltd, 2003 [3] Baum-Welch algorithm, http://www.cs.umass.edu/~mccallum/courses/inlp2004/lect10-

hmm2.pdf. Last visited April 2004 [4] Forward algorithm, http://www.comp.leeds.ac.uk/roger/HiddenMarkovModels/html_dev/

forward_algorithm/s1_pg1.html. Last visited April 2004 [5] Fuad Iraqi, Steven J.Clapcott, Praveen Kumari, Chris S. Haley, Stephen J. Kemp and Alan J.

Teale: Fine mapping of trypanosomiasis resistance loci in murine advanced intercross lines. In Mammalian Genome 11: 645-648, 2000

[6] Gel Electrophoresis, http://www.bergen.org/AAST/Projects/Gel/ElecNucleics.htm. Last visited,

April 2004 [7] Geoffrey M. Cooper: The Cell. ASM Press, 2000 [8] Henrik Bränden & Jan Andersson: Grundläggande Immunologi. Studentlitteratur 1998 andra

upplagan [9] Hidden Markov Model, http://en.wikipedia.org/wiki/Hidden_Markov_model#External_links.

Last visited April 2004 [10] Information on Cytokines, http://www.copewithcytokines.de/cope.cgi?006272 Last visited April

2004 [11] Information on MS, http://www.multipelskleros.nu. Last visited, April 2004 [12] Ingrid Dahlman: Genetic dissection of experimental autoimmune neuroinflammatory diseases in

rats. Karolinska Insititutet, 1999. [13] Karl W. Broman: Review of statistical methods for QTL mapping in experimental crosses. In

Lab Animal 30(7):44-52, 2001 [14] Karl W. Broman, Hao Wu, Śaunak Sen and Gary A. Churchill: R/qtl: QTL mapping in

experimental crosses. In Bioinformatics Application note Vol.19 no.7:889-890, 2003 [15] Kristina Bečanović: Genetic regulation of autoimmune neuroinflammation. Karolinska

Insititutet, 2003. [16] Lecture notes, http://www.biostat.jhsph.edu/~kbroman/teaching/misc/quantGenet/

quantGenet.pdf. Last visited, April 2004 [17] Maja Jagodic, Kristina Bečanović, Jian Rong Sheng, Xingchen Wu, Liselotte Bäckdahl, Johnny

C. Lorenzen, Erik Wallström and Tomas Olsson: An advanced intercross line resolves Eae18 to two narrow QTLs syntenic to multiple sclerosis QTLs. Accepted in The Journal of Immunology.

[18] Meiosis, http://www.accessexcellence.org/AB/GG/meiosis.html. Last visited April 2004 [19] Meiosis, http://www.erin.utoronto.ca/~w3bio380/Lectsked/Supple2/meiosis.html. Last visited

April 2004

45

[20] Min Wang, William J. Lemon, Gongjie Liu, Yian Wang et al.: Fine Mapping and Identification of Candidate Pulmonary Adenoma Susceptibility Genes Using Advanced Intercross Lines. In Cancer Research 63: 3317-3324, 2003

[21] Multiple sclerosis international federation, http://www.msif.org. Last visited April 2004 [22] Oxford Medical Dictionary. Oxford University Press, 2002 [23] QTL manual, http://www.biostat.jhsph.edu/~kbroman/qtl/qtl-manual.pdf. Last visited April

2004 [24] QTL homepage, http://www.biostat.jhsph.edu/~kbroman/qtl . Last visited April 2004 [25] Restriction Endonucleases, http://www-ang.kfunigraz.ac.at/~binder/thesis/node26.html. Last

visited, April 2004 [26] R homepage, http://www.r-project.org. Last visited April 2004 [27] The Ensembl Genome Browser, http://www.ensembl.org. Last visited, April 2004 [28] Tom Strachan & Andrew P. Read: Human Molecular Genetics. BIOS Scientific Publishers Ltd,

2000 [29] Viterbi algorithm, http://www.comp.leeds.ac.uk/roger/HiddenMarkovModels/html_dev/

viterbi_algorithm/ s1_pg2.html. Last visited April 2004 [30] Xiaosong Wang, Isabelle Le Roy, Edwige Nicodeme, Renhua Li et al.: Using Advanced

Intercross Lines for High-Resolution Mapping of HDL Cholesterol Quantitative Trait Loci. In Genome Research 13:1654-1664, 2003