The Genetics of Systemic Lupus Erythematosusuu.diva-portal.org/smash/get/diva2:171099/FULLTEXT01.pdf · 2009. 2. 14. · The Genetics of Systemic Lupus Erythematosus The Specificity

ACTA

UNIVERSITATIS

UPSALIENSIS

UPPSALA

2007

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Medicine 296

The Genetics of Systemic LupusErythematosus

The Specificity of IRF5 to SLE.

MV PRASAD LINGA REDDY

ISSN 1651-6206ISBN 978-91-554-7038-8urn:nbn:se:uu:diva-8332

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List of papers

1. Graham RR*, Kozyrev SV*, Baechler EC*, MV Prasad Linga Reddy*, Plenge RM, Bauer JW, Ortmann WA, Koeuth T, Gonzalez Escribano MF; Argentine and Spanish Collaborative Groups; Pons-Estel B, Petri M, Daly M, Gregersen PK, Martin J, Altshuler D, Behrens TW, Alarcon-Riquelme ME. A common haplotype of interferon regulatory factor 5 (IRF5) regulates splicing and expression and is associated with increased risk of systemic lupus erythematosus. Nature Genetics. 2006 May; 38(5):550-5.

2. Sergey V. Kozyrev, Susanna Lewén, MV Prasad Linga Reddy,

Bernardo Pons-Estel and the Argentine Collaborative Group, Torsten Witte and the German Collaborative Group, Peter Junker, Helle Laustrup, Carmen Gutiérrez, Ana Suárez, Ma. Francisca González-Escribano, Javier Martín and the Spanish Collaborative Group, Fabio Sánchez, and Marta E. Alarcón-Riquelme. Structural INDEL Variation defines the Isoforms of IRF5 in a haplotype-specific fashion and Interacts with the 5‚UTR Polymorphism to increase the risk for SLE. Arthritis and Rheumatism. 2007:Apr;56(4):1234-41

3. M.V Prasad Linga Reddy*, Velazquez-Cruz R*, Baca V, Lima G,

Granados J, Orozco L, Alarcon-Riquelme ME. Genetic association of IRF5 with SLE in Mexicans: higher frequency of the risk haplotype and its homozygozity than Europeans. Human Genetics. 2007 Jul;121(6):721-7.

4. Blanca Rueda* ,M V Prasad Linga Reddy* ,Miguel A .Gonzalez -

Gay, Alejandro Balsa,Dora Pascual-Salcedo,Ingemar F. Petersson, Alicia Eimon, Sergio Paira, Hugo R. Scherbarth, Bernardo A. Pons-Estel,Maria F.Gonzalez-Escribano, Marta E.Alarcon-Riquelme, and Javier Martin. Analysis of IRF5 Gene Functional Polymorphisms in Rheumatoid Arthritis. Arthritis and Rheumatism. 2006 Dec;54(12):3815-9.

5. Fabio Sánchez1, MV Prasad Linga Reddy, Kazuko Sakuraba,

Mona Ståhle and Marta E. Alarcón-Riquelme. Interferon regulatory factor 5 gene variants interact with the MHC class I locus in the

Swedish psoriasis population. The Journal of Investigative Dermatology(submitted).

* These authors have equal contribution.

Contents

I. Introduction ...............................................................................................11 A. Human genetics ...................................................................................11 B. Microsatellites .....................................................................................12 C. Single nucleotide polymorphisms (SNPs) ...........................................12 D. Types of genetic diseases ....................................................................13

a. Monogenic diseases .........................................................................13 b. Polygenic (Complex or Multifactorial) disease ...............................14 c. Mitochondrial disease ......................................................................14

E. Approaches for the identification of complex disease genes ...............15 a. Linkage ............................................................................................15 b. Association and Linkage Disequilibrium ........................................15

1. Family-based designs..................................................................16 2. Case control studies ....................................................................18

F. Population stratification .......................................................................19 G. Genetic heterogeneity..........................................................................19 H. Epistasis...............................................................................................20 I. Gene-gene and gene-environment interaction ......................................20 J. Statistical methods ................................................................................21 K. Functional studies................................................................................23

II. Autoimmunity in general .........................................................................27 A. Immunological aspects ........................................................................27 B. Clinical aspects ....................................................................................31 C. IFN Signature ......................................................................................31

III. Autoimmune diseases .............................................................................32 A. Rheumatoid arthritis (RA)...................................................................32 B. Psoriasis ...............................................................................................33 C. Systemic lupus erythematosus: An overview ......................................33

IV. Different types of lupus ..........................................................................35 A. Systemic lupus erythematosus (SLE)..................................................35 B. Discoid lupus erythematosus ...............................................................35 C. Drug-induced lupus .............................................................................35

D. Neonatal lupus.....................................................................................36

V. Characteristics of SLE .............................................................................37 A. Epidemiology ......................................................................................37 B. Symptoms of SLE................................................................................38 C. Criteria for diagnosis ...........................................................................38 D. Lupus treatment and management.......................................................39

VI. Genetics of SLE......................................................................................41 A. HLA.....................................................................................................41 B. Complement system ............................................................................41 C. Fc� receptors ........................................................................................42 D. PDCD1 ................................................................................................42 E. PTPN22................................................................................................43

VII. The interferon system in SLE................................................................45 A. IFN-�...................................................................................................45 B. IRF5.....................................................................................................48

VIII. Paper descriptions ................................................................................53 Aim...........................................................................................................53 Paper 1......................................................................................................53 Paper 2......................................................................................................56 Paper 3......................................................................................................57 Paper 4......................................................................................................59 Paper 5......................................................................................................61

IX. Concluding remarks................................................................................64

X. Acknowledgements..................................................................................67

XI. References ..............................................................................................69

Abbreviations

SLE Systemic lupus erythematosus SNPs Single nucleotide polymorphisms TDT Transmission-Disequilibrium Test FBAT Family-based association test LD Linkage Disequilibrium HLA Human leukocyte antigen PDCD1 Programmed cell death-1 PTPN22 The protein tyrosine phosphatase 22 IRF5 Interferon regulatory factor 5 MAF Minor allele frequency MtDNA Mitochondrial DNA HWE Hardy–Weinberg equilibrium TLRs Toll like receptor ISGF3 Interferon stimulated gene factor ISRE Interferon stimulated response element Fc�Rs Fc gamma receptors STAT Signal transducer and activator of transcription

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I. Introduction

A. Human genetics The genotype is the genetic constitution of an organism that is not directly observable in a living organism. In contrast, the phenotype of an organism represents its actual physical properties such as height, weight, and hair color. The genetic constitution is what makes individuals different from each other. Males have 22 pairs of autosomes and two sex chromosomes, the X and Y chromosomes, while females have 22 pairs of autosomes and a pair of X chromosomes. It can be difficult for non-scientists to understand how our 46 chromosomes are conserved in our offspring. This is because during gametogenesis reduction, division occurs, and results in the generation of egg and sperm with only half of the original number of chromosomes (23). Thus, the fused egg and sperm results in a zygote with 46 chromosomes of which 23 come from the mother and 23 from the father. The genetic information in all living things is stored in deoxyribonucleic acid (DNA), which is present in chromosomes. DNA is made up of adenine, thymine, guanine and cytosine (A, T, G and C) molecules. The DNA contains genes and its function is to transmit hereditary information. Genes are divided into coding (exon) and non-coding (intron) regions. Most of the DNA sequence between individuals is the same (99.9%). The small percentage that is different makes one individual different from the other. Genetic differences can either be in the total number of chromosomes (copy number variations), a part of a chromosome (microsatellites), or a base pair (single nucleotide polymorphism). These differences are called genetic variations and may occur for many reasons. For example, they might have been transmitted from the parents, occurred as a result of replication errors, recombination, or factors like exposure to UV radiation.

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B. Microsatellites Microsatellites are sequences of repeated nucleotides mostly di-, tri- or tetra nucleotide repeats. They can also be called simple sequence repeats (SSRs), short tandem repeats (STRs), or variable number tandem repeats (VNTRs). Microsatellites are highly polymorphic. Many alleles may exist (up to 80) at a single microsatellite locus. The majorities are in non-coding regions. Microsatellite marker analysis has been the most successful tool for locating genes involved in single gene disorders, and they have also proved to be useful in studying complex diseases. Microsatellite markers close to the disease gene correlate with the disease and by analyzing these markers within families, it can be predicted how the disease will be inherited. Compared to using SNPs, it is easier to begin a gene hunt using microsatellite markers and then to switch to SNPs once the region has been located. In addition, because of its multiple alleles, one microsatellite usually provides more information than one SNP. It has also been proposed that a randomly chosen set of microsatellites shows greater information than a randomly chosen set of SNPs.(1) At the same time, there are a few limitations when using microsatellites to study complex diseases. Microsatellites do not occur as frequently as SNPs. The higher mutation rate of microsatellites will generally cause LD to decrease more rapidly. The speed of generating genotype data is slower with microsatellites compared to SNPs.

C. Single nucleotide polymorphisms (SNPs) Single base or multiple base differences may be due to insertions, deletions or substitutions. The insertion or deletion of either a single base or multiple bases can cause a frame shift in the gene which can lead to a different protein or a non-functional protein. The smallest possible change in DNA is an insertion, deletion or substitution of a single base and this is called a SNP. SNPs can be detected when the minor allele frequency (MAF) is at least 1%. Furthermore, SNPs can be categorized as being rare when the MAF is less than 5%, common when the MAF is greater than or equal to 5%, and frequent when the MAF is greater than or equal to 20%. In many cases, SNPs are difficult to detect because point mutations never reach an appreciable frequency and therefore become lost in the population (when less than 1%). They may also be hard to detect when a point mutation drifts to a higher MAF over many generations and becomes fixed at 100%.

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SNPs with higher MAF are considered to be ‘‘older’’ than SNPs with very low frequencies. SNPs are very frequent, on average they occur at least once every 400 to 600 base pairs. They are frequently used in association studies because many disease-susceptible genetic variants are likely to be SNPs and they are very common in the genome.(2) In addition, SNPs are less prone to mutation than other types of polymorphisms because of their stability. Although some SNPs affect gene function, most do not.(3,4) The effect depends on the area where they are located in the gene. SNPs can occur either in coding or non-coding regions. This does not mean that SNPs in the non-coding region cannot affect the function of the gene. Any SNP can affect gene function and cause a phenotypic change. It is possible that a SNP associated with a particular disease can be associated with many other diseases as well (e.g. association of PTPN22 with most autoimmune diseases). It is also possible that the association of a set of SNPs could be specific for a given disease (e.g. association of IRF5 SNPs to SLE).

D. Types of genetic diseases Genetic diseases can mainly be classified into three groups, which are Monogenic, polygenic (complex diseases) and mitochondrial.

a. Monogenic diseases These are also referred to as single gene disorders, where a mutation or number of mutations in same gene may cause the disease. These diseases segregate in a clear Mendelian pattern. Most of single gene (Mendelian) disorders have been identified. Monogenic diseases can be broadly classified into two groups, recessive and dominant. Recessive disease occurs due to damage in both copies of alleles. Examples include sickle cell anemia, cystic fibrosis, phenylketoniuria and Gaucher disease. Dominant disease occurs due to damage in only one gene copy such as in Huntington’s disease and retinoblastoma.

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b. Polygenic (Complex or Multifactorial) disease Polygenic diseases are disorders with a complex mode of inheritance, and are therefore referred to as complex diseases. In these diseases, there is no recognizable pattern of inheritance (Mendelian inheritance is not observed). The heritability is low when compared to single gene disorders. As the name suggests multiple genes may be involved. It has been hypothesized that each gene contributes to a relatively small individual risk for the disease. However, when a sufficient number of genes are inherited, an individual will become susceptible to the disease. This is known as the gene threshold liability hypothesis.(5) This hypothesis also states that certain sets of genes in different combinations are likely to influence the severity of the disease. These disorders are also called multifactorial diseases because many factors may play an important role in activating susceptibility genes such as environmental factors, viral infections and hormones. Examples of multifactorial diseases include heart disease, high blood pressure, Alzheimer’s disease, arthritis, SLE, diabetes, cancer, and obesity.

c. Mitochondrial disease Mitochondrial diseases are very rare and caused by mutations in the non-chromosomal DNA of mitochondria. Mitochondria are called the powerhouse of the cell and are found in the cytoplasm. A mitochondrial disease is an inherited condition that runs in families. Mitochondrial DNA (mtDNA) mutations are maternally inherited, since children inherit their mitochondrial DNA only from their mother, unlike nuclear DNA which comes from both the mother and father. Therefore we can also say, a child has the same mtDNA sequence as his or her siblings and mother, but not as his or her father. Defects in mitochondrial functions can affect many parts of the body including the brain. Mitochondrial defects have also been linked to many diseases like cancer, stroke, Parkinson’s disease, Leber heredity optic neuropathy (LHON), Myoclonic epilepsy and ragged red fiber disease (MERRF).

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E. Approaches for the identification of complex disease genes There are different ways to study whether genetic factors play a role in the susceptibility of a given disease. The most widely used in methods for identifying complex disease genes are called linkage and association.

a. Linkage Linkage determines if a particular region in the genome contains a gene related to the phenotype of interest. This can be done by measuring the segregation of a group of alleles and a phenotype within a family. The susceptible region might be somewhere in the large physical distance linked to the phenotype, and this may include many genes. Among the many genes found in the region, a candidate gene can be selected based on its functional relevance to the disease.

b. Association and Linkage Disequilibrium Once linkage studies have identified a large chromosomal region, carrying out a candidate gene approach association study can help narrow down the region to a disease-causing locus.(6) This does not mean that association studies should only follow linkage. It is also possible to use SNP-based genome wide association studies to find susceptible loci. Association measures preferential segregation of polymorphic marker alleles with a phenotype. The risk factor may be in close proximity to the associated region. Most tests of association evaluate the evidence of linkage disequilibrium (LD) between various polymorphisms to find the disease risk allele in the candidate gene. LD is an important tool for dissecting complex diseases. Alleles at neighboring loci tend to segregate together, and when linked alleles are associated it is called linkage disequilibrium. There are two methods by which the extent of LD can be determined: using D prime (D’) and the correlation coefficient (r2). D’ is scaled to have a minimum value of 0 and a maximum value of 1. Complete LD is when D’ is equal to 1 and this means that no recombination has occurred between the two loci in the past. In other words, all copies of the more rare allele occur exclusively with one of the possible alleles at the other marker. When D’ is low or zero, the two loci tend to be inherited in a nearly random manner (known as independent assortment).(7,8)

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D’ is very unstable for small sample sizes, therefore r2 is more widely used to measure LD. When r2 is equal to 1, it is referred to as perfect LD and the allelic frequencies at the two loci are the same. The measure r2 is equal to D2 divided by the product of the four allelic frequencies at the two loci {r2=D2/(PAPaPBPb)}.(9) Suppose there are two loci and at locus one ‘A’ is the frequency of the minor allele, while ‘a’ is the frequency of the common allele. Similarly, at locus two, let ‘B’ be the frequency of the minor allele and ‘b’ be the frequency of the common allele. D = P (AB) � P (A) P (B). D’= D/Dmax or D/Dmin If D > 0: Dmin = min (p (A) p (b), p (a) p (B)) If D < 0: Dmax = max (-p (A) p (B), -p (a) p (b)) We can say that the association observed between an allele and a phenotype can be due to many reasons. For example: It may be chance or an artifact (false positive). The allele may be in LD with an allele at another locus that is directly affecting the expression of the phenotype. The allele itself may be functional and is directly affecting the expression of the phenotype.(10) The two commonly used designs in association studies are the family-based design and population-based design (also called as the case control design).

1. Family-based designs Complex diseases tend to aggregate in families. There are many family-based designs used to detect association in these samples. Family designs have advantages over population-based designs as they are resistant to population admixture and stratification.(11) Family-based association studies have internal controls and are designed to avoid possible bias from inadequate controls. Some of the most popular and frequently used family-based association designs are described below.

i. Case-sibling design In the case-sibling design, unaffected siblings of the affected person are used as controls. For a disease with a large variation in age of onset, the selection of controls is problematic. For example, siblings used as controls should be at least the same age as the case. If not, then we cannot use them as controls. Therefore, this will limit the sample to older siblings only. In the case of complex diseases where environment plays a key role, siblings born and

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brought up under different environmental conditions cannot be used in the study because they have probably been exposed to different levels of environmental factors.(12)

ii. Twin study In twin studies concordance rate is compared between monozygotic and dizygotic twins. If the disease is completely genetic, the concordance of monozygotic twins would be 100% and the concordance of dizygotic twins would be 50%. If it is only partly genetic, the concordance in monozygotic twins would be less than 100% and less than 50% in dizygotic twins.

iii. TDT The Transmission-Disequilibrium Test (TDT) is a design originally proposed by Falk and Rubinstein(13) and it uses McNemar’s(14) chi-square test to determine significance. TDT is the simplest family-based design for testing association and it uses genotype data from trios, which consists of an affected offspring and his or her two parents.(15) In this design, parental disease status is neither used nor required for the analysis.(16) The main idea behind the TDT is to determine under the null hypothesis which marker alleles are transmitted more often to the affected offspring.(15)

This is done by comparing the frequency of alleles transmitted to affected offspring to the frequency of alleles not transmitted to the affected offspring. TDT discards all homozygous parents since homozygous parents are non-informative. Instead, it only considers transmissions from heterozygous parents to an affected offspring.(16) TDT shows increased power with multiple affected sibs. TDT under the null hypothesis, H0, shows no association, and when there is evidence of association the null hypothesis H0 is rejected.

iv. Family-based association test (FBAT) This is an extension of the original TDT and it has many advantages over TDT. The FBAT can be computed from the available data, meaning that it can handle missing parental genotypes as well as extended pedigrees.(17,18) In addition, FBAT is resistant to population admixture and population stratification. It can also be used to construct haplotypes.

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v. Advantages and Disadvantages Compared to the standard case-control approach, all the above family-based tests have many advantages. For instance, they are constructed in such a way that they decrease the risk for false positive results due to population admixture. Family-based tests are also resistant to population stratification since untransmitted alleles within the family are used as the control alleles while transmitted alleles are used as the case alleles. The risk for a false positive result is smaller because significant findings always imply both linkage and association. At the same time, there are a few disadvantages with family-based designs. One disadvantage is that it is sometimes difficult to obtain samples from the parents. For tests like the TDT, at least one parent is required to be heterozygous at the marker being tested otherwise they are non-informative and the whole family must be eliminated from the analysis. Additionally, it is more time-consuming and expensive to collect family-based samples, in particular for late-onset diseases since the parents of the affected might not be alive at the time of diagnosis. Because these kinds of eliminations must be made, the power of this approach is significantly lowered when compared with the case control approach. Family-based studies are expensive to perform because they require more genotyping compared with population-based studies.

2. Case control studies Case control design is also an important tool for studying complex diseases. The case control approach is very simple and the oldest method to detect association. Here, two sets of samples are used: one set of individuals with a particular disease (cases), and the other set of individuals without the disease (controls) which are selected randomly from the same population as the cases. The case control approach is used to test whether a particular variant allele is more common (significant) among the cases than the controls. The case control design shows increased power when more controls are used compared to cases, and for better results at least a 1:3 ratio (cases: controls) should be used. The case control approach has many advantages compared to the family-based approach. First, it is easy to collect the cases and controls. Another advantage is that the sample size required is small (100-500) compared to family-based designs which make the case control approach cheaper to perform.

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There are a few disadvantages for the case control design as well. For example, it is usually not too difficult to obtain suitable cases, but selecting controls tends to be more problematic since the controls should be selected from the same source population as the cases. Because disease risk often varies substantially with age, sex and ethnicity, the cases should be matched with appropriate controls. In some cases, population admixture and hidden population stratification can invalidate this approach and may lead to strong associations at markers that are unlinked to disease loci. Therefore, it is often best to work with a homogeneous population which is difficult to obtain.

F. Population stratification Population stratification may occur even when individuals come from the same population when the population is composed of unidentifiable subpopulations. False positive results are more likely to occur, as a result of the differences in genetic backgrounds. Acceptable methods for population stratification are case control and some family-based methods such as TRANSMIT. Population stratification can be avoided in case controls by a simple procedure which is to divide them into homogenous subgroups or strata and match independently within each stratum by their age, sex and ethnicity. When performing family-based association studies, it is better to use methods that are resistant to population stratification, e.g. FBAT.

G. Genetic heterogeneity There are two types of genetic heterogeneity, and they are referred to as locus and allelic heterogeneity. When mutations present in different genes result in the same phenotype, it is called locus heterogeneity. In contrast, allelic heterogeneity is when different mutations in the same gene cause the same phenotypic change.

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H. Epistasis The term ‘epistatic’ was first used in 1909 by Bateson to describe a masking effect.(19) Later, epistasis was described as the interaction between two or more genes to control a single phenotype. The interactions between different genes are of great importance in complex disease genetics because it is assumed that interacting genes might result in a certain disease phenotype.(19) Sometimes the terms epistasis and dominance are used synonymously, however. Epistasis is not the same thing as dominance. In epistasis, a mutation in one gene masks the expression of a different gene. On the other hand, dominance is when one allele of a gene masks the expression of another allele of the same gene.

I. Gene-gene and gene-environment interaction It has been suggested that gene-gene and gene-environment interactions may play an important role in the etiology of complex diseases.(20-26) The primary goal of gene-gene interaction is to evaluate the frequency of individuals with a particular disease having two or more gene variants at the same time.(20)

This can be tested using different statistical models with different study designs.

a. Case-only design In the case control design and case parent design, the interaction between two genes can be detected by conditional logistic regression.(27) According to W. James Gauderman, testing for gene-gene and gene-environment interactions with the case-only design is more efficient than the case-sibling design or the standard matched case-control design.(27) This is because the case-only design assumes that gene frequencies are independent in the population. The other major advantage of this model over other models is that no controls are used in the analysis, since there is always a chance that the controls may affect the interactions present in the cases. The standard Chi-square test of association is used to test gene-gene and gene-environment interactions.(26-30)

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J. Statistical methods

a. Hardy–Weinberg equilibrium (HWE) The first and most important test to be performed once the genotyping is done is Hardy–Weinberg equilibrium (HWE), which is named in honor of the two men who formulated it. A population that is in Hardy-Weinberg equilibrium does not show a substantial change in observed and expected allele frequencies. In addition, HWE can be used as a tool in detecting genotyping errors, and it is strongly recommend that a HWE test be routinely performed particularly when working with SNPs. p+q=1 q=1-p where p= 2xObs(AA)+Obs(Aa)/2x(Obs(AA)+Obs(Aa)+Obs(aa)) Exp(AA)=p2xn Exp(Aa)=2pqxn Exp(aa)=q2xn X2=�(Obs-Exp)2/Exp Where n=Obs(AA)+Obs(Aa)+ Obs(aa) It should be noted that deviations from HWE are not necessarily due to genotyping errors as they may be due to chance or other genetic factors such as small population size, gene flow, genetic drift, nonrandom mating and natural selection. Theoretically, the disease-free controls chosen randomly from populations should follow HWE, whereas HWE may be violated in the disease group with the presence of association.

b. Chi square test of association The Chi square (X2) test of association was derived by Karl Pearson, in the beginning of 20th century, and is often referred to as Pearson's Chi square test of association. The Chi square test of association allows us to evaluate associations (i.e. correlations) between variables. The null hypothesis, H0, assumes that there is no association between the variables (in other words, one variable does not significantly differ from the other variable). In contrast, the alternative hypothesis, Ha, claims that association does exist (there is a significant difference between one variable and the other variable).

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It is common practice to obtain a P-value from the Chi square value, and this P-value is a measure of how much evidence we have against the null hypothesis. The smaller the P-value, the more evidence we have against H0 and the stronger the evidence is for a significant association.

c. Confidence intervals It is good to support a P-value with a confidence interval because this gives us some idea about the range that probably covers the true value. Confidence intervals are more informative than other simple tests because it is easy to decide whether H0 should be rejected or not. The confidence level is the probability value (1-�) associated with a confidence interval. It is often expressed as a percentage. For example, when � is equal to 0.05 (5%), then the confidence level is 95% (1-0.05=0.95). In a 95% confidence interval, one can be 95% sure that the true value lies somewhere within that interval. If the confidence interval includes zero, then this indicates that there is no significant difference between the means of the two populations at a given level of confidence. The width of the confidence interval gives us an idea of how uncertain we are about the difference between the means. A very wide interval may indicate that more data should be collected before anything can be confidently asserted.

d. Odds ratio The odds ratio (OR) is sometimes also called the relative odds. It is frequently used in case control studies. The odds ratio is a way of comparing whether the probability of a certain event is the same for two groups. An odds ratio of 1 implies that the event is equally likely in both groups. An odds ratio greater than one implies that the event is more likely in the first group. An odds ratio less than one imply that the event is less likely in the first group.

e. Meta-analysis Meta-analysis is used to investigate the combined effect of a group of independent studies,(31) for example a series of similar studies conducted at different centers.

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f. Mantel-Haenszel test: a test for stratified samples The Mantel-Haenszel test is a summary Chi-square test developed by Mantel and Haenszel for stratified data and is used to control for confounding results. A separate 2-by-2 table is formed for each stratum (e.g. population) when response rates vary among strata, and hypotheses about the overall odds ratio can then be tested using the Mantel-Haenszel test. To perform the Mantel-Haenszel test, there should be a linear association between X and Y (cases and control) in at least one stratum. Though the other strata are not associated, they should at least follow the same direction. Then, the Mantel-Haenszel test can be used to measure the strength of association by estimating the common odds ratio. Two models can be used to combine the individual effects, a fixed-effects model or a random-effects model. The random-effects model can be used when there is heterogeneity between populations. In other words, when there may be populations for which there is no effect and other populations for which there is a substantial effect. A fixed-effects model is used when all the populations have a substantial effect (homogeneity). Increasing the number of studies contributing to the analysis will result in an increase in power because additional studies will result in a narrower confidence interval.

g. Logistic regression Logistic regression models can be applied to both binary data and proportions but it is most frequently used for dichotomous (binary) outcomes(32,33) usually in the form of presence or absence, (yes or no).(34) Logistic regression is widely used in medical and social science studies, and it can also be used to explore whether two susceptibility loci jointly influence the risk of developing a disease.

K. Functional studies Once the association has been confirmed using case control and family-based methods, one should look at the functional aspects of the SNPs, which

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may affect transcription (introduction of a frame shift), translation (initiation, termination, RNA processing, RNA stability and alternative splicing) and protein synthesis (premature truncated peptides, overexpression or decreased expression). The altered function depends mostly upon the area where the mutation is located. Mutations in the 5' UTR are thought to play a role in controlling mRNA translation, while mutations in the 3' UTR control RNA cleavage, stability, export and subcellular localization. It is also known that mutations in promoter motifs usually lead to reduced mRNA levels. These variants may also affect the transcription factor binding sites. Here, I would like to focus more on a few of the topics that we have touched on like RNA stability, alternative splicing, gene expression and transcription factor binding sites. Most of these activities are linked to each other, e.g. RNA stability and expression are directly proportional to each other.

a. RNA stability The balance between mRNA degradation and synthesis determines the level of individual mRNAs within cells. mRNAs vary in stability and play an important role in regulating gene expression. The stability of a given mRNA transcript is determined by the presence of specific sequences (exonic splicing enhancers, intronic splicing enhancers, exonic splicing silencers, and intronic splicing silencers) within an mRNA known as cis-elements, which can be bound by trans-acting RNA-binding proteins to inhibit or enhance mRNA decay.(35,36)

The 5' caps and 3' poly (A) tails also contribute to mRNA stability by protecting it from degradation by exonucleases. The presence of AU-rich motifs in mRNAs also tends to destabilize mRNA stability through the action of cellular proteins that bind these motifs. In some instances, an mRNA can be edited which changes the nucleotide composition of that mRNA. This editing may create an early stop codon, producing a shorter and more stable transcript. Finally, one can say that the more stable an mRNA is, the more protein may be produced from that transcript.

b. Alternative splicing When the pre-mRNA has been transcribed from the DNA, it includes several introns and exons. At this stage, it is not known which introns and exons will eventually be included or excluded in the mRNA. This decision is made during the splicing process. Alternative splicing enables a single gene to generate multiple protein isoforms, which may be structurally and functionally distinct from each

25

other. Alternatively spliced mRNAs are most commonly produced by including or excluding (skipping) individual exons. In other words, exons in the primary gene transcript, the pre-mRNA, are arranged in alternative ways. Pre-mRNAs that have been transcribed from the same gene can be separated and reconnected in different ways to produce various mRNAs. These mRNAs are then translated into proteins, which may differ from one another in their function. The mRNA splicing variants are mostly found at the donor or acceptor splice sites. The selection of splice sites is regulated by serine/arginine-residue proteins (SR proteins). There are a few known ways in which alternative splicing can occur, and it may be due to alternative promoters, alternative stop codons, polyadenylation sites, retaining an intron, exon skipping, etc. The above mentioned alternative splicing mechanisms may be influenced by a number of other factors such as intron length, exon length, splice site strength and pre-mRNA secondary structure, etc.(35-37)

c. Gene expression Gene expression studies can be used to study the multiple transcripts expressed as a result of alternative splicing. They can also be used to study mRNA stability on most stable transcripts. Real time PCR is one method that can be used to study gene expression. RNA is first isolated from the samples to be studied, which is then converted to cDNA. Based on the region of interest, special primers and probes are designed and used for amplication. In short, real-time PCR amplifies a specific target sequence in a sample and monitors the amplification progress using fluorescent technology. Amplification curves are graphed to determine the “cycle time” (CT) at which fluorescence reaches a threshold level. This CT value is inversely proportional to the amount of specific nucleic acid sequence in the original sample. In a perfectly efficient PCR, the amount of amplified product doubles after each cycle. Therefore, an increase of one CT means that the sample had double the amount of target sequence.

d. Transcription factor binding sites The transcription of a gene is influenced by special binding proteins known as transcription factors. They bind to short segments of DNA known as transcription factor-binding sites which are found in the regulatory regions of genes.

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How are these binding sites found? If a binding site for a certain transcription factor is already known, then it is quite easy to locate additional binding sites in silico by scanning the DNA for segments resembling the known binding sites.

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II. Autoimmunity in general

The principal function of the immune system is to eliminate foreign antigens by mediating an immune response. Disorders can occur when the immune system is less active (immunodeficiency) or overactive (autoimmune response). An individual is susceptible to bacterial, viral or parasitic infections when the immune system is less active. More damage can occur when the immune system is overactive, as it results of autoimmunity. What is autoimmunity? In order to function properly the immune system must be able to distinguish what is self and non-self (foreign). When the immune system fails to recognize its own constituent parts as "self", the result is an immune response against its own cells and tissues. In other words, autoimmunity results when self-tolerance fails to occur. Self tolerance is a mechanism which minimizes the risk of harm to the body from an overactive immune system.

A. Immunological aspects The immune system plays a major role in the onset and development of autoimmune diseases since defects in the immune system probably lead the body to attack its own organs, tissues and cells. There are many studies that have explored different immunological aspects, like the role of B cells, T cells and inhibitory molecules in the initiation of autoimmune disease. Self-reactive B and T lymphocytes are eliminated during their development in the bone marrow or thymus by negative selection, and this process is called central tolerance. However, a few self-reactive B and T cells escape into the periphery. Peripheral tolerance is a process by which these autoreactive cells are controlled so that no damage is done to its own cells and tissues in the body.(38)

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Most of the damage that is caused in autoimmune diseases occurs as a result of the production of autoantibodies. There are multiple reasons for the overproduction of autoantibodies including dysfunctional kinases, prolonged exposure to an autoantigen, signals from cytokines such as IL-2, IL-4 and IL-6, as well as other triggering factors such as environment, viruses, bacteria and certain types of medicines. Normally, B lymphocytes have thousands of receptors on their surface with binding sites for different antigens. Only one in 1 million B lymphocytes happen to produce receptors capable of binding to a particular foreign antigen. To fight an infection caused by a foreign antigen, this number would be too small. Therefore, if a foreign antigen is encountered the B cells bind the specific antigen and engulf it via receptor-mediated endocytosis. B cells can also act as an antigen presenting cell (APC) by further processing the antigen and presenting it to T cells with the help of MHC. This is called primary signaling and this binding initiates T cell activation. Several autoimmune diseases are genetically linked to the MHC (HLA complex in humans). The contact between B and T cells initiates interactions between different ligands and receptors on the surfaces of the two cells. Some of these ligand-receptor interactions act as co-stimulatory signals and initiate the production of cytokines that induce B cell proliferation and differentiation to generate plasma cells (which produce antibodies for long periods of time) and memory cells (which produce antibodies in a secondary immune response).

Figure 1. B cell activation occurring after presentation of foreign antigen via MHC to T cell receptor.

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Figure 2. B cell differentiation and proliferation that results in the generation of plasma and memory cells.

Other ligands and receptors, like PDCD1 and CTLA4, initiate inhibitory signals that control B cell proliferation and the production of immunoglobulins. Under abnormal conditions, when a cell is damaged beyond repair, for example by a virus infection, it undergoes apoptosis. During this process, both damaged cell fragments and viral components are released into the circulation.

Figure 3. Viral infection leading to destruction of the cell and the release of apoptotic cell fragments into the circulation.

When apoptotic fragments are present in the circulatory system for a long time without being cleared, one’s own apoptotic cell fragments may also be

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bound to B cell (self reactive lymphocytes that escaped into periphery) receptors and internalized. Like macrophages and dendritic cells, B cells also function as antigen presenting cells (APCs) and may present the self-antigens through MHC to T cells as foreign bodies. As a result, the B cell in turn receives signals from the activated T cell which induces autoantibodies.

Figure 4. B cell activation occurring after presentation of self-antigen via MHC to T cell receptor.

Under such conditions, if inhibitory molecules like PDCD1 and CTLA4 are malfunctioning, it may result in the overproduction of autoantibodies. When autoantibodies in turn bind to antigens in the circulatory system, this results in the generation of immune complexes (ICs). The deposition of ICs in different parts of the body causes different disease manifestations, for example they can affect joints in the case of RA and kidneys in the case of SLE. A common feature of autoimmune diseases is that they have genetic predisposition.(39) In support of this, increased concordance is observed in monozygotic twins compared to dizygotic twins and increased frequency is observed in families with more than one affected individual.(40-42) Autoimmune diseases are complex, polygenic and involve environmental factors. The abnormal immune response probably depends upon interactions between susceptibility genes and various environmental factors. For example in SLE, viruses (i.e. EBV) may act as triggering agents in initiating autoimmune responses. Most autoimmune diseases tend to affect women. For example in thyroiditis, scleroderma, lupus and Sjögren’s syndrome, more than 85% of the patients

31

are females. Of course, there are a few diseases such as type 1 diabetes that affect both men and women equally and even a few diseases that occur more frequently in men like ankylosing spondylitis. The exact reason for gender-based differences are not known, but probably the production of sex hormones play a major role (e.g. Androgens and estrogens). Estrogens can stimulate B cell growth, antibody production, and cytokine release, thereby increasing the susceptibility to autoimmune diseases.(43,44,45) There are also some reports that show that autoimmune disease rates vary among racial groups. Many autoimmune diseases share common susceptibility genes (e.g PTPN22), although a few are specific for a certain disease. For instance, the association of IRF5 seems to be specific for SLE. Due to overlapping genetic traits, a patient may suffer from more than one autoimmune disease or multiple autoimmune diseases may occur in the same family.

B. Clinical aspects Most autoimmune diseases are not curable and patients must deal with their illness and different treatments throughout their lifetime. Autoimmune diseases can affect any part of the body and have many clinical manifestations. Until a few years back, it was difficult to clinically identify the people at risk but recently there have been major improvements in the field. For example, people can be tested for lupus-related autoantibodies in serum which are often present years before the patient begins to display disease symptoms. In the case of RA, high concentrations of anti-CCP antibodies, T cells and rheumatoid factors in the blood were found to be an indicative factor of rapid disease progression. Of course, a negative RF does not rule out RA.

C. IFN Signature In most patients, a trend has been observed. Patients with severe lupus consistently showed higher expression of IFN alpha and interferon inducible genes, and this has become known as an IFN signature. Most of the genes linked to lupus are activated by interferons and are collectively referred to as the IFN signature genes.(46) Later, this signature was observed in other autoimmune diseases as well including psoriasis and RA.(47,48)

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III. Autoimmune diseases

Systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and psoriasis are a few autoimmune diseases that we have studied. Among these, we have mainly focused on SLE. However, I would like to give a brief description of RA and psoriasis before going into detail about SLE.

A. Rheumatoid arthritis (RA) Rheumatoid arthritis is a complex disease. The exact cause of RA is unknown, but it is thought to be triggered and initiated by infections with viruses and bacteria. The result is that the body’s immune system attacks healthy joints. This causes inflammation in the lining of the joints. It can also affect other parts of the body such as the eyes, lungs and heart. This inflammation can lead to pain and permanent damage if the disease is not treated or controlled. RA affects women three times more frequently than men. Most people develop RA between the ages of 25 and 50. Many patients with RA have other family members that are affected as well. There are several genes that predispose individuals to the development of the disease, and each gene is thought to contribute a small amount to the overall risk. The results are conflicting in the case of RA, and it is difficult to be certain whether some genes are really involved or not. One group that is definitely involved in the disease is a group of genes that determines the human leukocyte antigens (HLA). Other groups of genes that have been reported to be associated with RA and increase the risk for disease include CTLA4, PADI4, PTPN22, and PDCD1, as well as some genes that encode cytokines and their receptors. Tumor necrosis factor, interleukin-1 and -10 are examples of cytokines whose genes have been reported in some studies to be of importance in increasing the risk for RA.

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B. Psoriasis Psoriasis is a chronic inflammatory skin disease. People often experience flares and remissions throughout their entire life. There is no cure, and psoriasis occurs equally in both males and females. The disease occurs more frequently within families, and approximately one-third of people who develop psoriasis have at least one family member with the condition. Recent studies show that ethnicity might play a major role. It seems that psoriasis is most common in Caucasians, slightly less common in African Americans and far less common among Asians. The immune system is affected by improperly functioning T cells. Normally, T cells protect the body from infection and disease. In the case of psoriasis, T cells are put into action by a false signal. They become so active that they induce other immune responses which leads to excessive growth and reproduction of skin cells. People with psoriasis may notice that the skin may sometimes gets better and sometimes worsen. In addition to genetic factors, there may be many other factors that cause the skin to become worse. These factors may include environmental factors like infection, stress, certain medications, or changes in weather that cause dry skin. There is a multitude of evidence showing that changes in cytokine production, particularly IL-6 and TGF-�, may play an important role in the propagation of the inflammatory response in psoriasis.(49) In addition, polymorphisms in many other cytokines have associated with psoriasis. For example, IL-2, IL-4, IL-10, IL12B, IL23R and TNF have been associated although these associations varied depending on population. Our reason for studying psoriasis is because of the possible involvement of IFN alpha in the pathogenesis of the disease, since it has been reported that PDC-derived interferon-� is a master cytokine in psoriasis development.(50-

52)

C. Systemic lupus erythematosus: An overview

a. History of Lupus Hippocrates wrote about red ulcerating skin lesions somewhere around 400 B.C, but the term lupus (meaning wolf in Latin) was coined by the thirteenth century physician Rogerius who used it to describe the classical malar rash, which ate away and destroyed facial tissue.

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In the late 19th century, Kaposi, a Viennese physician, described systemic lupus erythematosus and was the first to describe the butterfly rash on the face of Lupus patients. At the same time, Sir William Osler described heart, lung, joint, brain, kidney and stomach symptoms. He also recognized that some cases of SLE occur without skin involvement.(53) The modern period of SLE began in the mid-20th century with the discovery of the LE cell by Hargraves and colleagues. Nowadays, the use of LE cells in the diagnosis of the disease has been largely abandoned.

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IV. Different types of lupus

A. Systemic lupus erythematosus (SLE) The word "systemic" means the disease can affect any organ system in the body such as heart, lungs, joints, kidneys, nervous system and skin.

B. Discoid lupus erythematosus This is when the disease is confined to the skin where a red, raised rash appears on the face, scalp, etc. The raised areas may become thick and scaly, and may cause scarring. The rash may last for days to years. Only a few people with discoid lupus have SLE or develop it later on.

C. Drug-induced lupus Sometimes medication can cause symptoms very similar to SLE. Many different drugs can cause drug-induced lupus. The symptoms are similar to those for SLE, for example arthritis, rash, fever, and chest pain. Furthermore, these symptoms may go away when the patient stops taking the medications that caused it.

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D. Neonatal lupus Most infants of mothers with SLE are entirely healthy, but on rare occasions the disease occurs in newborn babies too. Sometimes it can occur in babies even when the mothers do not have the disease. The newborn babies often have a skin rash, liver problems, and low blood counts. These symptoms gradually go away after several months. In rare cases, the babies with neonatal lupus may have a serious heart problem that slows down the natural rhythm of the heart.

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V. Characteristics of SLE

SLE is an autoimmune disease in which the body's immune system overreacts and produces antibodies against the body's own cells and tissues. Autoantibodies contribute to inflammation and damage to various organs and tissues. The exact reason for the abnormal immune reactivity causing lupus is not known. It is possible that genetic factors in combination with environmental factors are involved for example inherited genes, mutations, viruses and ultraviolet light. Therefore, it is suspected that environmental triggers act upon genetically susceptible individuals to create T- and B-cell defects that result in increased autoantibody production.(54) Several different autoantibodies are detected in patients with SLE. They are anti-nuclear antibodies (ANA) with many subtypes for instance anti-dsDNA, anti-ssDNA, anti-ribonuclear protein, anti-SSA (Ro), anti-SSB (La), and anti-histone antibodies as well as other types like anti-cardiolipin and anti-ribosomal P antibodies. Out of the above mentioned autoantibodies, anti-nuclear antibodies (ANA) are the most common type of autoantibodies in SLE patients. These are found in more than 95% of SLE patients.(55) ANA are directed against parts of a cell's nucleus thereby generating circulating immune complexes. Immune complexes cause most of the damage that occurs in SLE, since inflammation and tissue damage occurs wherever these immune complexes are deposited. It has been postulated that the nature of these immune complexes and where they are deposited is also determined by a number of genetic and environmental factors.

A. Epidemiology SLE affects approximately 40 to 50 individuals per 100,000,(56) although the prevalence varies with sex,(57) ethnicity,(58) and socioeconomic status.(59) It is

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mostly women who are affected, and it is about 8 to 10 times more common in women compared to men. Though women of all ages can be affected, it occurs more often around the ages of 18 to 45, and after 45 the prevalence decreases. It is also known that SLE is most common in women of childbearing age and some women with SLE experience worsening of their symptoms prior to their menstrual periods. This evidence clearly suggests that female hormones probably play an important role in the expression of SLE, although the role of these hormones in disease expression has yet to be proven experimentally.

B. Symptoms of SLE Not all patients with SLE have the same symptoms, and there are probably no two SLE patients with exactly the same symptoms since each person may have slightly different symptoms. The symptoms may range from mild to severe which may depend upon both genetic and environmental factors. It has also been noticed that in some patients the symptoms may come and go over time. Some SLE symptoms affect the body as a whole, while other symptoms target a specific organ. Examples of whole body symptoms are fatigue, weight loss, fever, etc. Organ-related symptoms include joint pain, skin rash, kidney problems, cardiovascular problems, etc. Virtually all organ systems can be affected.

C. Criteria for diagnosis Historically, it has been quite hard for doctors to diagnose this complex disease accurately, particularly when people have mild symptoms. To help distinguish lupus from other diseases, the American College of Rheumatology (previously American Rheumatism Association) initially published 11 classification criteria in 1971,(60) which were first revised in 1982(61) and later in 1997.(62) The diagnosis of SLE is made if four or more of the 11 manifestations are present, either one after the other or at the same time. Peter H. Schur classified patients based on the number of criteria observed as follows: * Possible SLE — two criteria * Probable SLE — three criteria * Definite SLE — four or more criteria * Classical SLE — many criteria

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Figure 5. An overview of the 11 criteria used for classification of SLE.

SLE is now correctly diagnosed in more than 95% of the cases. If the ANA test is negative, one can almost certainly rule out SLE. If the ANA is positive, the rest of the ACR criteria and other clinical findings need to be applied to differentiate SLE from other diseases. Very rarely a patient may be diagnosed with ANA-negative SLE. In some cases, a patient may not fulfill four of the criteria but may still appear to have SLE. These patients are often diagnosed as having a “lupus-like syndrome.”

D. Lupus treatment and management Although there is no permanent cure for lupus, there are a few supportive drugs that can be used. For example, NSAIDs, antimalarials, and corticosteroids. Most of these drugs have some side effects like swelling, increased appetite and weight gain, but most of these side effects generally stop when the drug is stopped. Some drugs like corticosteroids have long-term effects as well. Let us hope in the near future that there will be a permanent cure for lupus.

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The disease can be managed to some extent, when proper care is taken. The patients can perform their normal daily routine by avoiding direct exposure to sunlight, getting sufficient sleep, having a healthy diet, avoiding stress, and exercising regularly.

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VI. Genetics of SLE

Several susceptibility genes have been identified. Some genes have been reported to play a role in SLE susceptibility in multiple studies, though this is not always the case. In fact, even within ethnic groups the results have been discordant. So far only a few genes have shown consistent association with SLE. A brief description of these genes is found in the following passages.

A. HLA The HLA system plays a major role in the normal functioning of the immune system. Hundreds of associations between HLA alleles and autoimmune disorders, as well as other diseases, have been consistently observed over the last three decades.(63) However, this region is complex due to the relatively large genomic distances that are in linkage disequilibrium, as well as the multiple alleles at multiple loci that appear to be associated with many autoimmune diseases. For example, HLA-DR2 and -DR3 appeared to have the strongest and most consistent associations with SLE.(64,65)

B. Complement system The complement system functions in the clearance of pathogens from an organism. It is derived from many small plasma proteins that work together to induce cytolysis by disrupting the target cell's plasma membrane. Defective or absent components of the complement system can affect solubilisation and cause improper clearance of immune complexes. There are several reports that have proposed the association between complement component deficiencies and autoimmune disease. As a result of a defective complement system, excess immune complexes may deposit in tissues resulting in inflammatory damage. This may initiate an autoimmune response, which is a characteristic feature of SLE.

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Individuals deficient in C1q carry the highest risk for SLE. Of those individuals who are C1q deficient, more than 92% are affected with SLE. Both complete and partial C1q deficiencies have been reported among SLE patients.(66-68) It has also been reported that deficiencies in early complement components such as C2, C4 and C1q predisposes an individual to the development of systemic lupus erythematosus.(69-72)

C. Fc� receptors There are different types of Fc receptors, and they are classified according to the type of antibody they recognize. Those recognizing the most common class of antibody, IgG, are called Fc-gamma receptors (Fc�R). The Fc gamma receptors are located on chromosome 1 and map to region 1q21-24. There are three subgroups of Fc� receptors: the Fc�R1 (CD64), Fc�R2 (CD32), and Fc�R3 (CD16). Variants in these receptors have been found to be associated with many complex diseases such as arthritis and serositis.(73)

It has been suspected that these receptors contribute to the susceptibility of SLE, as one of the main functions of these Fc�Rs is clearance of immune complexes. It has also been showed that Fc�Rs carry ICs from the cell surface to endosomal compartments and assist in the stimulation of TLR7/8/9.(74,75) These TLRs are, in turn, required for a more effective activation of IFN� production, which plays a major role in SLE susceptibility.(76) There are many reported studies that confirm the role of Fc�Rs in the susceptibility of SLE.(77,78) It has also been reported that missense mutations of Fc�RIIA and Fc�RIIIA are associated with lupus.(79)

D. PDCD1 The PDCD1 protein acts as a co-inhibitor of T-cell activation and it has functional similarities to CTLA-4. PDCD1 is expressed on activated peripheral T and B lymphocytes.(80,81) It was reported that mice deficient in PDCD1 developed lupus-like conditions.(82) These observations suggested that PD-1 plays an important role in regulating peripheral tolerance and autoimmunity.(82,83) Furthermore, a former member of our group, Ludmila Prokunina, reported that a SNP (PD1.3 G/A) in the PDCD1 gene is associated with susceptibility to human SLE. This association was shown using Swedish, European American and Mexican families as well as sporadic cases.(84)

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When further tested for functional importance, this polymorphism (PD1.3A) was found to disrupt the binding of the runt-related transcription factor 1 (RUNX1), suggesting a possible mechanism through which it may contribute to the development of SLE in humans.(84) The association of PDCD1 seemed to show population-specific effects, since no association with the disease was observed in a few population and subpopulations. However, stratifying the data according to different manifestations showed association. For example, a set of patients and controls with renal disorder from northern Sweden showed a strong association.(85) It was recently reported by C.M. Thorburn et. al.(86) that PDCD1 genetic risk variants influencing the expression of SLE may vary according to ethnic background. They evaluated the roles of five SNPs within PDCD1. When haplotype analysis was performed, the data revealed that it might not be PD 1.3A itself, but something in or around the gene, that confers the risk for disease. Recently, a Swedish set of patients and controls was tested to see if PDCD1 interacts with another gene (PTPN22) associated with SLE to elevate disease symptoms. It was found that PDCD1 and PTPN22 are independent of each other in contributing to the disease risk.(87)

E. PTPN22 The protein tyrosine phosphatase 22 gene (PTPN22) encodes the lymphoid tyrosine phosphatase (LYP). LYP is involved in inhibition of T-cell activation. It was believed until recently that the PTPN22 risk-associated variant containing tryptophan was a loss of function mutation. However, it has now been suggested that the PTPN22 risk variant might result in a gain of PTPN22 phosphatase activity in T cells(88) though it is not known exactly how this gain in phosphatase activity contributes to inhibition of T-cell activation. The R620W variant of the PTPN22 gene has been found to be associated with many autoimmune diseases. First, association was found with TID(89) and subsequently in RA(90) and SLE.(91) This association was also replicated in a set of Swedish SLE patients and controls.(87) Additionally, there have been many other studies showing the association of PTPN22 to SLE.(87,92,93)

The results in lupus have varied with some showing association and others not. That is ethnicity seems to play a major role in the PTPN22 genetic

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association, as the risk variant of PTPN22 is mostly present in European individuals but is not found in the Asian and African populations.(94)

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VII. The interferon system in SLE

Interferons are small proteins released by macrophages, lymphocytes and other cells in virus infected tissue. There are three main types of interferon systems: IFN-� and -� belong to the type 1 system, IFN-� belongs to the type 2 system and IFN-� belongs to the type 3 system.(95,96,97) If we try to understand the pathogenesis of SLE with the data available so far, it is clearly observed that type I interferons and other genes involved in these signaling pathways are upregulated and strongly associated with the disease.(46,98) Also, there have been recent reports supporting the role of type 1 IFNs in SLE murine models. NZB knockout mice lacking the IFNAR1 gene had reduced hemolytic anemia, glomerulonephritis and increased survival compared with wild type mice. In another study giving inducers of type 1 IFNs to lpr mice, immune complex glomerulonephritis was worsened and lead to higher titers of autoantibodies.(99,100)

A. IFN-� Among type I IFNs, interferon-� is proven to be one of the chief cytokines involved in systemic lupus erythematosus (SLE). IFN-� is produced by cells like monocytes, macrophages and dendritic cells. The production of IFN-� stimulates a strong immune response. Both exogenous and endogenous inducers can activate IFN-� production. Some examples of exogenous inducers are viruses, bacteria, protozoa and some parasites. Endogenous inducers include nucleic acids like DNA and RNA.(101-103) The role of IFN-� in the development of lupus was suggested at least 2 to 3 decades ago.(104) It was observed that there is an elevated level of IFN-� in SLE patient sera and that it correlated with disease activity and severity.(105-108) In addition, some of the genes found to be up- or downregulated by IFN-� also correlated with disease activity. It has also been reported frequently that recombinant IFN-� could induce SLE or symptoms of lupus when used to treat viral diseases such as hepatitis and tumors with uncontrolled growth.(109-111) IFN-� was also found to induce thyroiditis, psoriasis and arthritis.(112,113)

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According to Dr. Virginia Pascual, approximately 95% of pediatric SLE patients have evidence of elevated levels of IFN-� in their blood cells. Furthermore, recent microarray and real-time polymerase chain reaction (PCR) analyses of peripheral blood mononuclear cells (PBMCs) from lupus patients have demonstrated increased expression of a number of IFN-inducible genes.(46,114-121) Recent evidence clearly supports the role of plasmacytoid dendritic cells (pDCs), ICs, Fc gamma receptors, toll-like receptors (TLRs) and type I interferons (IFN�/�) in the pathogenesis of SLE. One can ask, what is the driving force causing the elevated levels of type 1 IFNs (especially IFN-�) and IFN-inducible genes in SLE patients? It is suspected that along with genetic factors, exogenous inducers such as viruses (e.g. EBV) may play a major role in the induction of IFN-�. Therefore, when people who are genetically susceptible to SLE are exposed to viral infections, their cells and tissues may be damaged beyond repair and this may lead to apoptosis. It is also known that SLE patients have been shown to exhibit increased levels of apoptosis and decreased clearance of apoptotic cell fragments such as DNA, RNA, ribonucleoproteins, cardiolipins, ribosomal proteins, etc. These apoptotic cell fragments may also contain viral fragments. Moreover, when these apoptotic cell fragments are not cleared and stay in the circulation for a long time, they can drive autoantibody production and lead to the generation of ICs. Normally, ICs are transported by erythrocytes to the liver and spleen where they are removed. However, when ICs are not efficiently cleared from the circulation they can cause inflammation and tissue damage wherever they are deposited. This could happen if ICs are formed as a result of autoantibodies.

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Figure 6. IFN-� is produced by ICs that bind to Fc� receptors.

It is known that these immune complexes containing either DNA or RNA (118–122) play a major role in the induction of type 1 IFNs. Additionally, it has been reported that deposited ICs may result in elevated levels of type I IFN production through the activation of Fc� receptors. Fc� receptors trigger TLR7/8 and TLR9 which in turn activates endogenous IRF3, IRF5, and IRF7 through phosphorylation. Activated IRFs translocate to the nucleus to stimulate the transcription of type I IFNs. Depending on its binding partners, IRF5 may function either to inhibit or stimulate expression of type I IFN genes. IRF5/IRF3 heterodimers and IRF5/IRF5 homodimers activate, whereas IRF5/IRF7 heterodimers repress transcription of type I IFN genes (IFN�/�).(122)

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Figure 7. IFN-inducible genes are produced through JAK/STAT signaling which is activated by IFN-alpha.

Therefore, the production of type I IFNs further activates the production of IFN-inducible genes through the JAK/STAT pathway. Type I IFNs bind to the common receptor complexes, IFNAR1 and IFNAR2, and initiate a JAK/ STAT signaling cascade leading to the transcription of specific IFN-inducible genes.(123) This JAK-STAT pathway, which leads to increased expression of genes involved in IFN signaling, is also called an interferon-inducible gene expression signature.(46)

B. IRF5 The IRF5 gene is located on chromosome 7q32. It is a novel mediator of cell cycle arrest and cell death.(124) It was recently reported that IRF5 plays an important role in apoptosis as well. IRF5 is expressed primarily in lymphocytes and dendritic cells.(125) Currently, known inducers and activators of IRF5 transcription include type I and type II IFNs and virus.

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IRF5 upregulates proinflammatory cytokines such as IL-10, IL-6, IL-12 and TNF. Supporting this, IRF5 knockout mice exhibited reduced production of pro-inflammatory cytokines including IL6, IL12 and TNF-alpha.(126) IRF5 is also essential for the induction of other cytokines involved in apoptosis and early immune responses.(127) Recent data also indicate IRF1 and IRF5 have essential roles in both DNA damage and apoptosis.(128-129) The upregulation of these molecules may therefore have important functional implications in the pathogenesis of SLE(124) by inducing apoptosis caused by triggers such as EBV. A 100k genome-wide SNP scan was performed in two sets of patients and controls. Several SNPs showed relatively strong association, among them were SNPs lying between the IRF5 gene and TNPO3 on chromosome 7q32. Just as we were about to publish this, Sigurdsson et. al.(127) identified some polymorphisms in the gene IRF5 associated with SLE, though neither haplotype analysis nor the functional roles of the associated variants were explored. We therefore decided to re-examine this association closer. We typed the SNP rs2004640 (G<T) which had the strongest association in the report by Sigurdsson et. al. and typed a few other new SNPs as well.(Paper 1) Later, haplotype analysis was performed and we identified a common haplotype in the IRF5 gene that was associated with SLE and overexpression of the gene. This was tested using four sets of SLE cases and controls from Spain, Sweden, Argentina and the United States (a total of 1661 cases and 2508 controls), as well as a USA-based TDT analysis. This was the strongest association ever detected in the history of SLE. We also tried to elucidate the mechanisms by which these associated genetic variants actually lead to disease susceptibility. The splicing of IRF5 is known to be highly complex. Previously, nine isoforms were identified and we identified two isoforms during the course of our study. Transcription of various IRF5 isoforms is initiated at one of the three promoters, giving rise to transcripts starting either with exon 1A, exon 1B or exon 1C. The T allele of rs2004640 is located 2 bp downstream of the intron–exon border of exon 1B, creating a consensus GT donor splice site. Exon 1B transcripts were only present in individuals having T alleles. This led us to hypothesize that rs2004640 may be a strong genetic risk factor and probably plays a major role in disease susceptibility by influencing exon 1B splicing.

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In contrast, results from our recent paper (Paper 2) suggest that the translation efficiency of the isoforms containing exon 1B are low compared to isoforms containing exon 1A. This has led us to pose the question of whether it really is exon 1B or perhaps something close by, in LD with rs2004640, that is the real susceptibility factor. We found another SNP 5kb downstream of IRF5, rs2280714, in which the T allele correlated with mRNA overexpression. In paper 2, we found a new SNP, rs10954213, that was in LD with rs2280714 and better correlated with IRF5 expression. SNP rs10954213 also gave us a better explanation for IRF5 overexpression. SNP rs10954213 is located in the 3’-UTR region and the substitution of G for A was thought to disrupt a poly A signal sequence and lead to the expression of a longer and more unstable mRNA. In contrast, the presence of the risk allele (A) produces a shorter and relatively stable mRNA, and this may lead to higher levels of mRNA. In addition, it has been found that AU-rich elements (AREs) confer instability to many mammalian mRNAs and two strong AREs were found in the long 3’ UTR. This was also proved experimentally by Graham et. al.(130) and C. Kyogoku et. al.(131)

Figure 8. The role of the A allele (rs10954213) in the generation of shorter and more stable transcripts.

In between the 5’ SNP rs2004640 and the 3’ SNP rs10954213, we found a SNP called rs2070197 that was very strongly associated but did not seem to have a functional role. However, when haplotype analysis was performed with all three SNPs, a clear and well-defined haplotype was formed (TCA).

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Additionally, we found an indel in the PEST domain of exon 6, and although it was not associated by itself, the insertion was present in the risk haplotype (TICA). Interestingly, the presence or absence of the insertion determined which isoforms were expressed. Specifically, individuals with 4 repeats (insertion) expressed isoforms V5 and V6, while individuals with 2 repeats (deletion) expressed isoforms V1 and V4. Activated IRF5 dimerizes and translocates to the nucleus via NLS (nuclear localization signal) motifs, which bind promoter sequences and activates transcription of IFN-�/� and other pro-inflammatory cytokines such as TNF-�, IL-12 and IL-6.(132) Our goal was to find out which of the isoforms (V1, V4, V5 and V6) had NLS motifs since this plays an important role. To our surprise, all four isoforms have two common NLS sequences, one in the 5’ end (PRRVRLK) and the other in the 3’end (PREKKLI). In addition, three nuclear export signals (NES) were found and among them one NES was found in all four isoforms (LQRMLPSLSLT). Another NES (TDAVQSGPHMTPYSLLKE DVKWPP) was found in V1and V5, whereas the last NES (TEDVKWPPTLQPPT LR) was found only in V6. It has been proposed that the counter effects of NLS and NES sequences decides which isoforms move into the nucleus. I suspect that isoform V4 plays a major role, since it has minimum counter effect. This is just an assumption, and further work must be done to confirm this. In collaboration with Tomas Bergstrom, we analyzed 380 Ceph samples from Africa, Asia and South America to see how the frequency of the risk alleles and haplotypes varied among different world populations. We found that the frequency of the risk haplotype was lower in these populations compared to European populations. Unfortunately, there was a paper published before us stating the same thing.(133)

Table 1. The frequencies of CEPH alleles and haplotypes in comparison to European controls Markers Europe Africa S. America Asia Alleles 2004640 = T 51.6% 47.5% 6.8% 44.6% Indel = I 51.5% 49.5% 84.2% 54.3% 2070197 = C 9.6% 5.4% 10.5% 14.7% 10954213=A 60.6% 52.6% 14.5% 51% Haplotypes TICA 9.9% 0.1% 2.6% 4.8%

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VIII. Paper descriptions

Aim The aim of my work in this thesis is to identify susceptibility genes behind SLE by using family-based and population-based approaches. To find if other autoimmune diseases (e.g. RA and psoriasis) share the same susceptibility genes as SLE. To analyze whether susceptibility gene interactions make the disease symptoms worse. To elucidate the mechanisms by which associated genetic variants could actually lead to disease susceptibility.

Paper 1 A common haplotype of interferon regulatory factor 5 (IRF5) was found to regulate splicing and expression and was associated with increased risk for systemic lupus erythematosus.

Introduction In this paper, we replicated the IRF5 rs2004640 association with SLE using four independent case control cohorts. This was well supported by a family-based TDT analysis. To date, this is the strongest association ever found with SLE. In the course of the study, we found that SNP rs2004640 creates a donor splice site. Haplotype analysis was performed with rs2004640 and a few other new SNPs that we typed. We found a haplotype that was significantly associated with the disease and elevated levels of IRF5 expression.

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Materials and methods The four sets of patients and controls were from Argentina, Spain, Sweden and USA. The family-based materials were from the USA.

Table 2. The numbers of patients and controls used in the analysis. Population Cases Controls Argentina 284 279 Spain 444 541 Sweden 208 254 USA 725 1434 The family-based transmission disequilibrium test (TDT) was performed using 467 U.S. pedigrees of European ancestry. Four polymorphisms from IRF5 (rs729302, rs2004640, rs752637 and rs2280714) were genotyped using TaqMan. For the family-based material, the TDT analysis was performed using Haploview. A Chi square analysis was done to evaluate whether there were significant differences in genotype and allele frequencies between case and control samples. Haploview was used to generate haplotype frequencies and calculate the significance of associations. Real-time PCR (TaqMan ABI) was used to quantify IRF5 isoforms with distinct 5’-UTRs. The statistical analysis of isoform expression was performed using GraphPad Software. To isolate new isoforms, RT-PCR was conducted using total RNA isolated from human DNA as a template.

Results and discussion Elevated levels of type I interferons and increased expression of IFN-inducible genes is commonly observed in SLE patients. Several studies have suggested that type I interferons play a pivotal role in the disease pathogenesis. IRF5 is one among many genes induced by type I interferons. IRF5, in turn, induces type I IFN production when exposed to a viral infection. It was recently reported that SNPs in IRF5 were associated with SLE. We were able to replicate the association of SNP rs2004640 with SLE. We also found new SNPs that were associated and formed a haplotype with rs2004640.

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Table 3. Association of SNP rs2004640 in four different case/control cohorts Population P-Value Argentina 0.00035 Spain 0.00016 Sweden 0.04268 USA 3.6 x 10-9

Table 4. Association and haplotype analysis of four different SNPs using family-based TDT

SNPs P-Value rs729302 A 0.0001 rs2004640 T 0.0006 rs752637 C 0.0450 rs2280714 T 0.1202 Haplotype ATCT 0.0035

The splicing of IRF5 is known to be highly complex, with nine previously identified isoforms and two more isoforms identified during the course of our study. The transcription of various IRF5 isoforms is initiated at one of the three promoters, giving rise to transcripts starting either at exon 1A, exon 1B or exon 1C. The rs2004640 T allele is located 2 bp downstream of the intron–exon border of exon 1B, and creates a consensus GT donor splice site. Exon 1B transcripts were only present in individuals having T alleles at rs2004640. In addition, we found SNP rs2280714, located 5kb downstream of IRF5, which showed a correlation between IRF5 expression and the presence of the T allele. Haplotype analysis was performed and haplotypes associated with IRF5 overexpression (the rs2280714 T allele) but lacking the exon 1B splice site, showed no evidence of association with increased risk of SLE. In summary, our data confirm the association of IRF5 with SLE and we identified a risk haplotype that contains a donor splice site that allows for the expression of multiple IRF5 isoforms containing exon 1B as well as a separate gene variant associated with elevated IRF5 expression. The exon 1B isoforms are strongly linked to elevated expression of IRF5 and risk for SLE, however, overexpression of IRF5 in the absence of exon 1B isoforms does not confer risk.

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Paper 2 Structural insertion/deletion variation in IRF5 is associated with a risk haplotype which defines which IRF5 isoforms are expressed in SLE.

Introduction It was previously described in paper one that the T allele of the SNP rs2004640 introduces a donor splice site leading to the expression of exon 1B transcripts. Furthermore, in a second SNP (rs2280714) located 5 kb downstream of IRF5, the T allele is strongly associated with high levels of IRF5 expression. Alleles at both SNP positions comprise a haplotype associated with SLE. In this paper, we genotyped other polymorphisms that showed an even stronger association (rs2070197) and correlation with IRF5 expression (rs10954213). In addition, we identified a novel polymorphic insertion/deletion that defines the pattern of isoform expression for IRF5.

Materials and methods The groups of patients and controls from Argentina and Spain used in this paper to type the new SNPs are the same groups referred to in paper one. The group of subjects from Germany consisted of 288 SLE patients and 245 healthy controls from various centers in northern Germany. Additionally, two sets of trios from Spain and Denmark, which totaled 168 families with 71 complete trios, were used in the analysis. The genotyping was done on a TaqMan ABI 9700 real-time PCR system. The primers and probes were designed by the Applied Biosystems Assays-on-Demand service. Genetic association analyses of the SNPs and haplotypes in patients and controls and family-based analysis were performed using Haploview. Correlations of IRF5 mRNA levels were analyzed using the GraphPad software.

Results and discussion

Table 5. Association analysis of four different SNPs. Markers Alleles P-value rs2004640 T 3.5 x 10-9 Insertion/deletion I 0.626 rs2070197 C 5.2 x 10-11 rs10954213 A 0.0008

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This SNP rs10954213 showed strong association and is located in the 3’UTR poly (A) site AAT (A/G) AA. This SNP can potentially determine IRF5 expression levels since the A allele produces mRNA with a short 3’UTR, and the G allele disrupts the poly (A) site thus causing transcription to continue. The mRNA with a longer 3’UTR has 2 strong AREs known to be responsible for a shorter half-life and more rapid RNA degradation. On the other hand, the mRNA with a shorter 3’UTR bearing no AREs should be more stable and present at higher levels. The novel insertion/deletion identified lacked 2 of 4 tandem repeats and is located in exon 6 which has a proline-rich putative PEST domain. Though this marker by itself did not show any association, the presence or absence of the repeats determined which isoform was expressed, such that individuals with 4 repeats (insertion) expressed isoforms V5 and V6, while individuals with 2 repeats (deletion) expressed isoforms V1 and V4. The SNP rs2070197 was not involved with splicing or expression, but is very strongly associated and present in the risk haplotype.

Table 6. The association of the most common haplotype. Haplotype P-value TICA (case controls) 5.72 x 10-8 TICA (family-based ) 0.000137 Haplotypes were constructed using the above four markers. Both population-based and family-based analysis revealed that TICA was the only haplotype that was strongly associated. This newly formed haplotype broke the previously identified haplotype into a more narrow and clearer haplotype.

Paper 3 Genetic association of IRF5 with SLE in Mexicans: homozygozity and a higher frequency of the risk haplotype compared to Europeans.

Introduction In this paper, we replicated two functional polymorphisms (rs2004640, rs109542130) and an insertion/deletion together with a tag SNP (rs2070197) that defined the risk haplotype in individuals of European ancestry.

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Materials and methods These IRF5 polymorphisms were genotyped in SLE cases and controls, as well as in families that included sets of pediatric SLE patients. One hundred and eighty nine adult SLE patients were studied from the Mexico City Mestizo population along with 284 pediatric SLE patients from Mexico City. In addition, 282 healthy controls matched by age and sex were also used. A total of 159 pediatric patients with both parents available for genotyping were used to confirm the genetic association along with a separate set of 151 trio families. Genotyping of rs2004640, rs2070197 and rs10954213 was performed using the TaqMan real-time PCR method as an ABI assay-on-demand. Genotyping of the indel was performed only on the adult SLE families. Genotyping of this variant has been described previously. The Breslow–Day test for heterogeneity was run on the STATS DIRECT software.

Results and discussion Mexican individuals with SLE appear to have a more severe disease and a lower age of onset than European women, therefore it is important to understand whether the same genes and alleles have a role in this population. Thus, we typed all four polymorphisms in the Mexican populations. The genetic association was very strong in the case control analysis in both the adult and pediatric sets when compared to European individuals. Compared to healthy individuals with European ancestry, the frequency of the risk haplotype in healthy Mexican individuals was significantly higher and even higher in the healthy Mexican Indian group. Furthermore, Mexican SLE patients also showed, a much higher frequency of the risk haplotype. This could be a result of genetic admixture, and it may be that IRF5 could be involved in the more active form of disease that is known to occur in Mexican SLE patients. Frequencies in adult and pediatric SLE patients were similar and the differences were not significant. Using the Breslow–Day test of heterogeneity, we observed that the data from adult and pediatric SLE patients could be combined. We therefore combined the data from both adult and pediatric SLE patients.

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Table 7. Association of markers and haplotypes using Mexican adult and pediatric cases and controls. Markers Alleles P-value rs2004640 T 6.07 x10-13 rs2070197 C 1.26 x 10-21 rs10954213 A 1.78 x 10-9 Haplotype TCA 7.69 x 10-21 This was well supported by a family-based TDT analysis. All of the markers showed stronger association compared to Europeans. We also performed a haplotype analysis and observed exactly the same thing we saw in the European materials, that is the insertion was found in the risk haplotype (TCA or TICA where I denotes insertion) (data not shown). Also, similar to the Europeans, the insertion by itself was not associated with SLE (Chi-Sq = 0.44; P = 0.50), but the risk haplotype containing the insertion was associated.

Table 8. Association of markers and haplotypes using family-based TDT analysis. Markers P-Value rs2004640 0.0004 rs2070197 3.21x10-6 rs10954213 0.0071 Haplotype (TCA) 3.64 x 10-7

Paper 4 Analysis of IRF5 gene functional polymorphisms in rheumatoid arthritis.

Introduction In this paper, we analyzed the role of two gene variants of the IRF5 gene in rheumatoid arthritis. The reasons for analyzing these variants was because they have been shown to affect IRF5 mRNA splicing (rs2004640) and expression (rs2280714) has been associated with genetic predisposition to SLE. In addition, recent findings suggest that IRF5 may play a crucial role in the transcription of many genes including inflammatory cytokines.

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Materials and methods Association analysis was performed using three different case control cohorts from Spain (724 RA patients and 542 healthy controls), Sweden (281 RA patients 474 healthy controls), and Argentina (284 RA patients and 286 healthy controls) which were independently analyzed. Genotyping for IRF5 SNPs rs2004640 and rs2280714 was performed using a TaqMan allele-discrimination assay. The test for significance was done using 2x2 contingency tables and Fisher’s exact test.

Results and discussion There is clear evidence that suggests autoimmune diseases share common genetic factors that predispose individuals to the development of the diseases. Because of its functional relevance in the immune system and its strong association with SLE susceptibility in different populations, the IRF5 gene has been thought to be a possible common genetic marker for autoimmunity. Upon analyzing the three RA case control cohorts, no statistically significant differences were observed in allele or genotype frequencies of the rs2004640 and rs2280714 IRF5 polymorphisms. Additionally, haplotype analysis was performed and revealed that none of the IRF5 haplotypes were associated with genetic predisposition to RA. Both the patient and control groups were found to be in Hardy-Weinberg equilibrium in all three populations in which the two SNPs were analyzed. When we analyzed different clinical manifestations of RA with the IRF5 SNPs and haplotypes, no significant differences were observed. We also analyzed whether susceptibility genes behind RA interacted with each other to worsen the disease symptoms. Since the PTPN22 gene has a well-described role in RA, we also performed an interaction analysis of IRF5 genotypes/alleles according to PTPN22 genotypes. A conditional logistic regression analysis revealed that there was no interaction between these two genes. Although patients with RA have signs of type I IFN production, type I IFNs seem to exert a secondary role in this disease in contrast to SLE. The IFN-inducible genes involved in RA seem to be dominated mainly by type II IFNs (IFN gamma). Thus, it could be speculated that the lack of association in RA to IRF5, and its strong association in SLE, might be a consequence of the different mechanisms through which IFNs are regulated in both diseases.

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Paper 5 IRF5 gene variants interact with the MHC class I locus in a Swedish psoriasis population.

Introduction Psoriasis is a genetically complex disorder. Psoriasis is a common skin disease characterized by skin redness and scaly plaques at different sites. In psoriasis, T cells abnormally trigger inflammation in the skin. These T cells can also cause skin cells to grow faster than normal, and to pile up in raised patches on the outer surface of the skin. A few decades back it was reported that psoriasis was exacerbated by IFN-�. In support of this, it was reported that when hepatitis patients were treated with IFN-�, symptoms of SLE, arthritis and psoriasis appeared. IFN-� is produced by DCs, PDCs and B cells. Through IFN-� production, PDCs drive the activation and expansion of autoimmune T cells in pre-psoriatic skin, leading to the development of psoriasis. IRF5 regulates interferon expression and is associated with a higher risk for SLE. An IRF5 haplotype was recently reported that associated with SLE through overexpression of the gene. We hypothesized that functional polymorphisms causing IRF5 overexpression might lead to the increased expression of IFN-�, which seems to play a significant role in the activation of autoimmune T cells. Therefore, we typed the four polymorphisms in a set of psoriasis patients and controls. To our surprise, neither the polymorphisms nor the haplotype were associated with the disease. However, when pairwise SNP interactions were done under different models between HLA-G and rs2004046, rs2070197, rs10954213 and rs2280714, a significant association was found between rs2004640 and psoriasis when adjustments were made for rs16375.

Materials and methods The Stockholm Psoriasis Cohort is a prospective collection of samples, together with clinical and environmental data, from psoriasis patients and population-matched controls. The selection was made randomly from Swedish national registries according to sex, age and place of residence. The study included 645 patients and 472 controls. Genotyping was performed using the Taqman platform 7900HT sequence detection system from

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Applied Biosystems (ABI, Foster City, CA, USA). The same company designed the primers and probes. Haplotype analysis, Hardy-Weinberg equilibrium and degree of LD were tested using Haploview. Allele, genotype and haplotype distributions were compared between cases and controls using the package SNPassoc which tests associations under several genetic models including dominant, recessive, co-dominant and over-dominant.

Results and discussion The SNPs rs2004640 and rs2070197 were more strongly associated with SLE, while SNP rs10954213 was associated with IRF5 overexpression. The most common haplotype that was observed in SLE was (TCAT) which contains the T allele of rs2004640, C allele of rs2070197, A allele of rs10954213, and T allele of rs2280714. Similarly, the allele, genotype and haplotype distributions of the above SNPs were determined for a set of patients and controls from Stockholm (Sweden). No significant differences were found between psoriasis patients and population-matched controls. All of the controls were found to be in Hardy-Weinberg equilibrium for the four markers tested. Strong LD was detected between the three most 3’ markers. Since IRF5 influences interferon expression which could regulate the expression of several other pathogenic factors involved in psoriasis such as MHC class I molecules, the interaction between the factors was tested at the genotypic level. We found a very interesting result when pairwise SNP interactions were done between HLA-G and rs2004046, rs2070197, rs10954213 and rs2280714 under different models. A significant interaction between the 14bp deletion/insertion in exon 8 of HLA-G (rs16375) and IRF5 rs2004640 was observed under a dominant model (p=0.04047), however since several models were tested this interaction should be taken as suggestive.

Table 9. Association analysis between psoriasis and IRF5 SNPs adjusted by HLA-G rs16375 co

dominant Dominant Recessive Over

dominant Log-additive

rs2004640 0.12068 0.04047 0.64303 0.13531 0.10543 rs2070197 0.43030 0.20474 0.56419 0.26643 0.19493 rs10954213 0.24436 0.11113 0.98804 0.11945 0.24927 rs2280714 0.80748 0.51340 0.86632 0.57520 0.56028

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In conclusion, neither individual IRF5 SNPs nor their haplotypes are associated with psoriasis. Yet, an interesting pattern has emerged after adjustment by rs16375, where rs2004640 displays a different distribution between cases and controls.

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IX. Concluding remarks

Despite the progress being made in the genetics of complex diseases, finding the functional implications of these associated genes is rather slow. Therefore, the biggest challenge for people working with complex diseases is finding the functional aspects of susceptibility genes that correlate to the phenotype of the disease. The work in my thesis involves the IRF5 association to SLE and the functional effects the variants may have in elevating disease symptoms. We found that SNP rs2004640 creates a donor splice site which affects the splicing patterns, while SNP rs10954213 correlates with the levels of IRF5 expression. Additionally, an indel was found in exon 6, and though this marker by itself did not show any association, the presence or absence of the insertion determined which isoforms were expressed. Specifically, individuals with 4 repeats (insertion) expressed isoforms V5 and V6, while individuals with 2 repeats (deletion) expressed the isoforms V1 and V4. IRF5 also plays a major role in apoptosis, and it is known that increased rate of apoptosis is a characteristic feature of SLE. I suspect that individuals with the associated variants in the IRF5 gene show overexpression, though this overexpression is not enough to induce apoptosis. Most likely the apoptosis is triggered by factors such as viral infection which may damage cells beyond repair.

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Figure 9. A description of how polymorphisms in the IRF5 gene might increase disease susceptibility.

Normally, apoptotic cell fragments are cleared by macrophages, dendritic cells, etc. However, increased apoptosis results in decreased clearance. As a result, the apoptotic cell fragments may remain in the circulation thereby activating the production of autoantibodies. Under normal conditions, the production of autoantibodies is kept under control by inhibitory molecules such as PDCD1 and CTLA4. This process is called immune tolerance. Improper functioning of these molecules results in a breakdown of immune tolerance which means that B and T lymphocytes can become overactive and recognize both viral fragments and self-apoptotic cell fragments as foreign bodies thereby generating autoantibodies. The interaction between autoantibodies and autoantigens results in the formation of immune complexes (ICs). The deposition of ICs in different parts of the body results in inflammation. For example, the deposition of ICs in the kidneys results in glomerulonephritis. ICs activate the interferon pathway through Fc gamma receptors and result in the induction of type I interferons (IFN�/�), which in turn induce the production of interferon inducible genes. It is known that elevated levels of

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type I interferons and increased expression of IFN-inducible genes is commonly observed in SLE patients. These IRF5 polymorphisms have been successfully replicated in many white(133-136) and Asian (Korean)(137) SLE populations. It is clearly understood from the above discussion that many genes (e.g. PDCD1, PTPN22 and IRF5) contribute to the overall risk of the disease. Of course, the association of PTPN22 with SLE is very weak when compared to the associations found with RA and type 1 diabetes. Because PDCD1 and PTPN22 were associated with other autoimmune diseases, we also tried to see if other autoimmune diseases share IRF5 as a common susceptibility gene. However, the association seems to be SLE-specific and has so far not been found to be associated with other autoimmune diseases. Along with others, we tested its association in RA(138,139) and psoriasis (paper 5 submitted) and found no association. Qu et. al. tested type 1 diabetes as well and no association was found.(140) Recently, it was reported by Sigurdsson et. al.(141) that when a Swedish set of RA patients were stratified into subgroups according to the presence or absence of RF and anti-CCP antibodies, significant associations were detected in patients who were negative for both anti-CCP and RF. When this analysis was replicated in a Dutch cohort, a suggestive association was detected. This finding strengthens our belief that this association is SLE-specific, because both RF and anti-CCP are characteristic features of RA. I suspect that for each complex disease there might be some disease-specific genes that make it different from other diseases, in addition to some commonly shared genes that may add to the increased risk. Therefore, we believe that this association is specific for SLE and that this may assist in understanding the important mechanisms behind the pathogenesis of SLE, which in turn might be helpful in finding a cure for the disease.

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X. Acknowledgements

I would like to express my sincere gratitude to all of those who gave me the possibility to complete this thesis. I am deeply indebted to my supervisor Prof. Marta whose help, suggestions and encouragement helped me a lot. Her moral support was of great help during difficult times. To me, she was more like a parent than a supervisor. Thank you Marta for making me strong both professionally and personally. A special thanks to my co-supervisor Ulf Gyllensten, for his thoughtful questions and inspiring discussions during PCR group meetings. Thank you also for always signing all of my papers and applications whenever needed in spite of your busy schedule. I would like to thank my colleague Anna Karin for her continuous support throughout my Ph.D. program. She was always there to listen and to give advice. Thank you especially for the infinite number of times you translated each and every letter and document that was in Swedish. Thank you, thank you! Sergey has been a friend and mentor, and he taught me how to work hard but at the same time to take life easy. Without his encouragement and constant guidance, I could not have finished things in time. He was always there to meet and talk about my ideas, ask me good questions and help me to think through my problems in both science and life. Thank you, Sergey. Also, I would like to thank Susana for the interesting discussions and fun we had during the real deadlines. Thank you Susana, you were of great help during that period. I am also grateful to Hong for her encouragement and never ending support, as well as for all of the urgent DNA dilutions she made, which is why I never missed any deadlines, so I sincerely give all my credit to her.

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I want to thank my former colleagues from the SLE group, for all of their help, support and valuable suggestions. I am especially obliged to Bo, Ludmila and Cecilia. I would also like to take this opportunity to welcome and thank all new colleagues in the SLE group namely Juan-Ramon, Amar, Angelica and Casimero for his re-entry. I would like to express my sincere gratitude to all of the people in the PCR group and the new Thursday seminar group for their enlightening presentations and discussions. I am especially grateful to all of the old and new people at Genome center especially Inger and Jenny for giving me special permission to change blocks during the summer and weekends, because of which I was able to complete my work during the peak periods. I am also grateful to all of my Indian friends here in Uppsala including M.Kiran, Gitender, Madhu, Pradeep, BMC Prasad and especially Chandu and Srivathsa for providing me with a place to stay and for the delicious food they made occasionally. I am especially very grateful to my wife whose patience, encouragement and understanding enabled me to complete this work. Last but not least, I would like to thank my parents for giving me their unconditional support and encouragement to pursue my interests, even when my interests were beyond their boundaries to support. Thank you dad, without your help all this might not have been possible.

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XI. References

1. Liu N, Chen L. et al. Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure. BMC Genet. 2005 Dec 30;6 Suppl 1:S26.

2. Stephen Chanock, MD and Meredith Yeager, PhD The Future of Pediatric Cancer and Complex Diseases: Aren’t They All? Pediatr Blood Cancer 2007;48:719–722

3. Erichsen HC, Chanock SJ. SNPs in cancer research and treatment. Br J Cancer 2004;90:747–751.

4. Savage SA, Chanock SJ. Genetic association studies in cancer: Good, bad or no longer ugly? Hum Genomics 2006;2:415–421.

5. Wandstrat A, Wakeland E. The genetics of complex autoimmune diseases: non-MHC susceptibility genes. Nat Immunol2001; 2: 802–809.

6. Schrodi SJ, Garcia VE, Rowland C, Jones HB. Pairwise linkage disequilibrium under disease models. Eur J Hum Genet. Eur J Hum Genet. 2007 Feb;15(2):212-20.

7. Weiss KM, Clark AG .Linkage disequilibrium and the mapping of complex human traits. Trends Genet. 2002 Jan;18(1):19-24.

8. Long AD, Langley CH. The power of association studies to detect the contribution of candidate genetic loci to variation in complex traits. Genome Res. 1999 Aug;9(8):720-31.

9. Lewontin RC. The Interaction of Selection and Linkage. I. General Considerations; Heterotic Models.Genetics. 1964 Jan;49(1):49-67.

10. Robert C Elston. Introduction and overview. Statistical methods in genetic epidemiology. Stat Methods Med Res. 2000 Dec;9(6):527-41.

11. Nan M. Laird and Christoph Lange . Family-based designs in the age of large-scale gene-association studies. Nature genetics. volume 7 May 2006 385-394

12. Boehnke M, Langefeld CD. Genetic association mapping based on discordant sib pairs: the discordant-alleles test. Am J Hum Genet. 1998 Apr;62(4):950-61.

13. Falk CT, Rubinstein P. Haplotype relative risks: an easy way to construct a proper controlsample for risk calculations. Ann Hum Genet. 1987; 51: 227–33.

70

14. McNemar Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 1947; 12: 153–57.

15. Zheng G, Chen Z, Li Z.Tests for candidate-gene association using case-parents design.Ann Hum Genet. 2003 Nov;67(Pt 6):589-97.

16. Spielman RS, McGinnis RE, Ewens WJ. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet. 1993 Mar;52(3):506-16.

17. Thomson G. Mapping disease genes: family-based association studies. Am J Hum Genet. 1995 Aug; 57(2):487-98.

18. Thomson G. Analysis of complex human genetic traits: an ordered-notation method and new tests for mode of inheritance. Am J Hum Genet. 1995 Aug;57(2):474-86.

19. Heather J. Cordell. Epistasis: what it means, what it doesn’t mean, and statistical methods to detect it in humans. Hum Mol Genet. 2002 Oct 1;11(20):2463-8.

20. Quanhe Yang, Muin J. Khoury. et al. Case-Only Design to Measure Gene-Gene Interaction. Epidemiology. 1999 Mar;10(2):167-70

21. Jarvik G, Larson EB, Goddard K, Schellenberg GD, Wijsman EM. Influence of apolipoprotein E genotype on the transmission of Alzheimer disease in a community-based sample. Am J Hum Genet 1996;58:191-200.

22. MacLeod S, Sinha R, Kadlubar FF, Lang NP. Polymorphism of CTP1A1 and GSTM1 influence the in vivo function of CYP1A2. Mutat Res 1997;376:135-142.

23. Beales PL, Kopelman PG. Obesity genes. Clin Endocrinol (Oxf). 1996 Oct;45(4):373-8.

24. Williams RR, Hunt SC, Hopkins PN, Wu LL. et al. Genetic basis of familial dyslipidemia and hypertension: 15-year results from Utah. Am J Hypertens 1993;6(11 pt 2):319S-27S.

25. Davignon J, Roy M. Familial hypercholesterolemia in French-Canadians: taking advantage of the presence of a ‘founder effect’. Am J Cardiol 1993 Sep 30;72(10):6D-10D.

26. Ma DQ, Whitehead PL et al. Identification of significant association and gene-gene interaction of GABA receptor subunit genes in autism. Am J Hum Genet. 2005 Sep;77(3):377-88.

27. Gauderman WJ. Sample size requirements for association studies of gene-gene interaction. Am J Epidemiol. 2002 Mar 1;155(5):478-84.

28. Rothman K, Greenland S, Walker A. Concepts of interaction. Am J Epidemiol 1980;112:467–70.

29. Khoury M, Flanders D. Nontraditional epidemiologic approaches in the analysis of gene-environment interaction: case-control studies with no controls! Am J Epidemiol 1996; 144:207–13.

71

30. Piegorsch WW, Weinberg CR, Taylor JA. Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat Med. 1994 Jan 30;13(2):153-62.

31. Munafo MR, Flint J. Meta-analysis of genetic association studies. Trends Genet. 2004 Sep;20(9):439-44.

32. Vollmer R.T., Multivariate statistical analysis for pathologists. Part I, The logistic model. Am J Clin pathol. (1996)105, pp. 115–126.

33. Hosmer D.W. and S. Lemeshow. Applied logestic regression. Wiley, New York (1989).

34. Bagley SC, White H, Golomb BA. Logistic regression in the medical literature: Standards for use and reporting, with particular attention to one medical domain. J Clin Epidemiol. 2001 Oct;54(10):979-85

35. Black DL. Mechanisms of alternative pre-messenger RNA splicing. Annu Rev Biochem. 2003;72:291-336.

36. Ladd, A. N. & Cooper, T. A. Finding signals that regulate alternative splicing in the post-genomic era. Genome Biol. 2002 Oct 23;3(11).

37. Black, D. L. & Grabowski, P. J. Alternative pre-mRNA splicing and neuronal function. Prog Mol Subcell Biol. 2003;31:187-216.

38. Lequn Li & Vassiliki A. Boussiotis Physiologic regulation of central and peripheral T cell tolerance:lessons for therapeutic applications. J Mol Med (2006) 84:887–899

39. Ermann J, Fathman CG. Autoimmune diseases: genes, bugs and failed regulation. Nat Immunol. 2001 Sep;2(9):759-61.

40. Hochberg MC. The application of genetic epidemiology to systemic lupus erythematosus. J Rheumatol. 1987 Oct;14(5):867-9.

41. Lawrence JS, Martins CL, Drake GL. A family survey of lupus erythematosus. 1. Heritability. J Rheumatol. 1987 Oct;14(5):913-21.

42. Deapen D, Escalante A, Weinrib L, Horwitz D, Bachman B. et al. A revised estimate of twin concordance in systemic lupus erythematosus. Arthritis Rheum. 1992 Mar;35(3):311-8.

43. McMurray RW. Sex hormones in the pathogenesis of systemic lupus erythematosus. Front Biosci. 2001 Dec 1;6:E193-206.

44. Cutolo M, Sulli A, Capellino S, Villaggio B, Montagna P, Seriolo B, Straub RH. Sex hormones influence on the immune system: basic and clinical aspects in autoimmunity. Lupus. 2004;13(9):635-8.

45. Zandman-Goddard G, Peeva E, Shoenfeld Y. Gender and autoimmunity. Autoimmun Rev. 2007 Jun;6(6):366-72.

46. Baechler EC, Batliwalla FM, Karypis G, Gaffney PM, Ortmann WA, Espe KJ et al. Interferon-inducible gene expression signature in peripheral blood cells of patients with severe lupus. Proc Natl Acad Sci USA 2003;100:2610–2615.

47. van Holten J, Smeets TJ, Blankert P, Tak PP. Expression of interferon beta in synovial tissue from patients with rheumatoid

72

arthritis: comparison with patients with osteoarthritis and reactive arthritis. Ann Rheum Dis 2005;64:1780–2.

48. van der Pouw Kraan TC, Wijbrandts CA, van Baarsen LG, Voskuyl AE, Rustenburg F, Baggen JM, Ibrahim SM, Fero M, Dijkmans BA, Tak PP, Verweij CL.Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: assignment of a type I interferon signature in a subpopulation of patients. Ann Rheum Dis. 2007 Aug;66(8):1008-14.

49. Kapp A. The role of cytokines in the psoriatic inflammation. J Dermatol Sci. 1993 Jun;5(3):133-42

50. Rachel Murphy. PDC-derived interferon- is a master cytokine in psoriasis development. J Exp Med. 2005 Jul 4;202(1):135-43.

51. Nestle FO, Conrad C, Tun-Kyi A, Homey B, Gombert M, et al. Plasmacytoid predendritic cells initiate psoriasis through interferon-alpha production. J Exp Med. 2005 Jul 4;202(1):135-43.

52. Funk J, Langeland T, Schrumpf E, Hanssen LE.Psoriasis induced by interferon-alpha. Br J Dermatol. 1991 Nov;125(5):463-5.

53. Smith CD, Cyr M. The history of lupus erythematosus from Hippocrates to Osler. Rheum Dis Clin North Am 1988; 14:1.

54. Hahn B. Pathogenesis of SLE. In: Kelley WN, Harris ED, Ruddy S, Sledge CB, eds. Textbook of Rheumatology. 5th Ed. Philadelphia: W.B. Saunders; 1997:1015-1027.

55. Tan EM. Antinuclear antibodies: Diagnostic markers for autoimmune diseases and probes for cell biology. Adv Immunol 1989;44:93-151.

56. Petri M.Epidemiology of systemic lupus erythematosus. Best Pract Res Clin Rheumatol. 2002 Dec; 16(5):847-58.

57. Yacoub Wasef SZ. Gender differences in systemic lupus erythematosus. Gend Med. 2004 Aug;1(1): 12-7.

58. Ghaussy NO, Sibbitt W Jr, et al. The Effect of Race on Disease Activity in Systemic Lupus Erythematosus. J Rheumatol. 2004 May;31(5):915-9.

59. Ward MM, Pyun E, Studenski S. Long-term survival in systemic lupus erythematosus. Patient characteristics associated with poorer outcomes. Arthritis Rheum 1995;38:274-283.

60. Cohen A.S., Reynolds W.E. and Franklin E.C. Preliminary criteria for the classification of systemic lupus erythematosus. Bull Rheum Dis 1971, 21: 643–8.

61. Tan EM, Cohen AS, Fries JF, Masi AT, McShane DJ, Rothfield NF, Schaller JG, Talal N, Winchester RJ: The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 1982, 25:1271-1277.

62. Hochberg MC: Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1997 Sep;40(9):1725.

73

63. Shiina, T., Inoko, H. & Kulski, J. K. An update of the HLA genomic region, locus information and disease associations: Tissue Antigens. 2004 Dec;64(6):631-49

64. Grumet FC, Coukell A, et al. Histocompatibility (HL-A) antigens associated with systemic lupus erythematosus. A possible genetic predisposition to disease. N Engl J Med. 1971 Jul 22;285(4):193-6

65. Steinsson K, Jonsdottir S, Arason GJ et al. A study of the association of HLA DR, DQ, and complement C4 alleles with systemic lupus erythematosus in Iceland. Ann Rheum Dis 1998; 57: 503–505.

66. Ghebrehiwet B, Peerschke EI: Role of C1q and C1q receptors in the pathogenesis of systemic lupus erythematosus. Curr Dir Autoimmun 2004, 7:87-97.

67. Bowness P, Davies KA, Norsworthy PJ et al. Hereditary C1q deficiency and systemic lupus erythematosus. QJMed. 1994; 87: 455–464.

68. Topaloglu R, Bakkaloglu A, Slingsby JH et al. Survey of Turkish systemic lupus erythematosus patients for a particular mutation of C1Q deficiency. Clin Exp Rheumatol 2000; 18:75–77.

69. Walport MJ. Complement. First of two parts. N Engl J Med 2001;344:1058–1066.

70. Walport MJ. Complement. Second of two parts. N Engl J Med 2001;344:1140–1144.

71. Bowness P, Davies KA, Norsworthy PJ, Athanassiou P, Taylor-Wiedeman J, Borysiewicz LK, Meyer PA, Walport MJ. Hereditary C1q deficiency and systemic lupus erythematosus. QJM. 1994 Aug;87(8): 455-64.

72. Sullivan KE, Petri MA, Schmeckpeper BJ, McLean RH, Winkelstein JA. Prevalence of a mutation causing C2 deficiency in systemic lupus erythematosus. J Rheumatol. 1994 Jun;21(6):1128-33.

73. Dijstelbloem HM, Bijl M, Fijnheer R, Scheepers RH, Oost WW, Jansen MD, Sluiter WJ, Limburg PC, Derksen RH, van de Winkel JG, Kallenberg CG. Fcgamma receptor polymorphisms in systemic lupus erythematosus: association with disease and in vivo clearance of immune complexes. Arthritis Rheum. 2000 Dec;43(12):2793-800

74. Means TK, Latz E, Hayashi F, Murali MR, Golenbock DT,Luster AD. Human lupus autoantibody-DNA complexes activate DCs through cooperation of CD32 and TLR9. J Clin Invest 2005; 115: 407–417.

75. Boule MW, Broughton C, Mackay F, Akira S, Marshak-Rothstein A, Rifkin IR. Toll-like receptor 9-dependent and -independent dendritic cell activation by chromatin-immunoglobulin G complexes. J Exp Med 2004; 199: 1631–1640.

76. Bave U, Magnusson M, Eloranta ML, Perers A, Alm GV,Ronnblom L. Fc gamma RIIa is expressed on natural IFN-alpha-producing cells

74

(plasmacytoid dendritic cells) and is required for the IFN-alpha production induced by apoptotic cells combined with lupus IgG. J Immunol 2003; 171: 3296–3302

77. Lehrnbecher T, Foster CB, Zhu S, Leitman SF, Goldin LR, Huppi K, Chanock SJ. Variant genotypes of the low-affinity Fcgamma receptors in two control populations and a review of low-affinity Fcgamma receptor polymorphisms in control and disease populations. Blood. 1999 Dec 15;94(12):4220-32.

78. van der Pol W, van de Winkel JG. IgG receptor polymorphisms: risk factors for disease. Immunogenetics. 1998 Aug;48(3):222-32.

79. Edberg JC, Langefeld CD, Wu J, Moser KL, Kaufman K, Kelly J, Bansal V, et al. Genetic linkage and association of Fcgamma receptor IIIA (CD16A) on chromosome 1q23 with human systemic lupus erythematosus. Arthritis Rheum. 2002 Aug;46(8):2132-40

80. Mataki N, Kikuchi K, Kawai T, Higashiyama M, Okada Y, Kurihara C, Hokari R, Kawaguchi A, Nagao S, Kondo T, Itoh K, Miyakawa H, Miura S. Expression of PD-1, PD-L1, and PD-L2 in the liver in autoimmune liver diseases. Am J Gastroenterol. 2007 Feb;102(2):302-12

81. Nishimura H, Honjo T: PD-1: an inhibitory immunoreceptor involved in peripheral tolerance. Trends Immunol 2001; 22: 265– 268.

82. Nishimura H, Nose M, Hiai H, Minato N, Honjo T. Development of lupus-like autoimmune diseases by disruption of the PD-1 gene encoding an ITIM motif-carrying immunoreceptor. Immunity. 1999 Aug;11(2):141-51.

83. Nishimura H, Minato N, Nakano T, Honjo T. Immunological studies on PD-1 deficient mice: implication of PD-1 as a negative regulator for B cell responses. Int Immunol. 1998 Oct;10(10):1563-72.

84. Prokunina L, Casrillejo-Lopez C, Oberg F, et al. A regulatory polymorphism in PDCD1 is associated with susceptibility to systemic lupus erythematosus in humans. Nat Genet 2002;32:666–669.

85. Johansson M, Arlestig L, Moller B, Rantapaa-Dahlqvist S. Association of a PDCD1 polymorphism with renal manifestations in systemic lupus erythematosus. Arthritis Rheum. 2005 Jun;52(6):1665-9.

86. Thorburn CM, Prokunina-Olsson L, Sterba KA, Lum RF, et al. Association of PDCD1 genetic variation with risk and clinical manifestations of systemic lupus erythematosus in a multiethnic cohort. Genes Immun. 2007 Jun;8(4):279-87.

87. Reddy MV, Johansson M, Sturfelt G, Jonsen A, Gunnarsson I, et al. The R620W C/T polymorphism of the gene PTPN22 is associated with SLE independently of the association of PDCD1. Genes Immun. 2005 Dec;6(8):658-62.

75

88. Vang T, Congia M, Macis MD, Musumeci L, et al. Autoimmune-associated lymphoid tyrosine phosphatase is a gain-of-function variant Nat Genet. 2005 Dec;37(12):1317-9.

89. Bottini N, Musumeci L, Alonso A, Rahmouni S, Nika K, et al. A functional variant of lymphoid tyrosine phosphatase is associated with type I diabetes. Nat Genet. 2004 Apr;36(4):337-8.

90. Begovich AB, Carlton VE, Honigberg LA, Schrodi SJ, Chokkalingam AP, et al. A missense single-nucleotide polymorphism in a gene encoding a protein tyrosine phosphatase (PTPN22) is associated with rheumatoid arthritis. Am J Hum Genet. 2004 Aug;75(2):330-7.

91. Kyogoku C, Langefeld CD, Ortmann WA, Lee A, Selby S, et al. Genetic association of the R620W polymorphism of protein tyrosine phosphatase PTPN22 with human SLE. Am J Hum Genet. 2004 Sep;75(3):504-7

92. Kaufman KM, Kelly JA, Herring BJ, Adler AJ, Glenn SB, et al. Evaluation of the genetic association of the PTPN22 R620W polymorphism in familial and sporadic systemic lupus erythematosus. Arthritis Rheum. 2006 Aug;54(8):2533-40.

93. Baca V, Velazquez-Cruz R, Salas-Martinez G, Espinosa-Rosales F, et al. Association analysis of the PTPN22 gene in childhood-onset systemic lupus erythematosus in Mexican population. Genes Immun. 2006 Dec;7(8):693-5.

94. Mori M, Yamada R, Kobayashi K, Kawaida R, Yamamoto K. Ethnic differences in allele frequency of autoimmune-disease-associated SNPs. J Hum Genet. 2005;50(5):264-6.

95. Samuel CE. Antiviral actions of interferons. Clin Microbiol Rev. 2001 Oct;14(4):778-809

96. Kotenko SV, Gallagher G, Baurin VV, Lewis-Antes A, et al. Donnelly RP. IFN-lambdas mediate antiviral protection through a distinct class II cytokine receptor complex. Nat Immunol. 2003 Jan;4(1):69-77.

97. Dash S, Prabhu R, Hazari S, Bastian F, Garry R, Zou W, Haque S, et al. Interferons alpha, beta, gamma each inhibit hepatitis C virus replication at the level of internal ribosome entry site-mediated translation. Liver Int. 2005 Jun;25(3):580-94.

98. Ronnblom, L., Eloranta, M. L. & Alm, G. V. The type I interferon

system in systemic lupus erythematosus. Arthritis Rheum. 2006: 54, 408–420.

99. Braun D, Geraldes P, Demengeot J. Type I Interferon controls the onset and severity of autoimmune manifestations in Lpr mice. J Autoimmun 2003;20:15–25

76

100. Dall'era MC, Cardarelli PM, et al. Type I interferon correlates with serological and clinical manifestations of SLE. Ann Rheum Dis. 2005 Dec;64(12):1692-7

101. Fitzgerald-Bocarsly P. Natural interferon-alpha producing cells: the plasmacytoid dendritic cells. Biotechniques. 2002 Oct;16-20, 22, 24-9.

102. Ronnblom L, Forsgren A, Alm GV. Characterization of interferons induced by bacteria and interferon-producing leukocytes in human peripheral blood. Infect Immun. 1983 Apr;40(1):126-32.

103. Ronnblom L, Ojo-Amaize EA, Franzen L, Wigzell H, Alm GV. Plasmodium falciparum parasites induce interferon production in human peripheral blood 'null' cells in vitro. Parasite Immunol. 1983 Mar;5(2):165-72.

104. Hooks JJ, Moutsopoulos HM, Geis SA, Stahl NI, Decker JL, Notkins AL. Immune interferon in the circulation of patients with autoimmune disease. N Engl J Med 1979;301:5–8.

105. Ytterberg, S. R., and T. J. Schnitzer. Serum interferon levels in patients with systemic lupus erythematosus. Arthritis Rheum 1982: 25:401-416.

106. Kim, T., Y. Kanayama, N. Negoro, M. Okamura, T. Takeda. Serum levels of interferons in patients with systemic lupus erythematosus. Clin Exp Immunol 1987:70:562-569.

107. Bengtsson, A. A., G. Sturfelt, L. Truedsson, J. Blomberg, G. Alm, H. Vallin, and L. Rönnblom. Activation of type I interferon system in systemic lupus erythematosus correlates with disease activity but not with antiretroviral antibodies. Lupus 2000: 9:664-671.

108. Preble OT, Black RJ, Friedman RM, Klippel JH, Vilcek J. Systemic lupus erythematosus: presence in human serum of an unusual acid-labile leukocyte interferon. Science 1982;216:429–31.

109. Kalkner KM, Ronnblom L, Karlsson Parra AK, Bengtsson M, Olsson Y, Oberg K. Antibodies against double-stranded DNA and development of polymyositis during treatment with interferon. QJM 1998; 91: 393–399.

110. Ronnblom LE, Alm GV, Oberg K. Autoimmune phenomena in patients with malignant carcinoid tumors during interferon-alpha treatment. Acta Oncol 1991; 30: 537–540.

111. Gutterman JU. Cytokine therapeutics: lessons from interferon alpha. Proc Natl Acad Sci. 1994 Feb 15;91(4):1198-205.

112. Ronnblom LE, Alm GV, Oberg KE. Possible induction of systemic lupus erythematosus by interferon-�� treatment in a patient with a malignant carcinoid tumour. J Intern Med 1990;227:207–10.

113. Gota C, Calabrese L. Induction of clinical autoimmune disease by therapeutic interferon-� �. Autoimmunity 2003;36:511–8.

77

114. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, BanchereauJ, et al. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med 2003;197:711–23.

115. Han GM, Chen SL, Shen N, Ye S, Bao CD, Gu YY. Analysis of gene expression profiles in human systemic lupus erythematosus using oligonucleotide microarray. Genes Immun 2003;4:177–86.

116. Ye S, Pang H, Gu YY, Hua J, Chen XG, Bao CD, et al. Protein interaction for an interferon-inducible systemic lupus associated gene, IFIT1. Rheumatology (Oxford) 2003;42:1155–63.

117. Kirou KA, Lee C, George S, Louca K, Papagiannis IG, Peterson MG, et al. Coordinate overexpression of interferon—induced genes in systemic lupus erythematosus. Arthritis Rheum 2004;50: 3958–67.

118. Kirou KA, Lee C, George S, Louca K, Peterson MG, Crow MK Activation of the interferon-� pathway identifies a subgroup of systemic lupus erythematosus patients with distinct serologic features and active disease. Arthritis Rheum 2005;52:1491–503

119. Crow MK, Kirou KA, Wohlgemuth J. Microarray analysis of interferon-regulated genes in SLE. Autoimmunity 2003;36:481–90.

120. Crow MK, Wohlgemuth J. Microarray analysis of gene expression in lupus. Arthritis Res Ther 2003;5:279–87.

121. Feng X, Wu H, Grossman JM, Hanvivadhanakul P, et al. Association of increased interferon-inducible gene expression with disease activity and lupus nephritis in patients with systemic lupus erythematosus. Arthritis Rheum. 2006 Sep;54(9):2951-62.

122. Ning S, Huye LE, Pagano JS. Interferon Regulatory Factor 5 Represses Expression of the Epstein-Barr Virus Oncoprotein LMP1:Braking of the IRF7/LMP1 Regulatory Circuit. J Virol. 2005 Sep;79(18):11671-6

123. Aaronson DS, Horvath CM. A road map for those who don’t know JAK-STAT. Science 2002; 296: 1653–1655.

124. Tsu-Fan Cheng, Sabrina Brzostek, Osamu Ando, et al. Differential Activation of IFN Regulatory Factor (IRF)-3 and IRF-5 Transcription Factors during Viral Infection 1 The Journal of Immunology 2006, 176: 7462–7470.

125. Barnes, B. J., M. J. Kellum, K. E. Pinder, J. A. Frisancho, and P. M. Pitha. 2003. Interferon regulatory factor 5, a novel mediator of cell cycle arrest and cell death Cancer Res. 63: 6424–6431.

126. Takaoka,A., Yanai,H., Kondo,S., Duncan,G., Negishi,H., et al. Integral role of IRF-5 in the gene induction programme activated by Toll-like receptors. Nature.,2005Mar: 434(7030), 243-249.

127. Sigurdsson,S., Nordmark,G., Goring,H.H., Lindroos,K., et al. Polymorphisms in the tyrosine kinase 2 and interferon regulatory factor 5 genes are associated with systemic lupus erythematosus. Am J Hum Genet. 2005 Mar;76(3):528-37

78

128. Taniguchi, T., K. Ogasawara, A. Takaoka, and N. Tanaka. IRF family of transcription factors as regulators of host defense. Annu. Rev. Immunol. 2001:19: 623–655.

129. Harada, H., T. Fujita, M. Miyamoto, Y. Kimura, et al. Structurally similar but functionally distinc factors, IRF-1 and IRF-2, bind to the same regulatory elements of IFN and IFNinducible genes. Cell 1989:58: 729–739.

130. Cunninghame Graham DS, Manku H, Wagner S, Reid J,Timms K, Gutin A et al. Association of IRF5 in UK SLE families identifies a variant involved in polyadenylation. Hum Mol Genet 2007; 16: 579–591.

131. C Kyogoku1, and N Tsuchiya. A compass that points to lupus: genetic studies on type I interferon pathway. Genes Immun. 2007 Sep;8(6):445-55.

132. Honda K, Yanai H, Takaoka A, Taniguchi T. Regulation of the type I IFN induction: a current view. Int Immunol 2005; 17: 1367–1378.

133. Graham RR, Kyogoku C, Sigurdsson S, Vlasova IA, Davies LR, Baechler EC, Plenge RM, Koeuth T, Ortmann WA, Hom G, Bauer JW, et al. Three functional variants of IFN regulatory factor 5 (IRF5) define risk and protective haplotypes for human lupus. Proc Natl Acad Sci : 2007 Apr 17;104(16):6758-63.

134. Demirci FY, Manzi S, Ramsey-Goldman R, Minster RL, Kenney M, Shaw PS, Dunlop-Thomas CM, Kao AH, Rhew E, Bontempo F, Kammerer C, Kamboh MI. Association of a common interferon regulatory factor 5 (IRF5) variant with increased risk of systemic lupus erythematosus (SLE). Ann Hum Genet. 2007 May;71(Pt 3):308-11.

135. Reddy MV, Velazquez-Cruz R, Baca V, Lima G, Granados J, Orozco L, Alarcon-Riquelme ME. Genetic association of IRF5 with SLE in Mexicans: higher frequency of the risk haplotype and its homozygozity than Europeans. Hum Genet. 2007 Jul;121(6):721-7.

136. Ferreiro-Neira I, Calaza M, Alonso-Perez E, Marchini M, Scorza R, Sebastiani GD, Blanco FJ, Rego I, Pullmann R Jr, Pullmann R, Kallenberg CG, et al. Opposed independent effects and epistasis in the complex association of IRF5 to SLE. Genes Immun. 2007 Jul;8(5):429-38.

137. Shin HD, Sung YK, Choi CB, Lee SO, Lee HW, Bae SC. Replication of the genetic effects of IFN regulatory factor 5 (IRF5) on systemic lupus erythematosus in a Korean population. Arthritis Res Ther. 2007;9(2):R32.

138. Rueda B, Reddy MV, Gonzalez-Gay MA, Balsa A, Pascual-Salcedo D, Petersson IF, Eimon A, Paira S, Scherbarth HR, Pons-Estel BA, Gonzalez-Escribano MF, Alarcon-Riquelme ME, Martin J. Analysis of IRF5 gene functional polymorphisms in rheumatoid arthritis. Arthritis Rheum. 2006 Dec;54(12):3815-9.

79

139. Garnier S, Dieude P, Michou L, Barbet S, Tan A, Lasbleiz S, Bardin T, Prum B, Cornelis F.IRF5 rs2004640-T allele, the new genetic factor for systemic lupus erythematosus, is not associated with rheumatoid arthritis. Ann Rheum Dis. 2007 Jun;66(6):828-31.

140. Qu HQ, Marchand L, Grabs R, Polychronakos C. The IRF5 polymorphism in type 1 diabetes. J Med Genet. 2007 Oct;44(10):670-2

141. Sigurdsson S, Padyukov L, Kurreeman FA, Liljedahl U, Wiman AC, Alfredsson L, Toes R, Ronnelid J, Klareskog L, Huizinga TW, Alm G, Syvanen AC, Ronnblom L. Association of a haplotype in the promoter region of the interferon regulatory factor 5 gene with rheumatoid arthritis. Arthritis Rheum. 2007 Jul;56(7):2202-10.

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The Genetics of Systemic Lupus Erythematosusuu.diva-portal.org/smash/get/diva2:171099/FULLTEXT01.pdf · 2009. 2. 14. · The Genetics of Systemic Lupus Erythematosus The Specificity