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THIRD FACULTY OF MEDICINE CHARLES UNIVERSITY Division of Cell and Molecular Biology Institute of Biochemistry, Cell and Molecular Biology MOLECULAR-GENETIC STUDY OF POLYGENIC DISEASES WITH A SPECIAL FOCUS ON DIABETES MELLITUS Čejková Pavlína Ph.D. Thesis Prague, July 2nd, 2008 This thesis was created with the assent of Division of Cell and Molecular Biology, Institute of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University at Prague within the scope of Doctoral study of Biomedicine under the division council Molecular and Cell Biology, Genetics and Virology. Corresponding address of the author: Division of Cell and Molecular Biology Institute of Biochemistry, Cell and Molecular Biology Third Faculty of Medicine, Charles University Ruska 87 Prague 10 100 00 Tel.: +420 267 102 540 Fax: +420 267 102 650 Email: [email protected] Tutors: RNDr. Emanuel Žďárský, CSc. Doc. MUDr. Marie Černá, CSc. Division of Cell and Molecular Biology Institute of Biochemistry, Cell and Molecular Biology Third Faculty of Medicine Charles University ACKNOWLEDGEMENTS This study was performed during the years 2000-2008 in the laboratories of Division of Cell and Molecular Biology, Charles University in Prague, Czech Republic, and at the Department of Molecular Diagnostics, Roswell Park Cancer Institute, Buffalo NY, U.S.A. I am thankful to these institutions for providing excellent facilities and scientific background for my doctoral study. I would like to thank my tutors, RNDr. Emanuel Žďárský, CSc. and Doc. MUDr. Marie Černá, CSc., for giving me the unique opportunity for professional growth and for their extraordinary guidance and incentive advices during my doctoral study. My grateful thanks belong also to Petr Starostik, M.D., the chief of the Department of Molecular Diagnostics, RPCI, Buffalo NY, who was my supervisor during my two-year research experience at his lab. He showed me a beauty of cancer research and thought me the basics of scientific cogitation. Many thanks belong to all coauthors and colleagues from the Division of Cell and Molecular Biology for creating an excellent working atmosphere, and for their professional help and encouragement. My special thanks go to my family, my parents and sister above all, for their patience and support in the tough moments of this period. This study was financially supported by the Research Program of the Czech Ministry of Education and Youth: Identification code: MSM 0021620814: Prevention, diagnostics and therapy of diabetes mellitus, metabolic and endocrine damage of organism, and the Research Programs of Ministry of Health of the Czech Republic, IGA MZ CR: NB/7432-3/2003 and IGA MZ CR: NR/8191-3/2004. CONTENTS ACKNOWLEDGEMENTS .......................................................................................................................3 CONTENTS ................................................................................................................................................4 ABBREVIATIONS .....................................................................................................................................6 LIST OF TABLES ......................................................................................................................................9 LIST OF FIGURES .................................................................................................................................. 10 CHAPTER 1 ................................................................................................................................................1 OVERVIEW OF CURRENT KNOWLEDGE .........................................................................................1 1.1 POLYGENIC DISEASES............................................................................................. 2 1.2 CLASSIFICATION OF VARIATION ............................................................................. 3 1.2.1 Physiological variabilty.................................................................................................................. 3 1.2.2 Mutations.......................................................................................................................................... 9 1.2.2.1 Classification of mutations ................................................................... 9 1.2.2.2 Pathological changes in tumors ......................................................... 10 1.3 QUANTITATIVE TRAIT LOCI (QTL) ....................................................................... 13 1.3.1 Methods for QTL analysis .......................................................................................................... 15 1.3.1.1 Experimental species .......................................................................... 15 1.3.1.2 Mapping QTLs in humans .................................................................. 19 1.3.1.3 Advantages and drawbacks of linkage analysis and association study ............................................................................................................ 20 1.3.1.4 QTL in human cancer disease ............................................................ 21 1.4 DISEASES WITH UNKNOWN QTLS ........................................................................ 27 1.4.1 Enteropathy-type T-cell lymphoma ......................................................................................... 27 1.4.1.1 Classification of ETL .......................................................................... 27 1.4.1.2 Environmental risk factors ................................................................. 28 1.4.1.3 Genetic risk factors ............................................................................. 30 1.5 DISEASES WITH KNOWN QTL............................................................................... 35 1.5.1 Diabetes mellitus ........................................................................................................................... 35 4 1.5.1.1 Classification ...................................................................................... 35 1.5.1.2 Type 1 diabetes mellitus (T1D) ........................................................... 37 1.5.1.2.1 Incidence and prevalence of T1D ....................................................... 37 1.5.1.2.2 Immunology of T1D ............................................................................ 38 1.5.1.2.3 Environmental risk factors.................................................................. 39 1.5.1.2.4 Genetic risk factors .............................................................................. 41 1.5.1.3 1.5.2 Type 2 diabetes mellitus (T2D) ........................................................... 51 1.5.1.3.1 Incidence and prevalence of T2D ....................................................... 51 1.5.1.3.2 Metabolic syndrome............................................................................. 52 1.5.1.3.3 Environmental risk factors.................................................................. 52 1.5.1.3.4 Genetic risk factors .............................................................................. 54 Other autoimmune diseases........................................................................................................ 56 1.5.2.1 Systemic lupus erythematosus ................................................................................................... 56 1.5.2.2 Rheumatoid arthritis ........................................................................... 62 1.5.2.3 Psoriatic arthritis................................................................................ 65 1.6 THE JOINT MARKER OF ENTEROPATHY-TYPE T-CELL LYMPHOMA AND AUTOIMMUNE DIABETES MELLITUS ...................................................................... 68 CHAPTER 2 .............................................................................................................................................. 72 AIMS OF THE STUDY AND METHODOLOGY ................................................................................ 72 2.1 AIMS .................................................................................................................... 72 2.2 METHODOLOGY ................................................................................................... 73 CHAPTER 3 ............................................................................................................................................ . 74 RESULTS .................................................................................................................................................. 74 3.1 LIST OF ORIGINAL PAPERS .................................................................................... 74 3.2 COMMENTARY TO THE ORIGINAL PAPERS ............................................................. 76 3.3 ORIGINAL PAPERS ................................................................................................ 80 CHAPTER 4 ........................................................................................................................................ 15543 CONCLUSIONS ................................................................................................................................... 1553 REFERENCES........................................................................................................................................ 157 5 ABBREVIATIONS ABL1 Abelson murine leukemia viral oncogene homolog 1 A-CGH Array-based comparative genome hybridization ADA American Diabetes Association AGA Anti-gliadin antibody ALCL Anaplastic large cell lymphoma ANA Antinuclear autoantibody APC Antigen-presenting cell BAC-CGH Bacterial artificial chromosome-based comparative genome hybridization YAC-CGH Yeast artificial chromosome- based comparative genome hybridization BMI body mass index CAPN10 Calpain 10 CD Celiac disease cDNA-CGH cDNA- based comparative genome hybridization CF Cystic fibrosis CFTR Cystic fibrosis transmembrane conductance regulator CGH Comparative genome hybridization CTLA4 Cytotoxic T-lymphocyte associated protein 4 DM Diabetes mellitus DNA Deoxyribonucleic acid ddNTP Dideoxy nucleotide triphosphates EBV Epstein-Barr virus EMA Anti-endomysium antibody ETL Enteropathy-type T-cell lymphoma F1, F2 Filial generation 1, filial generation 2 FISH Fluorescein in situ hybridization FITC Fluorescein isothiocyanate GAD Glutamic acid decarboxylase 6 GADA Glutamic acid decarboxylase autoantibody HLA Human leukocyte antigen HNF Hepatocyte nuclear factor HS Heterogeneous stock Htg High-temperature growth HTLV-1 Human T-lymphotropic virus ICA Islet-cell autoantibody ICA512/IA-2 Protein thyrosine phosphatase IDDM insulin-dependent diabetes mellitus IEL Intraepithelial lymphocytes IGF2 Insulin-like growth factor 2 IL Interleukin KCNJ11 Potassium inwardly-rectifying channel, subfamily J, member 11 KIR Activating killer immunoglobulin-like receptor Kir6.2 Inwardly rectifying K+ channel LADA Latent autoimmune diabetes in adults LCR Low-copy repeats LD Linkage disequilibrium LINE Long interspersed nuclear elements LOH Loss of heterozygosity MHC Major histocompatibility complex MIC-A MHC class I chain – related gene A MODY Maturity-onset diabetes of young mRNA Messenger ribonucleic acid MSI Microsatellite instability NAHR Non-allelic homologous recombination NFKB1 Nuclear factor kappa B, subunit 1 NFKBIA Nuclear factor of kappa light chain gene enhancer in B–cells inhibitor α NHL Non-Hodgkin lymphoma NIDDM non-insulin-dependent diabetes mellitus NK cells Natural killer cells NKG2D C-type lectin like NK receptor NOTCH1 NOTCH, Drosophila homolog 1 PCR Polymerase chain reaction 7 PPARγ Peroxisome proliferator-activated receptor γ PsA Psoriatic arthritis PTPN22 Protein thyrosine phosphatase non-receptor type 22 QPCR Quantitative polymerase chain reaction QTL Quantitative trait locus RA Rheumatoid arthritis RB Retinoblastoma RCD Therapy-refractory celiac disease RNA Ribonucleic acid rRNA Ribosomal ribonucleic acid SINE Short interspersed nuclear elements SLE Systemic lupus erythematosus SNP Single nucleotide polymorphism SSR Simple sequence repeats STR Short tandem repeats SUR1 Sulphonylurea receptor-1 T1D Type 1 diabetes mellitus T2D Type 1 diabetes mellitus TALL T-cell acute lymphoblastic leukemia TCR T-cell receptor TGFβ Transforming growth factor β TNF Tumor necrosis factor TP53 Transcription protein 53 tRNA Transfer ribonucleic acid TSG Tumor suppressor gene tTG Tissue transglutaminase VNTR Variable number tandem repeats WHO World Health Organization 8 LIST OF TABLES Table 1: Number of recurrent chromosomal gains and losses published so far ............. 31 Table 2: Autoantigens in type 1 diabetes mellitus ......................................................... 39 Table 3: List of IDDM loci ............................................................................................ 42 Table 4: Some of the non-IDDM loci associated with both types of diabetes .............. 43 Table 5: HLA class II haplotypes associated with susceptibility for or protection against type 1 diabetes .................................................................................................. 46 Table 6: Environmental factors that may be relevant in the pathogenesis of systemic lupus erythematosus........................................................................................ 59 Table 7: Genes involved in human systemic lupus erythematosus ............................... 60 Table 8: List of some genes and potential candidates involved in etiology of rheumatoid arthritis ............................................................................................................ 65 9 LIST OF FIGURES Figure 1: Deletions, duplications, inversions and gene conversions ............................... 8 Figure 2: Comparison of cumulative data for human Mendelian trait genes and complex trait genes ......................................................................................... 14 Figure 3: Reciprocal hemizygosity ................................................................................ 18 Figure 4: Comparative genome hybridization – procedure ........................................... 22 Figure 5: Comparative genome hybridization – results................................................. 23 Figure 6: Fluorescence in situ hybridization – schematic illustration ........................... 24 Figure 7: Suggested classification of two ETL subtypes based on the clinical, morphologic, immunophenotypic and genetic features ................................ 34 Figure 8: Overview of the extended HLA complex on the chromosome 6 ................... 44 Figure 9: Insulin VNTR minisatellite repeats................................................................ 47 Figure 10: Insulin gene variants that affect pre-mRNA splicing................................... 48 Figure 11: Kir6.2/SUR1 pancreatic β-cell ATP-sensitive K+ channel complex ........... 62 Figure 12: Genomic organization of prolactin gene ...................................................... 70 Figure 13: Multistage development of ETL .................................................................. 68 10 CHAPTER 1 OVERVIEW OF THE CURRENT KNOWLEDGE The biological mechanisms that link together genetic variation and its phenotypic outcome have been for long decades one of the most challenging puzzles of biology, especially in the field of genetics. This is based on the faith that the genetic determinants behind complex traits are tractable and that knowledge of genetic variation and its contribution to a pathological phenotype will improve the diagnosis, treatment and prevention of a significant portion of cases of the diseases that constitute major public health burden of industrialized nations. (Un)fortunately, it is not only about gene(s) that contribute to a specific phenotype, but also some additive effects as well as epistatic interactions need to be taken into account since majority of the common human diseases, like a cancer, heart disease, diabetes, schizophrenia etc. have complex etiologies involving the action of many genes, as well as dynamic gene-environment interactions. To elucidate the mechanisms underlying disease susceptibility and progression and to improve diagnosis and treatment, an important strategy is to use proper genetic methods that enable identifying of the causative DNA variants, and to use this knowledge as the first step towards the eventual unraveling of the complex interplay between genes and environment. Geneticists have approached this problem by trying to uncover genetic variants that underlie the trait in question. It is possible to study both common diseases (because of their direct medical relevance) and phenotypic traits (because they are easier to accurately measure, often have a stronger genetic component, and often represent themselves risk factors for the disease). 1 1.1 POLYGENIC DISEASES A polygenic disorder (also termed multifactorial or complex) is caused by combined effect of multiple genes and lifestyle and environmental factors. Examples of polygenic diseases include coronary heart disease, hypertension, schizophrenia and diabetes. Compared to its counterpart - monogenic disorders, which are associated with defect in a single gene and whose inheritance follows simple Mendelian patterns, a pattern of inheritance of complex disorders is not explicit. It is thus difficult to determine person’s risk of inheriting or passing on such a disease. It is a simultaneous presence of several genes responsible for the disease manifestation and the fact that specific factors causing most of these disorders have not yet been identified what makes the complex diseases difficult to study and treat. Moreover, multiple genes participating in pathological phenotype may interact with each other with an unpredictable effect on the expression (epistasis). One of the basic problems may be the disease classification itself: Even single-gene disorders inheritance of which can be explained by the classical Mendelian laws may lack genotype-phenotype correlation and display complex phenotype. This discrepancy can be enlightened by a “threshold model” [1] suggesting a presence of other independently inherited factors, such as modifier genes, that influence the clinical phenotype by turning "simple" Mendelian disorders into complex traits. This phenomenon may perhaps be documented by cystic fibrosis, an autosomal recessive disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR). The phenotypic differences described in this illness seem to be caused by modifier genes influencing the function of CFTR gene and thus also patients’ phenotypes. 2 1.2 CLASSIFICATION OF VARIATION 1.2.1 Physiological variability Variation at the DNA level is the basic source of phenotypic diversity. A DNA sequence polymorphism is considered any variation present in the population at frequency greater than 1% [2]. Although the majority of DNA changes have no impact on the final protein structure and function, there are some that result in changes in the quality or quantity of expressed protein. Until recently interest was focused only on the polymorphisms located in the coding regions. Nowadays, however, there is a growing evidence that regulatory polymorphisms in the non-coding DNA sequences located outside exon regions modulate gene expression and even more, may be involved in processes of evolutionary changes [3]. Regulatory polymorphisms may be “cis-acting” or “trans-acting” depending on the copy of gene where they are located relate to affected gene. The cisacting polymorphisms are acting on the copy of the gene present on that allele and typically, they can be found in or near the locus of the gene that they regulate. For example, sequence in gene promoter is changed and this alters the binding affinity of transcription factor, which in turn regulates expression of the gene. In contrast, the trans-acting polymorphism in one gene regulates the expression of another gene that can be located even on a different chromosome. As an example a gene encoding some transcription factor that regulates the expression of other gene can serve. Single nucleotide polymorphism (SNP) The most common polymorphisms are single nucleotide polymorphisms (SNPs); they have one of four possible nucleotides substituted for another. SNPs occur approximately every 800 nucleotides across the genome [2]. Simple substitution of one or more nucleotides in a sequence may change the amino acid in resulting protein depending on the position of nucleotide in a triplet; substitution of the third nucleotide is usually silent and does not express due to a degeneracy of the genetic code. Overwhelming variability at the DNA level represented mainly by single nucleotide polymorphisms is of a great importance even in an evolutionary context. Increased levels of genetic variation between alleles or haplotypes in a species can be found in a significant 3 rate. There is a form of natural selection – called balancing selection – that works to maintain such a genetic polymorphism (or multiple alleles) within a population. A balanced polymorphism is a situation in which balancing selection within a population is able to maintain stable frequencies of two or more phenotypic forms. The reason for maintaining genetic polymorphism in such a considerable rate is that it brings a selective advantage at both, population and individual levels. As an example, for the huge diversity of human leukocyte antigens (HLA), which belong to the most polymorphic system in human genome, is believed to be responsible the positive selection of new alleles that confer the advantage of heterozygosity thus increasing the ability to optimally bind a broader range of epitopes and keep up with the evolution of pathogens. Two of several major mechanisms by which natural selection preserves this variation and consequently may produce a balanced polymorphism, are best understood: (A) heterozygote advantage (overdominance) and (B) frequency-dependent selection. (A) In heterozygote advantage, an individual who is heterozygous at a particular gene locus has a greater fitness than a homozygous individual. A well-studied case of heterozygote advantage is that of sickle cell anemia (SCA). Individuals who are homozygous for the recessive allele at this locus are inflicted with sickle-cell disease, a dissease in which red blood cells are grossly misshapen and which often results in a reduced lifespan. Individuals heterozygous at this locus will not suffer from sickle-cell disease but because of slightly irregularly shaped blood cells they are resistant to malaria. This resistance is favored by natural selection in tropical regions where malaria is present and so the heterozygote has an evolutionary edge. It is in this way that natural selection preserves stable frequencies of both the heterozygote and the homozygote dominant phenotypes. Cystic fibrosis (CF), hereditary disease of the lungs, sweat glands and digestive system, may serve as another example. It was shown that the presence of a single CFTR mutation (heterozygous state) may influence survivorship of people affected by diseases involving loss of body fluid, typically due to diarrhea. The most common of these maladies is cholera, which throughout history has killed many Europeans. A mouse model of CF was used to study resistance to cholera, and the results were published in Science in 1994 [4]. Nevertheless, recent work evaluating candidate agents of selective pressure for CF [5] suggests that from the three historically most probable death-causing epidemics, 4 cholera, typhoid and tuberculosis (TB), it was the latter one that enabled to explain the CF gene persistence. And last but certainly not least example of advantageous heterozygosity represents a certain mutation within triose phosphate isomerase (TPI), central enzyme of glycolysis. In humans, this mutation affects the dimerisation of the protein and is causal for a rare disease, triose phosphate isomerase deficiency. However, cells having reduced TPI activity are more resistant against oxidative stress. Frequency-dependent selection is a form of selection in which the relative fitness of a specific phenotype declines if the frequency of that phenotype becomes too high. An example of this type of selection is between parasites and their hosts: A certain parasite can recognize one of two receptors in its host, receptor a or receptor β. If many parasites with receptor a exist then hosts with receptor β will be selected for, and this will subsequently increase the selective pressure on parasites using receptor β. This relationship will continue rocking back and forth. Frequency-dependent selection has been observed in the banding and color polymorphism in the European land snails, Cepaea nemoralis where thrushes preferentially predate the most common morph, and also appears in the form of mate preference, a type of sexual selection. Repetitive elements Other factors that contribute to a considerable proportion of variability of a human genome are repetitive elements that make up over 45% of the human genome [6]. The term “repetitive elements” stands for various DNA sequences that are present in multiple copies in the genomes. They can be subdivided into tandemly repeated like microsatellites, minisatellites and telomeres; or interspersed, for example mobile elements (e.g. retroelements) and processed pseudogenes. · Tandem repetitions o Microsatellites Microsatellites are short tandem repeated motifs of one to six nucleotides (Simple Sequence Repeats, SSRs, or Short Tandem Repeats, STRs). SSRs comprise about 3% of the human genome, with the greatest single contribution coming from dinucleotide repeats (0.5%). The most frequent dinucleotide repeats are AC and AT (50% and 35% of 5 dinucleotide repeats, respectively) [7]. The repeating number of sequence unit can be highly variable (10 to more than several hundreds), which is a characteristic that makes microsatellites useful as genetic markers used for identification of a specific chromosome or locus. The microsatellites are thought to arise by slippage during DNA replication [8, 9]. The majority of them occur in introns or other non-coding regions of the genome. Since usually many alleles are present at a microsatellite locus, the microsatellite repeats may be used in pedigrees for paternity testing, in forensic genetics to trace inheritance patterns [10], for population genetic studies, linkage analysis and recombination mapping [11]. o Minisatellites (VNTR) Longer tandem repeats are termed minisatellites or variable number tandem repeats, VNTRs; their variant repeat units range in length from 10 to over 100 nucleotides and span 500 – 20.000 bp [6]. Similarly to microsatellites, the extreme polymorphism of minisatellites has been extensively used for DNA fingerprinting [12] as well as for genetic markers in linkage analysis and population studies. Moreover, minisatellites have been intensively investigated in studies focused on mechanisms that evoke their instability. · Interspersed repetitive elements Recent analyses show that more than half of the human genome may consist of interspersed repetitive element [7, 13]. They can be subdivided based on their size, with short interspersed nuclear elements (SINEs) being less than 500 bp long [6] and long interspersed nuclear elements (LINEs) reaching more than 6-8 kb in size [14]. o Short Interspersed Nuclear Elements SINEs represent reverse-transcribed RNA molecules originally transcribed by RNA polymerase III into rRNA, tRNA and other small nuclear RNAs. The most abundant SINEs in human chromosomes are mobile Alu elements. They represent around 10% of all SINEs [7, 15]. Alu elements are about 300 bp long and are commonly found in introns, 3′ untranslated regions of genes and intergenic regions and do not contain any coding sequence. They can be recognized by the restriction enzyme AluI (thus the name). Previously, Alu repeats were believed to be "junk DNA", however, recent research suggests they have a significant role in gene evolution, structure and transcription levels [16]. At the 6 molecular level, Alu elements are capable of insertion mutations, recombination between elements, gene conversion and gene expression by altering the distribution of methylation of genes throughout the genome. Based on the above, it is not surprising that Alu repeats have been proposed to predispose the genome to instability [15] and their distribution seems to be implicated in some genetic diseases including breast cancer, acute myelogenous leukemia and Ewing sarcoma, but also neurofibromatosis, hemophilia, familial hypercholesterolemia and T2D [6]. o Long Interspersed Nuclear Elements Similarly to SINEs, LINEs constitute a considerable fraction of the human genome (about 17%) [7, 14]. LINEs represent reverse-transcribed RNA molecules originally transcribed by RNA polymerase II into mRNA. They code for 2 proteins; one that has the ability to bind single stranded RNA, and another one with known reverse transcriptase and endonuclease activity enabling them to copy both themselves and non-coding SINES such as Alu elements [17]. It was shown that LINEs can generate processed pseudogenes [18]. It is now recognized that chromosomal sequence context plays a role in monoallelic gene expression and may involve the recognition of long repeats or other features, since LINEs were found significantly less truncated and more abundant around random monoallelically expressed genes [19]. There are two major LINE families in mammalian genome: an old LINE family, called LINE2, which apparently stopped transposing before the mammalian radiation, and a younger family, called L1 or LINE1. Many members of L1 were inserted after the mammalian radiation (and are still being inserted). A human genome contains about 60.000 to 100.1000 L1 elements. · Segmental duplications ~ Low-Copy Repeats In contrast to SINE and LINE repeats for which there are thousands of copies in the genome, segmental duplications or low-copy repeats (LCR) occur, as the name says, only in few copies. They are blocks of the genome that have been duplicated, once or several times. The LCRs can contain genes; such genes are as a result of the duplications members of a gene family with high homology between the individual members. The first LCRs identified were very large (in contrast to microsatellites and LINE repeats; LCRs may reach up 1 - 200 kb in size [7]) but as the technology is advancing, smaller LCRs are also being 7 identified. Because of their size and a high degree of sequence homology (95-97%) [20], LCRs are prone to non-allelic homologous recombination (NAHR) leading to diseasecausing DNA rearrangements such as deletions (e.g. male infertility), duplications (e.g. Charcot-Marie Tooth disease - a peripheral neuropathy), inversions (e.g. Haemophilia) and gene conversions (generating pathogenic mutations by introducing non-functional mutations from a pseudogene into an expressed gene (e.g. Congenital adrenal hyperplasia)) [20-22]. Figure 1: Deletions, duplications, inversions and gene conversions. Nevertheless, the gene conversions and rearrangements generated by NAHR do not need to be pathogenic; they contribute to sequence variation and structural polymorphism in the human genome. 8 RNA variability Next to DNA, mRNA variation is being intensively studied. Since gene expression is the primary mechanism how the information encoded in genome projects into phenotype, it is of a fundamental importance to understand how gene-expression variability influences pathogenesis of numerous diseases. Variability in the gene expression has already been used as a quantitative trait for linkage disequilibrium analysis in human lymhoblastoid cell line (Morley et al., 2004). The approach that is called “genetical genomics” experiences considerable boom in these days. In genetical genomics, expression levels are treated as quantitative phenotypes and genetic variants that influence gene expression are sought [23, 24]. 1.2.2 Mutations Genetic variation may under some circumstances cause disease development. Pathological changes in genetic information are called mutations. Mechanisms similar to those responsible for physiological DNA variability are applied in induction of mutations; however, the effect is in the majority of cases pathological. 1.2.2.1 Classification of mutations There are many ways of classification. The basic ordination compromises three main categories [25]: (A) Genome mutations that affect the number of chromosomes in the cell; (B) chromosome mutations altering the structure of individual chromosomes; and finally (C) gene mutations responsible for alterations of individual genes. A. Alteration in number of intact chromosomes (aneuploidy) is a consequence of nondisjunction, the failure of chromosome pairs to separate properly during cell division. B. Large-scale mutations in chromosomal structure can occur spontaneously or may result from abnormal segregation of translocated chromosomes during meiosis. They include: o Amplification (duplication) o Deletions of large chromosomal regions o Chromosomal translocations 9 o Inversion and interstitial deletion o Loss of heterozygosity (loss of one allele in an individual who previously had two different alleles, either by a deletion or recombination event; LOH) C. Small-scale mutations affecting a gene in one or a few nucleotides. These mutations involve: o Single nucleotide substitution (point mutation, exchange of one nucleotide for another) § Silent mutation (no effect on the coding amino acid) § Missense mutation (alteration of the sense of the gene coding strand by specifying a different amino acid) § Nonsense mutation (chain termination mutation; it generates one of the three “stop” codons into the sequence) o Insertion (addition of one or more extra nucleotides into the DNA): shift of reading frame, splice site mutation o Deletion (removal one or more nucleotides from the DNA): alteration of reading frame, splice site mutation Based on the cause two classes of mutations are known, spontaneous and induced, which can be further distinguished by the nature of inductor to physical, chemical or biological. Other classifications point out the effect on function (loss-of-function mutations, gain-of-function mutations, dominant negative mutations, lethal mutations); highlight the aspect of affected phenotype (morphological mutations vs. biochemical mutations), or underline the cause of inheritance: o Wild type or homozygous non-mutated (neither allele is mutated) o Heterozygous (only one allele is mutated) o Homozygous (both the paternal and maternal alleles have an identical mutation) o Compound heterozygous mutations (genetic compound; paternal and maternal alleles have two different mutations) 1.2.2.2 Pathological changes in tumors Compared to non-cancer complex diseases (type 1 diabetes, atherosclerosis, obesity, coronary heart disease, hypertension, schizophrenia and many other), tumor development 10 generally undergoes a characteristic process. Three types of genes are involved in the tumorigenesis: proto-oncogenes, tumor suppressor genes (TSG, anti-oncogenes), and mismatch repair genes. Activation of these key players happens as a consequence of various mutations. Chromosomal aberration is defined as a condition that leads to chromosome rearrangement affecting both chromosome structure and chromosome number. Such chromosomal changes cause gain or loss of chromosomal material [26]. Although this specific condition may affect whatever DNA region, microsatellites pay for their variability by increased rate of mutations compared to other DNA regions. This high rate of mutations is explained by slipped strand mispairing (slippage) during DNA replication or by errors during recombination during meiosis [7, 27-29]. Recently, impact of low-copy repeats on the generation of balanced and unbalanced chromosomal aberrations has been recognized. Homologous recombinations have been proposed to be a major cause of loss of heterozygozity involving tumor suppressor genes and its significance and frequency may be more central to the process of carcinogenesis than described previously [30-32]. It has been also speculated that at least in some case, NAHR involving LCRs or other repeats may also be responsible for (onco)gene amplification [33]. Homologous recombination also plays a fundamental role in the somatic cells of the body during the repair of damaging DNA lesions known as Double Strand Breaks (DSB) and in various stages in carcinogenesis by implementing recombination events leading (next to deletions, translocations and gene conversion) possibly also to telomere maintenance [34, 35]. There are two main types of mechanisms that activate proto-oncogenes: 1) Specific gene mutations (SNP, point mutation; affects oncogenes from ras family: H-ras, K-ras, N-ras) 2) Chromosomal aberrations (specific for a given tumor) a. Amplification (gain); duplication or multiplication of chromosomal material at a given locus causing increase in number of proto-oncogene copies per genome b. Translocation The most frequent tumor types fated by the above described pathological changes are leukemias and lymphomas. 11 Many malignancies grow up after repression of anti-oncogenes. The following mechanisms can inactivate them: 1) Specific gene mutations (deletion, SNP - either inherited or de novo) 2) Chromosomal aberrations a. Chromosome loss b. Translocation The most frequent tumor types having TSGs with altered (decreased) or no function, are solid tumors. Since the TSGs manage to sustain its function even in a recessive state (in heterozygotes), the two hit hypothesis has been postulated explaining tumor development [36]. Normal cells possess mechanisms for DNA reparation and mechanisms enabling the cell to destroy itself by apoptosis if DNA damage is too severe. Genes, whose protein products are involved in DNA replication or code for enzymes responsible for repair of base-pair mismatches introduced during DNA replication, are termed mismatch repair genes (or “caretakers”). Mutations that repress these genes can also eventually lead to cancer. Nevertheless, a mutation in one oncogene or one tumor suppressor gene is usually not enough for a tumor to occur and a combination of a number of mutations is necessary. 12 1.3 QUANTITATIVE TRAIT LOCI (QTL) Many complex phenotypes (e.g. height, weight, intelligence) are quantitative and express continuous variation. Such a quantitative trait is controlled by multiple genes and therefore is called multigenic or genetically complex. Such a complex genetics of quantitative traits requires different approaches than the study of diseases caused by monogenic traits. Genes that contribute to complex traits bring special challenges that make gene discovery more difficult: locus heterogeneity1, epistasis2, low penetrance3, variable expressivity4 and pleiotropy5, limited statistical power [37, 38]. A quantitative trait locus (QTL) is a term defining a region of a genome that is responsible for variation in the quantitative trait of interest. Although identifying all such regions that are associated with a specific complex phenotype might seem quite simple, especially with all the genomic and computational tools available, the task is rather difficult. Not only the entire number of QTL complicates the situation, but also the above mentioned additional sources of variation bedevil the search. Ere now, more or less only monogenic traits supplied encouraging results in associating genotype with phenotype not only in humans [39] but also in animals [40] or plants [41]. The following figure (from Glazier et al., [42]) indicates how different is a situation in identification of genes controlling Mendelian traits versus those underlying genetically complex traits in the last 20 years. 1 A single disorder, trait, or pattern of traits caused by mutations in genes at different chromosomal loci. Retinitis pigmentosa may have autosomal dominant, autosomal recessive or X-linked origin 2 Term used for interaction between genes/loci; epistasis occurs when the action of one gene is modified by one or several other genes (sometimes called modifier genes). The combined phenotypic effect of two or more genes/loci exceeds the sum of effects at individual genes/loci. 3 Penetrance is the proportion of individuals with a given genotype that express it at the phenotype level 4 Term coined for the level of phenotypic expression for a particular genotype 5 The situation in which a single gene affects two or more characteristics 13 Figure 2: Cumulative data for human Mendelian trait genes (to 2001) include all major genes causing a Mendelian disorder in which causal variants have been identified. This reflects mutations in a total of 1336 genes. Complex trait genes were identified by the whole-genome screen approach and denote cumulative year-on-year data described in this review. From Glazier, 2002 As already emphasized, genetic background of complex disease or trait cannot be explained by single causative gene. Instead of that, terms like candidate genes (loci), genetic linkage, association and linkage disequilibrium are frequently used. Genetic linkage occurs when particular genetic loci or alleles are inherited jointly. Genetic loci on the same chromosome are physically connected and tend to segregate together during meiosis and are thus genetically linked. Gene alleles localized on different chromosomes are usually not linked, due to independent assortment of chromosomes during meiosis. Genetic linkage is measured by frequency of recombination between the two loci. Linkage disequilibrium (LD): Is defined as non-random distribution of alleles at two (or more) loci. In other words, LD describes situation when some combination of alleles or genetic markers occurs more or less frequently in population than it would be expected if the formation of haplotypes from these alleles or markers is random. LD is generally caused by genetic linkage and rate of recombination, but also by microevolution process in 14 population (mutation rate, random drift, natural selection, non-random mating, and population structure). Genetic association: When we cannot prove the causality of some gene(s) but we do observe its/their presence together with some trait (they occur in the population simultaneously) more often than could be explained by the chance, we talk about the association of a given gene(s) with a trait or disease in question. Association between the two genetic traits is called genetic linkage. 1.3.1 Methods for QTL analysis The primary and major difficulty in identifying QTLs is that any single QTL explains only a small fraction of the phenotypic variation and thus the phenotype–genotype correlation is low. To identify chromosomal regions that affect the complex traits, various approaches were implemented that may be divided into two basic stages [43]: The first stage involves the QTL identification based on phenotype and genotype and correlation of gene markers (e.g. DNA polymorphisms as SNPs or microsatellites) with a phenotype is carried out. According to Darvasi and Pisante-Shalom, this stage consists of three subsequent steps, suggested accordingly also by others [42]: (a) QTL detection, (b) QTL mapping and (c) QTL fine mapping [43]. The main aim is to narrow down the region correlating with the trait in question so that it contains as few genes as possible. The analysis of previously identified QTL down to a single gene is a second stage, which can be performed e.g. by functional assays; they provide an evidence that variation at a particular gene has an effect on phenotype. 1.3.1.1 Experimental species The fact that genetic loci contributing to quantitative traits have only small effect on phenotype slows down the progress in identifying single genes. First, association studies seemed to be the most appropriate method for finding the genes that influence complex traits [44]. However, family-based studies have to be very large to provide the resolution that is needed for positional cloning (mapping a disease gene to 1cM (or approximately 1Mb) will require a median of 700 sib pairs if the gene causes a twofold increase in risk to offspring [45]), and population-based association studies may be confounded because of 15 environmental or genetic differences between cases and controls [46]. These difficulties have led to the study of animal models of human traits. Indispensable role in QTL detection play mice and other experimental species (both plants and animals); works made on Drosophila melanogster or even yeasts contributed greatly to genetic research of complex traits as well, especially in the methodological area [47, 48]. Regardless of all methods, however, resolution is limited by recombination [49]. Recombination is the natural crossing over between the two copies of each chromosome that occurs during meiosis; it is important for mapping, because it breaks down the linkage between QTL and molecular markers on the same chromosome. The result is that markers closer to a QTL tend to show greater association with the trait of interest than markers that are further away. If there have been many generations of recombination, only markers that are extremely close to the QTL will show association with phenotype. Nevertheless, in most human pedigrees, it is impossible to get sufficient number of recombination events, which causes poor resolution of the trait locus position [50]. That is why experimental animals such as mice are intensively studied: Homozygous inbred parental lines are being crossed to create F1 population heterozygous at all markers and QTLs. F1 population is then used for further crosses such as backcross, F2 intercrosses and crosses to generate recombinant inbred lines that serve for statistical analysis between DNA polymorphic traits and phenotypes. Nevertheless, although the inbred lines mating is advantageous compared to study of QTLs directly in humans, it has its drawback as well. When inbred lines are used for QTL mapping, only limited number of crosses in which recombination may occur is available (in F2 cross there is only one generation in that recombination may happen and thus disrupt the linkage between QTL and nearby marker). In an attempt to circumvent the difficulties encountered with inbred crosses, Mott and colleagues used a genetically heterogeneous stock (HS) of mice for which the ancestry was known [51]. Their heterogeneous stock was established from cross of eight inbred lines 30 years ago and thus each chromosome from an HS animal represents a fine genetic mosaic of the founder strains. Mapping small-effect QTLs with a high degree of precision was enabled by another approach that used commercially available outbred mice [52]. As already mentioned, an advantage of HS and outbred strain is that many previous generations of recombination 16 effectively break down the linkage between QTLs and their neighboring markers. However, there is a problem with studying marker–phenotype association in outbred strains because a given marker allele may be located next to different QTL alleles and vice versa. To evade this potential weakness of the method, Yalcin et al. took an advantage of the knowledge that MF1 outbred mice can be treated as mosaics of standard inbred strains and instead of analyzing a single SNP they analyzed the association between phenotype and haplotypes (sets of closely linked SNPs) of commonly used inbred strains in order to model the origins of the SNP alleles in MF1 outbred population [52]. The QTL fine-mapping was carried out using HAPPY software, as progenitors were used eight inbred strains. A final step in QTL investigation and the stage 2 according to Darvasi and PisanteShalom [43] should be the functional study that confirms the candidate gene(s) of the detected QTL. To approaches that can be used for functional validation of suspected gene(s) belong among others altering gene expression (either knockout or transgenic) and quantifying natural gene-expression levels. Novel method to resolve the underlying genetic architecture of QTLs was demonstrated in 2002 year in yeasts [48]. First, by strategy called selective genotyping the authors detected regions where studied QTL (high-temperature growth, Htg) was mapped. Nevertheless, neither detailed sequence analysis of several strains nor a comparison of mRNA transcript of the parental strains led to any result in fine mapping. This was predictable because in quantitative complex traits, a single variation will probably never be sufficient to display a given phenotype. Since functional validation of the designated genomic region was still missing the authors established a novel approach termed reciprocal-hemizygosity. They produced pairs of heterozygous strains from the original Htg+ and Htg− parents, each member of a pair carrying reciprocal short deletions within the candidate interval (Figure 3). 17 Figure 3: Reciprocal hemizygosity is the novel strategy introduced by Steinmetz et al. 2002. To examine the high temperature growth phenotype, hybrids are formed between an Htg+ strain (red) and an Htg− strain (blue). Each block represents a gene in the region identified by quantitative trait analysis as containing genes that affect the Htg phenotype. A pair of hemizygote hybrids is made for each gene in the region: one with a gene deleted from the chromosome of Htg+ origin (Htg+Δ) and one with a gene deleted from the chromosome of Htg− origin (Htg−Δ). By deleting the gene in question from the Htg+ and the Htg− parents, and replacing it with two different dominant drug markers (white boxes), the two hemizygotes can be distinguished. From Darvasi and Pisante-Shalom, 2002 [43] With reciprocal-hemizygosity analysis, it is tested whether an allele from one genetic background was advantageous over that from the other by measuring competitive growth between reciprocal strains. This approach thus allows observing the effect of individual alleles in an otherwise uniform genetic background. Another method was applied by Yalcin and co-workers [52]. They identified the candidate genes by technique termed as quantitative complementation testing, originally used with Drosophila melanogaster [47]. The test was re-named by Darvasi to “QTLknockout interaction test” [53] since it better describes its principle. The test requires a strain carrying a mutation at a known candidate gene, a strain with wild type sequence in that gene, and strains with different QTL alleles. Four combinations are generated by 18 crossing the lines and the individuals are measured for the trait of interest. If the gene underlying the QTL is not the same as the knocked-out gene and the two genes do not interact, then there will be an effect of the QTL and potentially of the mutation, but these two effects will be independent and the interaction between the two factors will not be significant. In contrast, a significant interaction indicates that the gene underlying the QTL is the same as the known candidate gene (allelism), or that the two genes interact (epistasis). However, this test does not distinguish between these two possibilities. So, the “QTL-knockout interaction approach” tests the presence or absence of an interaction effect between the null allele and the QTL. 1.3.1.2 Mapping QTLs in humans Although in human linkage disequilibrium has been successfully used for mapping simple Mendelian traits, it has been suggested that under certain settings even quantitative trait loci may be mapped using LD [54]. The authors proposed the use of threshold-defined cases for mapping loci influencing quantitative traits using LD at the population level. Two approaches were suggested by Tenesa and.colleagues: Marker-based versus trait-based method [50]. These two methods differ in the way how they deal with the phenotype factor. Trait-based (dichotomizing) approach uses the phenotypic information only for the selection step and thus creates two populations to be compared: one that has a trait and the other that does not. Since the trait is continuous, only subjects who fall below some defined cut-off are treated as positive (e.g. suffering from the disease; cases) and the rest as negative (controls). The advantage of such a dichotomizing approach is that it resembles the traditional disease mapping methods and similar ways of interpretation can be applied. However, the information within the two classes is lost and the price paid – in terms of loss of statistical power – might be very high. The trait-based approach for mapping quantitative trait loci using LD is being frequently applied in association studies. In the non-dichotomizing (in Tenesa et al. termed marker-based) approach is the mean phenotypic value (quantitative trait) for each marker allele or genotype compared in the same individuals. The study showed that significant amount of information is lost when analyzing a quantitative trait as a threshold-defined binary trait, as opposed to analyzing it using all the information available. The lost information is reflected in a loss of statistical power to detect an association between marker genotype and phenotype. 19 1.3.1.3 Advantages and drawbacks of linkage analysis and association study Linkage analysis and association studies represent two major approaches that have been used to map genetic variants influencing disease risk. In linkage analysis, a genome-wide set of a few hundred or a few thousand markers spread millions of bases apart is typed in families with multiple affected relatives. Markers that segregate with the disease/trait in relatives more often than expected are than used for localization of the disease genes. This approach is considered to be unbiased and comprehensive search across the genome for susceptibility alleles. Although it has been successfully applied to find the genes for many single-gene disorders, linkage analysis has been much less successful for polygenic diseases and quantitative traits [38]. This may be perhaps because of a limited power to detect the effect of common alleles with modest effect on the disease. Association studies look for a particular marker and correlate it with a disease (or trait values) across a population rather than within families. For example, the insulin VNTR class I allele, which has a frequency of approximately 29%, has definitively shown to modestly affect the risk of type 1 diabetes, using association studies [55]. Association studies require many more markers than linkage analysis and have much bigger power than linkage analysis; they require that the markers are in linkage disequilibrium with a disease allele, whereas in linkage analysis it is sufficient to have markers only in a linkage with the disease allele. The association studies involve a simple comparison of the frequencies of an allele or haplotype between individuals with and without a trait of interest. If compelling evidence for frequency differences exists, then either the locus (or loci) in question harbors alleles that causally or directly influence the trait, or the alleles are in linkage disequilibrium with alleles at a neighboring locus that directly influence the trait in question. Nevertheless, despite the bigger power, the association studies still require huge sample size (thousands of individuals) and thus they are currently limited to candidate genes or regions. The search throughout the genome would be too expensive. 20 1.3.1.4 QTL in human cancer disease Detection of QTLs and sequentially genes involved in tumorigenesis (process of tumor formation in a body) engage a whole cascade of methods. In general, similarly to what was described up to here, it covers an identification of affected chromosome (1) followed by delineation and downsizing of the region (2), to finally map and identify the candidate gene(s) (3). Methods most frequently used for searching genes participating in pathological cell growth classified according to their order in the multi-step process are: 1) 1) CGH and its modifications, FISH 2) Microsatellite analysis, SNaPshot analysis, gDNA quantitative PCR 3) Sequencing, expression and functional studies Methods listed in step 1 are suitable for the primary superficial location of mutation, with a resolution at a chromosome/chromosomal band level. Comparative Genome Hybridization (CGH) is a molecular-cytogenetic technique for analysis of copy number changes (gains/losses) in the DNA content of tumor cells. The method is based on the hybridization of fluorescently labeled tumor (frequently with Fluorescein - FITC) and normal DNA (frequently with Rhodamine or Texas Red) to normal (meaning diploid) human metaphase chromosome. After in situ hybridization, regional differences in the fluorescence ratio of tumor vs. control DNA corresponding to the loss or gain of genetic material along the length of chromosome can be detected by microscope. CGH will detect only unbalanced chromosomal changes; structural aberrations such as balanced reciprocal translocations and inversions can not be detected. CGH has limited mapping resolution (approximately 20 Mb) therefore modifications of the hybridization method have been recently invented that help in the routine research praxis to track down smaller and smaller DNA copy number variations associated with a wide range of diseases, from developmental disorders to cancer. These array-based comparative genome hybridization techniques (ACGH) use probes (printed in on a glass slide) to which the DNA hybridizes. The size of the probes determines the resolution: BAC-CGH (Bacterial Artificial Chromosome-based comparative genome hybridization), YAC-CGH (Yeast Artificial Chromosome) and cDNA-CGH use for physical mapping genomic DNA sequences cloned in vectors such as cosmids, bacterial artificial chromosomes (BACs) or yeast artificial chromosomes (YACs) 21 and thus enable to detect chromosome copy number changes at a higher resolution level (~ 0.5 Mb) than conventional chromosome-based CGH. Figure 4: Comparative genome hybridization - procedure 22 Figure 5: Comparative genome hybridization - results. Green lines on the right side of chromosomes represent amplification of genetic material at a given region (overrepresented loci); red lines on the left are deleted (underrepresented) regions; yellow rectangles: high level of amplification Fluorescence in situ hybridization (FISH) employs fluorescently labeled sequences of single-stranded DNA (probes), which are complementary to the DNA sequences the researchers wish to visualize and examine. The technique provides similar results as CGH but with higher resolution. Moreover, compared to CGH, it can recognize balanced reciprocal translocations. Unlike most other techniques used for chromosomes study that require the cells to be actively dividing, FISH can be performed on non-dividing cells, making it a highly versatile procedure. 23 Figure 6: Fluorescence in situ hybridization – schematic illustration There are three different types of FISH probes, each of which has a specific application: Whole chromosome probes are actually collections of smaller probes, each of which hybridizes to a different sequence along the length of the same chromosome. Using these libraries of probes, it is possible to paint an entire chromosome and generate a spectral karyotype. This full color image of the chromosomes allows distinguishing between the chromosomes based on their colors, rather than based on their dark and light banding patterns, viewed in black and white through traditional karyotyping. Whole chromosome probes are particularly useful for examining chromosomal abnormalities, e.g. when a piece of one chromosome is attached to the end of another chromosome. Alphoid or centromeric repeat probes are generated from repetitive sequences found at the centromeres of chromosomes. Because each chromosome can be painted in a different color, this technique serves to determine whether an individual has the correct number of chromosomes or, for example, whether a person has an extra copy of a chromosome. Locus specific probes hybridize to a particular region of a chromosome. This type of probe is useful when a small portion of a gene has been isolated and used to determine on which chromosome that gene is located. 24 2) After the affected chromosome is known, methods allowing screening of the whole chromosome to narrow down the aberrant region may be applied. Microsatellite analysis takes an advantage of frequent pathological errors that occur during cell division and detects resulting change in number of microsatellite repetitions. The variation in number of repeats affects the overall length of the microsatellite, characteristics that can be measured by various laboratory techniques. The regions containing the microsatellite repeats are amplified by polymerase chain reaction (PCR) with oligonucleotides (primers) that flank the microsatellite region. The size of amplicon corresponds with the number of repeats and can be detected on a gel electrophoresis that distinguishes the DNA fragments based on their size. Nevertheless, the most precise results can be obtained from fragment analysis method: One oligonucleotide of each PCR primer pair is labeled with fluorescent dye, usually FAM or HEX, and PCR product than subjected to electrophoresis on a sequencer. The automatically collected data are analyzed using specialized software. Only genotypes heterozygous for a given locus are regarded as informative; homozygosity and microsatellite instability (MSI) render the particular locus unevaluable for loss of heterozygosity (LOH) or amplification. In heterozygous genotypes, ratio of both alleles in normal and tumor tissues is calculated. If these ratios show a difference of more than 40%, the genotype is determined to display allelic imbalance. Next to the microsatellite repeats, SNPs may be used for detection of loss or gain of chromosomal material as well. The technique is called SNaPshot and uses labeled dideoxynucleotides (ddNTPs), common in sequencing. Each ddNTP (ddATP, ddGTP, ddCTP, ddTTP) is labeled by different fluorescent dye. Except for 2 oligonucleotides used for PCR amplification, one extra primer for SNapShot reaction is designed whose 3’end terminates just one nucleotide before the SNP is located. The procedure is same as in sequencing with labeled ddNTPs except for one difference: in SNapShot reaction only 1 nucleotide-extension occurs since no free dNTPs are present. The reaction is carried out on a sequencer; data are collected and analyzed using specialized software. Similarly to microsatellite analysis, only heterozygous genotype is informative; the areas of peaks representing two alleles are measured and the ratio is compared between patient’s tumor and normal DNA. Although the SNaPshot chemistry was originally developed for allelic discrimination, it may have much broader utilization. 25 In case there are no microsatellite repeats or suitable SNPs within the locus of interest available, quantitative PCR (QPCR) that measures copy number of genomic DNA may be used. This technique compares the DNA copy number between tumor and normal sample (it does not require matched normal and tumor DNA from one patient). It takes an advantage of real-time PCR instruments and chemistry (both SYBRGreen and TaqMan are suitable) that allow to monitor an increasing amount of amplified PCR product in real-time, which is dependent on the template input. The DNA quantities are normalized using several reference loci located on chromosomes usually unaltered in the studied cancer type and compared with standard DNA (calibrator) from unrelated normal individuals. Therefore, this technique can answer the question regarding copy number changes that might have occurred in tumor DNA. 3) As soon as the microsatellite or SNP analysis narrows down the affected region up to several kb, all genes and expressed sequence tags (ESTs) need to be considered carefully with regards to their potential tumorigenic effect. With a help of publicly available databases (NCBI - http://www.ncbi.nlm.nih.gov/, Sanger - http://www.ncbi.nlm.nih.gov/, PANTHER - http://www.pantherdb.org/, USCS - http://genome.ucsc.edu/ etc.) genes of known function (or based on protein similarity) are examined and those ones most probably not involved in the malignant process are excluded. The remaining genes and ESTs of a given locus are to be analyzed in details – e.g. by sequencing, expression or functional studies. Sequencing is a technique that enables to determine the exact DNA sequence. There are several ways how to sequence target DNA: next to the Maxam-Gilbert and in these days more often used Sanger’s sequencing it is possible to sequence directly using polymerase and modified dNTPs, by annealing to the oligonucleotides attached to the chips, by pyrophosphate sequencing, MALDI-TOF sequencing etc. 26 1.4 DISEASES WITH UNKNOWN QTL 1.4.1 Enteropathy-type T-cell lymphoma 1.4.1.1 Classification of ETL Enteropathy-type T-cell lymphoma (ETL or EATL), a rare intestinal lymphoma accounting for less than 1% of non-Hodgkin lymphomas (NHL) [56] and fewer than 5% all gastrointestinal tract lymphomas [57], was characterized by Isaacson and Wright only in 1978 [58, 59]. In 1994 was this cancer included as “intestinal T-cell lymphoma (with or without enteropathy)” in Revised European American classification of lymphoid neoplasms (REAL) [60]. The present term, enteropathy-type T-cell lymphoma, is recommended by World Health Organization (WHO) [56] that defines ETL as a tumor of intraepithelial Tlymphocytes with various degrees of transformation. · Occurrence ETL occurs mostly in jejunum or ileum; presentation in duodenum, stomach, colon or outside the gastrointestinal tract has also been recorded but is rare [56]. Malignant ETL cells arise after clonal proliferation of phenotypically abnormal intraepithelial lymphocytes (IELs) [56, 61] that might have already persisted for years [62]. Interleukin-15, whose physiological function is to launch lymphocyte proliferation, seems to be responsible for neoplastic expansion of aberrantly changed monoclonal IELs. Resulting accumulation of phenotypically aberrant IELs is probably the first step in pathogenesis of ETL [63]. · Histology and immunology Histologically, wide range of cytomorphological appearances was recognized, ranging from highly pleiomorphic or anaplastic tumors to those composed of monomorphic small- to medium-sized tumor cells. However, some discrepancies in published percentages of particular cell types do appear: As described in Chott et al. [64, 65], the majority of ETLs are composed of pleiomorphic medium-sized to large, sometimes anaplastic cells that are usually CD3+, CD7+, CD4- and CD5-. Approximately 20% of the recorded cases are monomorphic, small- to medium-sized and express CD3, CD7, CD8 and CD56. In contrary, according to WHO histological description the most frequently observed tumor cells are relatively monomorphic of a medium to large size with round or angulated 27 vesicular nuclei, prominent nucleoli and moderate to abundant, pale-staining cytoplasm. Less commonly, the tumor exhibits marked pleiomorphism with multinucleated cells that resemble anaplastic large cell lymphoma [56]. Nevertheless, the recent data on ETL suggest that morphological classification of this lymphoma by itself is of lesser importance [66]. Regarding immunophenotype, WHO classification points out to expression of CD3, CD7, CD103 and in a subset of cells CD8, and lack of expression of CD4 and CD5. Almost all ETL cases express in varying proportion of tumor cells also CD30 [56], [61]]. Moreover, in the subset comprised of small to medium-sized cells the tumor cells were CD8+ and CD56+, as reported elsewhere [56], [64], which has been used in a proposed ETL sub-classification [64, 67]: Approximately 80%–90% of ETLs (frequently referred to as non-monomorphic or type 1 ETL, see below) are composed of pleiomorphic, anaplastic, or immunoblastic tumor cells that usually have a CD3+CD4-CD8-CD7+CD5-CD56immunophenotype, a phenotype similar to that of the majority of normal intestinal intraepithelial T-cell receptor a/β T lymphocytes. About a third of these ETLs are associated with a previous clinical history of celiac disease. In 85% of these tumors there is histological evidence of enteropathy-like alterations in the gastrointestinal mucosa adjacent to the invasive tumor. In contrast, about 10% – 20% of ETL cases (subsequently referred to as monomorphic or type 2 ETL, see below) are characterized by frequent expression of CD8 and CD56, and are composed commonly of monomorphic small- to medium-sized tumor cells. These likely are derived from activated CD8+CD56+ intraepithelial T lymphocytes, which make up around 15% of intraepithelial T-cell receptor a/β T lymphocytes. This subtype appears to be associated rarely with a previous clinical history of celiac disease. In addition, only 50% of tumors show histological evidence of enteropathy adjacent to the invasive neoplasm. 1.4.1.2 Environmental risk factors T-cell neoplasms are in general relatively uncommon. Together with NK-cell neoplasms they account for only 12 % of all non-Hodgkin lymphomas [56]. These lymphomas show significantly different incidence variations in different geographical regions, which suggests environmental factors may have an impact on the tumorigenesis. T-cell lymphomas are reported more often in Asia where is also simultaneously high 28 incidence of the HTLV-1 virus (in endemic regions of Japan the seroprevalence of HTLV-1 is 8-10%). It is thus considered as proved that one of the main risk factors for T-cell lymphoma in Japan is the virus [56]. Next to this, Epstein-Barr virus (EBV) has been detected in a minority of cases, but the viral infection is often restricted to a small subpopulation of tumor cells and again, is to a certain extent epidemiologically dependent [68, 69]. As another factor deemed to influence the incidence of T- and NK-cell lymphomas is a racial predisposition. It was shown that aggressive NK/T-cell leukemias are more frequent in Asia inhabitants than in the rest of the world. ETL is often described as the most serious complication of celiac disease (CD) [70]. Clinically, there is an evidence of three possible associations with celiac disease: (1) ETL is a long-standing complication of celiac disease; (2) A short history of malabsorption (weeks or months) clinically reminding celiac disease (this is the most frequent condition; such a form, however, may already arise as a consequence of lymphoma rather than celiac disease alone); (3) The clinical picture presenting no malabsorption at all or the history of malabsorption that is not clear. Nevertheless, the histological analysis of the resected tissue adjacent to invasive tumor shows increased intraepithelial lymphocytes, villous atrophy and crypt hyperplasia, features similar to celiac disease. With regard to pathogenesis, a chronic stimulation of intraepithelial lymphocytes by nutritional gluten may be an important trigger of lymphomagenesis (reviewed by [61]). Nevertheless, specific genetic events are probably required to drive the process of lymphomagenesis further – as evident in long-term celiac disease patients who never developed ETL although they displayed clonal T-cell population [71]. Recently, two types of the therapy-refractory celiac disease (RCD) have been distinguished [72]. Except for persistent features of celiac disease, type I RCD does not exhibit any atypical histology or immunology of intraepithelial T lymphocytes, and shows polyclonal pattern. In contrast, type II RCD is CD3+, does not express surface T-cell markers such as CD8 or CD4, and commonly shows monoclonal T-cell receptor (TCR) rearrangement. Moreover, intraepithelial lymphocytes of type II RCD are often described with clonal cytogenetic alterations [73]. Type II RCD thus may as a “cryptic ETL” represent link between celiac disease and enteropathy-type T-cell lymphoma [74]. 29 1.4.1.3 Genetic risk factors With regards to genetic alterations occurring in ETL and compared to other gastrointestinal lymphomas, only few data are available. Next to the factors that constitute physiological genetic polymorphisms and whose variances are considered prerequisite for inconsiderable amount of polygenic disorders (such as HLA haplotypes), specific features – genetic aberrations – representing hallmark in ETL were found. · Most frequent chromosomal aberration The most frequently changed locus in ETL is the highly recurrent 9q gain (70%) [67]. The conventional CGH [66] and microsatellite marker study [75] allowed for more precise determination of the locus and narrowed it down to 9q33-q34 region. Till the date, all studies designated this amplified region as a unifying characteristic feature present in a very high (40 – 70%) number of patients [57, 66, 67, 75]. The 9q33-q34 amplification was observed in ETL irrespective of lymphoma morphology or immunology, which only highlights the presumptive pivotal role of this aberration in ETL pathogenesis. In the vicinity of amplified locus flanked by D9S290 and D9S1847 microsatellite markers (positioned 4cM apart) at least two candidate genes are located: Proto-oncogene ABL1 (whose involvement in translocation t(9;22)(q34;q11) and consequent fusion to BCR in chronic myelogenous leukemia, CML, is notoriously known) [76, 77] and transmembrane receptor involved in T-cell development, NOTCH1. Also NOTCH1 has an attractive suspect-like record for tumorigenesis: Its overexpression observed in anaplastic large cell lymphoma (ALCL) [78] or high expression in T-cell acute lymphoblastic leukemia (TALL) caused by t(7;9)(q34;q34.3) [79] is a clear evidence of its potential oncogenic property. · Further frequent chromosomal changes Among additional chromosomal imbalances frequently detected in ETL by the conventional CGH dominate recurrent chromosomal gains of 7q, 5q and 1q and recurrent losses of 8p, 13p, and 9p loci. All chromosomal aberrations detected in more than 15% of ETL cases analyzed and published in 4 key studies are summarized in Table 1. 30 Table 1 Gains Losses Diverse aberrations Chromosomal loci 9q31.3-q33.2 9q33.2-q33.3 9q34-qter 9q33-q34 1q23.1-q23.3 1q25.3-q31.2 1q32.2-q41 5p15.33 5q33-q34 5q34-q35 6p25.2-pter 7p22.3 7q11.22-q11.23 7q11.23-q21.3 7q22 7q31 7q33-q34 7q36-qter 8q13.3-q21.11 8q24 12p13.31-p13.33 16p13.3 17q25.3 22q12.3-q13.2 21q22.3 11p15.5 20q11.21-q12 22q11.21-q11.23 2q31.1-q31.3 2q24.3-q25.1 8p22-p23 8cen-p21 9p21 9p11.2-p13.3 10q26.2-q26.3 11q14.1-q14.2 13q22 18q22 17q23-25 17p13.1 (p53) 19q13.2-q13.31 3p21.31 5q21 (APC) 7p21 16q12.1 Zettl 2002 (CGH, FISH) Baumgärtner 2003 (MA) Zettl 2007 (CGH, FISH) De Leeuw 2007 (a-CGH) 60% 67% 63% 58% 16% 40% 64% 26% 40% 43% 47% 37% 30.8% 18% 21% 24% 31% 32% 18% 21% 24% 18% 15% 24% 50% 23% 27% 37% 37% 43% 43% 47% 43% 43% 43% 27% 23% 23% 23% 23% 20% 20% 20% 20% 17% 36.7% 23% 20% 30% 20% 25% 18% 34.6% 26.9% 16.6% 24% 17q23-25 42.3% 5q33-q34 7q31 1q41 7p21 38.5% 40% 36.4% 32% 31 23% 20% 20% 20% 23% Table 1: Number of recurrent chromosomal gains and losses published so far (in % of all cases). Only aberrations with frequency ≥ 15% are shown. Frequency above 30% depicted in bold. As shown, several regions containing loci of known tumor suppressor genes or oncogenes suffer from consistent abnormalities: The p53 (TP53) locus in 17p13.1, the APC gene locus in 5q21, the INK4a/ARF (p15/p16) locus in 9p21, and the retinoblastoma (RB1) gene locus in 13q14 revealed LOHs (although the last two loci in frequency lower than 15%) [75]. Similar to other lymphomas, the minimal overlapping region on 7q was 7q21q22 [80, 81]. It harbors genes playing a role in cell cycle control, CDK6 and ASK/DBF4, as well as gene encoding transcriptional co-activator TRAPP and multidrug resistance gene MDR1. Interestingly, gains of 1q22-q44 had been reported previously as a highly recurrent genetic alteration in refractory celiac disease [73]. Certain genetic alterations presented a close correlation with tumor morphology. Notably, monomorphic ETL more frequently showed gains of the MYC oncogene locus at 8q24 (73% vs. 27%, P < 0.03) and lacked gains of 1q32.2-q41 (20% vs. 73%, P < 0.01) and 5q34-q35.2 (20% vs. 80%, P < 0.01) [67]. In contrast, non-monomorphic ETL was characterized genetically by frequent gains of 1q32.2-q41 (75% vs. 32%, P < 0.01). It is noteworthy that, in contrast to gains of 9q chromosomal region, amplifications of 7q and 1q are among the most frequent genetic alterations detected both in classical cytogenetic studies and CGH analysis of different types of T- and B-cell lymphomas [8082]. It thus seems that genetic alterations shared among different T-cell lymphoma entities (such as gains of 1q and 7q or losses of 13q) may abrogate the function of genetic loci that control basic regulatory mechanisms of T lymphocytes proliferation or may be acquired during lymphoma progression. In contrast, entity-characteristic genetic alterations (such as 9q amplification in ETL) may overcome growth regulation mechanisms in specific T-cell subpopulation (such as in intraepithelial T lymphocytes), which gives rise to distinct T-cell lymphoma entities (ETL). · HLA class II Most patients show the HLA-DQA1*0501-DQB1*0201 (HLA-DQ2) genotype [83]. The HLA analysis in the 2 histological types of ETL revealed significant differences. Compared with monomorphic ETL, non-monomorphic ETL patients more frequently 32 carried HLA-DQ2 (73% vs. 33%; P = 0.03) and were homozygous significantly more often for HLA-DQ2 (40% vs. 6%; P = 0 .03). Homozygosity for HLA-DQ2 is associated with almost 5-fold increase in risk of disease development than HLA-DQ2 heterozygosity [84, 85]. The study by de Leeuw et al. [67] reported in non-monomorphic ETL cases the overall HLA-DQB1 genotype pattern closely resembling genotypes commonly published for ETL, whereas genotypes detected in monomorphic ETLs did not significantly differ from those recognized in normal Caucasian controls (P = 0.677). Next to this, correlation of the HLA genotypes with genetic aberrations identified an association between 5q34-q35.2 gains and DQB1*02 genotype (79% vs. 31%, P = 0.014) [67]. · Minimum two pathways of lymphomagenesis Next to the gain of 9q34, which is the unifying characteristic feature present in 40% of both, monomorphic and pleiomorphic histological types of lymphoma, Baumgärtner et al [75] observed another, much smaller group of cases that showed 3q27 amplification. These two pathological features were mutually exclusive. Interestingly, although de Leeuw and colleagues suggest an existence of two ETL types as well; they discovered that 9q31.3q33.2 gains were predominantly absent in ETLs with 16q12.1 losses (56% vs. 10%, P < 0.01) [67]. Similar to 9q gains, 16q losses did not correlate to any clinical, morphologic or immunophenotypic features, thus suggesting that both amplification of 9q34 and deletion of 16q12.1 may result in similar biological effects in ETL lymphomagenesis. To conclude, based on the all correlative findings 2 types of ETL could be distinguished (Figure 7) [67]: Type 1 ETL is characterized by non-monomorphic cytomorphology (pleiomorphic, anaplastic, immunoblastic), CD56 negativity, common gains of chromosomes 1q and 5q, but rarely that of the MYC oncogene locus. In view of shared HLA genotypes and genetic alterations, type 1 ETL may arise from, and be pathogenetically linked to, refractory celiac disease by a stepwise acquisition of genetic alterations, among which gains of 1q may be the hallmark genetic alteration at the stage of refractory celiac disease (see chapter 1.6). Type 2 ETL displays monomorphic small- to medium-sized tumor cell morphology and is characterized by frequent CD56 expression, MYC oncogene locus gain, and rare 33 gains of 1q and 5q regions. Given the genetic alterations and HLA-DQB1 genotype patterns, celiac disease may not be causal to type 2 ETL. These data suggest that a different mechanism of lymphomagenesis is prevalent in type 2 ETL. Both types, however, are characterized by a high prevalence of 9q gains/16q losses as the unifying feature. Figure 7: Suggested classification of two ETL subtypes based on the clinical, morphologic, immunophenotypic and genetic features. From de Leeuw 2007 [67] 34 1.5 DISEASES WITH KNOWN QTL 1.5.1 Diabetes mellitus Diabetes mellitus is one of the most common endocrine disorders affecting almost 6% of the world’s population. The prevalence of this chronic metabolic disease is on the increase. According to the projections of Amos et al. [86], the total number of people with diabetes mellitus all over the world will reach 221 million in the year 2010 in contrast to the 124 million estimated for 1997. Based on other estimates, this number of diabetic patients is expected to reach 300 million in 2025 year [87]. 1.5.1.1 Classification Diabetes mellitus (DM) is a complex of several disorders with wide range of pathogenesis affecting the glucose, lipid and protein metabolism in the body. The main characteristic represents hyperglycemia resulting from defects in insulin secretion, insulin action or both. The chronic hyperglycemia of diabetes is associated with long-term damage, dysfunction, and failure of various organs, especially the eyes, kidneys, nerves, heart, and blood vessels [88]. Several pathogenic processes are involved in the development of diabetes. These range from autoimmune destruction of the β-cells of the pancreas with consequent insulin deficiency to abnormalities that result in resistance to insulin action. The basis of the abnormalities in carbohydrate, fat, and protein metabolism in diabetes is deficient action of insulin on target tissues. Deficient insulin action results from inadequate insulin secretion and/or diminished tissue responses to insulin at one or more points in the complex pathways of hormone action. Impairment of insulin secretion and defects in insulin action frequently coexist in the same patient, and it is often unclear which abnormality, if either alone, is the primary cause of the hyperglycemia. Based on the etiopathogenesis, there are two main types of diabetes: Autoimmune type 1 diabetes (T1D) (or insulin-dependent DM, IDDM) is caused by an absolute deficiency of insulin secretion. The cause of type 2 diabetes (T2D) (or formerly noninsulin-dependent, NIDDM) is a combination of resistance to insulin action and an inadequate compensatory insulin secretory response. Nevertheless, there has been a long history of discussion on the proper diabetes classification and it is not absolutely clear 35 whether these two most frequent forms of diabetes should be viewed, understood and treated as one disorder with partly different etiopathogenesis, especially when a large proportion of diabetic patients may have both type 1 and type 2 processes contributing to their diabetic phenotype [89], or whether the unifying hypothesis for the mechanisms of βcell death in T1D and T2D should rather be ignored [89, 90]. A specific subtype of T1D is Latent Autoimmune Diabetes in Adults (LADA). LADA represents controversial type of diabetes sometimes designated type 1.5 (T1.5D) [91] or “slow-progressing type 1 diabetes”. A fraction of individuals with chronic destructive process of insulin-secreting β-cells associated with immunological changes have clinical diabetes that initially is not insulin-requiring. The onset of diabetes in these patients is usually in adult life, and because their diabetes is at least initially not insulinrequiring, they appear clinically to be affected by type 2 diabetes. The Immunology of Diabetes Society proposed that patients are ≥ 30 years of age, positive for at least one of the four autoantibodies commonly found in T1D patients (ICAs and autoantibodies to GAD65, IA-2, and insulin), and not treated with insulin within the first six months after diagnosis (which is the criteria supposed to distinguish between T1D occurring in patients ≥ 30 years and LADA). Special attention should be paid to diagnose such patients because therapy may influence the speed of progression toward insulin dependency, and in this respect, efforts should be made to protect residual C-peptide secretion [92]. Only the studies of immunologic, metabolic and genetic profiles of late-onset T1D and LADA [93-95] are believed to answer the question how close regarding the pathophysiology adult-onset T1D and LADA are. Distinct forms of type 2 diabetes that are connected to the impaired β-cell function due to monogenetic defect are referred to as Maturity-Onset Diabetes of Young (MODY) [88]. They are characterized by impaired insulin secretion with minimal or no defects in insulin action and an autosomal dominant inheritance pattern. Mutations at six genetic loci on different chromosomes have been identified and confirmed to date: (HNF)1α (MODY3), glucokinase (MODY 2), HNF-4α (MODY 1), HNF-1β (MODY 5), insulin promoter factor (IPF)-1 (MODY 4), and NeuroD1 (MODY 6) [96]. Recently, new three potentially MODY-related loci are under close testing. 36 1.5.1.2 Type 1 diabetes mellitus (T1D) 1.5.1.2.1 Incidence and prevalence of T1D Several lines of evidence support a critical role of exogenous factors in the pathogenesis of T1D. Studies in monozygotic twins indicate that only 13-33% are pairwise concordant for T1D [97, 98] suggesting that there is either acquired postconceptional genetic discordance or differential exposure to putative environmental factor(s). It has also been observed that if the first twin of a monozygotic twin pair develops diabetes prior to age 5, the risk for the second twin to develop diabetes within 20 years of follow up is 50%, while if the first twin develops diabetes after age 20, the risk for the co-twin is less than 5% [99]. In contrast to monozygotic twins, the risk for developing diabetes or anti-islet autoantibodies does not appear to differ between dizygotic twins and siblings of patients with T1D. Early reports of increased prevalence of anti-islet autoantibodies in dizygotic twins have been retracted [100], and a combined series indicates a relatively low prevalence of autoantibody-positivity in dizygotic twins. For monozygotic twins, expression of any of the “biochemical” anti-islet autoantibodies (GAD65, ICA512 (IA-2) or insulin autoantibodies, see below) is associated with a very high risk of progression to T1D [99]. Based on decades of follow up, the development of diabetes in autoantibody-positive monozygotic twins is preceded by progressive loss of insulin secretion [101]. Other evidence of genetic involvement in the etiology of T1D is that 95% of type 1 diabetes patients carry HLA-DR3, HLA-DR4, or both. The relative risk of type 1 diabetes conferred by DR3 is seven fold, by DR4 nine fold and by DR3/DR4 is more than fourteen fold of that conferred by other alleles. Recently, mathematical model has been introduced that tries to explain the rising incidence of T1D and the excess of HLA-DR3/HLA-DR4 heterozygotes among patients [102]. The incidence of type 1 diabetes follows two-peak distribution: it begins sharply to rise at about 9 months of age, continues to rise until age 12–14 years, and then declines [103]. The second, less significant increase is documented around 40 years of age. A rise of incidence in the old age (above 70 years) is rather a consequence of natural exhaustion of the immune system. A similar pattern is seen in many countries irrespective of whether the overall incidence of type 1 diabetes is low or high. The incidence of T1D is lowest in Japan, the Caribbean, and southern Europe, while the highest incidence rates are in the Scandinavian countries [104], particularly in Finland, and in Sardinia in Italy [105]. 37 Accumulated data on the incidence of T1D during the last 20 years show that type 1 diabetes occurs in most racial and ethnic groups but the risk is highest among white population [104]. While these differences among races may be determined in part by genes, the variation within the white population is almost as large as that observed among all races. All of these examples support the role of genetic and environmental factors in the etiology of diabetes. A more recent study shows that also climate has a role in the etiology of T1D [106]. 1.5.1.2.2 Immunology of T1D Despite the fact that the trigger leading to the pathogenesis of type 1 diabetes is currently unknown, the pathophysiology of the disease is believed to be biphasic. The first stage is represented by leukocytes infiltration to the pancreatic islets; this response does not cause damage. Nevertheless, in the second phase, which occurs only in diabetes-prone individuals, autoreactive T cells acquire aggressive potential and destroy the majority of the pancreatic islets [107]. The cellular autoimmunity in T1D responsible for the destruction of pancreatic β-cells is mediated by T cells [108]. B cells produce autoantibodies to β-cell antigens, and in turn present these self-antigens to T cells, but β-cells and autoantibodies are not strictly required in this autoimmune process [109]. In most cases a preclinical period characterized by the presence of autoantibodies to pancreatic β-cell antigens precedes the onset of hyperglycemia by a few years. This long pre-clinical period provides an opportunity for prevention. Known autoantigens include insulin, glutamic acid decarboxylase (GAD), and antibodies against the islet cell antigen 512 phosphatase (IA-2), of which only insulin is cell specific (Table 2). Insulin autoantibodies occur more in DR4 haplotype patients and are useful if measured prior to administering exogenous insulin. Glutamic acid decarboxylase antibodies persist the longest time following diagnosis and are useful in confirming autoimmune etiology in long-standing cases. The presence of more than one type of antibody is highly predictive of disease, and occurs years before clinical manifestations [110]. 38 Major autoantigens Minor autoantigens Shared autoantigens Ÿ Glutamic acid decarboxylase (GAD65) Ÿ Protein thyrosine phosphatase (ICA512/IA-2) Ÿ Insulin (IAA) Ÿ ICA 12 (SOX13) Ÿ ICA 69 Ÿ Carboxypeptidase H Ÿ Gangliosides (GM-1) Ÿ Sulfatides Ÿ Tissue transglutaminase (tTG) Ÿ 21 – hydroxylase (21- OH) Ÿ Gliadin Ÿ Endomysium Ÿ Other antigens Table 2: Autoantigens in type 1 diabetes mellitus T1D occurs often in association with other autoimmune diseases (thyroid disease, Addison’s disease, celiac disease, rheumatic disease and others). The celiac disease (CD) is the second most prevalent autoimmune condition accompanying type 1 diabetes, after autoimmune thyroid disease. Co-existence of T1D and CD has been reported in 1-7% of diabetic cases [111, 112], ten times more frequent than in general population [113], and the anti-gliadin, anti-endomysium and anti-tissue transglutaminase antibodies are detected in sera of DM patients [114]. 1.5.1.2.3 Environmental risk factors A series of evidence support a critical role of exogenous factors in the development of type 1 diabetes, such as (1) the fact that < 10% of individuals with HLA-conferred diabetes susceptibility do progress to clinical disease, (2) a pairwise concordance of type 1 diabetes of < 40% among monozygotic twins, (3) a more than 10-fold difference in the disease incidence among Caucasians living in Europe, (4) a several fold increase in the incidence over the last 50 years, and (5) migration studies indicating that the disease incidence has increased in population groups who have moved from a low-incidence to a high-incidence region. Environmental factors have a role in ethnic differences in the prevalence of diabetes mellitus. However, there are clear differences in the clinical phenotype of diabetes between 39 different ethnic groups, which are pinpointing to a genetic role. The annual rate of incidence of childhood T1D in France, between 1988 and 1995, was comparable to that observed in the Nordic countries. In spite of the similarity in the annual rate of incidence of childhood type 1 diabetes, the overall incidence of T1D in childhood is still smaller in France, suggesting that environmental factors play a major role in the pathogenesis of type 1 diabetes. o Ethnic factors Major ethnic and geographical differences in prevalence and incidence of T1D are known [115]. The most frequently reported cases of T1D are in the Nordic countries, with the highest incidence in Finland (35/100,000/year) of the population in children aged 0–14 years. The lowest incidence is seen in Asia (0.5–1.3/100,000/year). Low rates are also reported in Africa and Latin America [116]. These significant differences in the incidences of type 1 diabetes are possibly due to variations in genetic and environmental factors [117]. Another example of how ethnicity may influence the disease development was published in 1996 [118]: The Chile study showed a significant difference between the incidence of T1D in native Chileans (0.42/100,000) compared to Caucasian Chileans (1.58/100,000) between years 1983 and 1993. o Nutrition Nutrition during the early months of life (neonatal period and early infancy) is considered of a high importance with regards to potential DM development. Early dietary exposure to cow’s milk containing feed, short duration of exclusive breast feeding, and high intake of cow’s milk protein in recent diet [119] may increase susceptibility to type 1 diabetes mellitus [120]. Prolonged breast feeding (minimum recommended is six months period, more beneficial is one year) has shown to be uncoverable for proper development of fully functioning child’s immune system. Immediately after birth, environmental stimuli such as intestinal flora and microbial infections provide antigens for neonatal T cells and TH1/TH2 balance, which triggers the education of the infant’s immune system. Maternal milk provides am important source of immunoglobulin A (SIgA) antibodies. Before infant’s own immunity is fully established, this SIgA from mother were shown to help to protect against Vibrio cholerae, Campylobacter, Shigella and Giardia [121]. Also protection 40 of breast milk against enterovirus infections has been reported, and cases of enterovirus infection in early life followed by T1D development documented [122]. Furthermore, milk is rich in receptor analogues for certain epithelial structures, which microbes need for attachment to host tissues as an initial step in infections. Next to that, human milk was shown to have the infant anti-inflammatory property. An early cessation of breast feeding thus may have negative impact on the newborn health and proper development of immunity. o Viruses and infection High incidence of diabetes was found to be associated with congenital rubella [123]. Based on the WHO analysis [124], 20% of children with congenital rubella develop T1D. Further, antibodies against Coxsackie B4 have been reported in 20–30% of new cases of T1D [125]. Rotavirus and herpes viruses have been also associated with β-cell autoimmunity and T1D [126]. It is hypothesized that particular genotypes may predispose their carrier to development of a stronger humoral response to infection. It is plausible that early infection with a non-diabetogenic strain of a virus can induce immunity to antigenetically similar diabetogenic strains and protects from T1D. 1.5.1.2.4 Genetic risk factors To better understand the pathophysiology of T1D, it is necessary to strike to a molecular basis of impaired regulation of glucose metabolism – to DNA. Nevertheless, several obstacles slow down the process of deciphering the exact role of genetic factors in the etiology of diabetes mellitus. These include the modification of the expression of the diabetic genotype by environmental factors and the variability in the age of onset of diabetes [105]. The T-cell mediated autoimmune process that destroys pancreatic β-cells in T1D is a complex phenotype influenced by multiple genetic and environmental factors. Based on the positional approach (linkage studies), 18 loci throughout the whole genome were identified and designated IDDM1 – IDDM18 [127]. Unfortunately, it was shown that many of the loci are false positive; these statistical artifacts originated from underestimates of the sample size required for meaningful statistical power [128]. Table 3 summarizes up to date recognized IDDM loci. 41 Locus Chromosome Gene/marker IDDM 1 IDDM 2 IDDM 3 IDDM 4 IDDM 5 IDDM 6 6p21 11p15.5 15q26 11q13 6q25 18q12-q21 IDDM 7 2q31 HLA INS D15S107 ZFM1 (Zinc finger protein 162) SOD2 (superoxid dismutase) DCC associated gene ZNF236 bcl-2 NEUROD1 IGPR (islet specific glucose 6 – phosphatase) IL-1 gene cluster, HOXD8, GAD1, GALNT3 IDDM 8 IDDM 9 IDDM 10 IDDM 11 IDDM 12 6q27 3q21-q25 10p11.2-q11.2 14q24.3-q31 2q33 IDDM 13 IDDM 14 IDDM 15 IDDM 16 IDDM 17 IDDM 18 2q34 GAD2 ENSA (alpha-endosulphine) CTLA-4 CD28 ICOS NRAMP1 6q21 14 10q25 5q31.1-33.1 Transient neonatal diabetes Ig heavy chain D10S1750-D10S1773 ILB12 Table 3: List of IDDM loci The presented work addresses in greater detail only two IDDM loci, HLA (IDDM1) and INS-VNTR (IDDM2), which are discussed in depth below. Nevertheless, the large body of evidence suggests that also other important disease-associated loci contribute to the T1D risk, although with much smaller effects. The IDDM12 locus (localized on chromosome 2q33) contains CTLA-4 gene encoding cytotoxic T-lymphocyte associated protein 4 and CD28. These are likely candidates for development of T1D and other autoimmune diseases because of their important role in the T-cell proliferative response [129]. Moreover, the effect of IDDM12 seems to be independent of HLA alleles or the IDDM2 risk genotype [130]. Next to aforementioned IDDM loci, several other non-IDDM regions of genome have been proven so far to be associated with diabetes mellitus as well. Among the susceptibility loci of non-IDDM nature may be mentioned genes such as MIC-A (Major histocompatibility complex (MHC) class I chain – related gene A) [131, 132] believed to be 42 recognized by one subpopulation of intestinal γδ T cells [133] and suggested to play a role in the activation of a subpopulation of NK cells that express the NKG2D receptor [134]; PTPN22 (Protein thyrosine phosphatase non-receptor type 22) [135], gene encoding a member of tyrosine phosphatases (PTPs) protein family, important down-regulator of T cell activity [136, 137]; NFKB and NFKBIA genes coding for transcription factor nuclear factor κB (NFκB) and its inhibitor IκB, respectively, whose role in inflammation process and autoimmune disease etiology has been proposed [138]; gene for proinflammatory cytokine interleukin-18 (IL-18) playing a key role in autoimmune, inflammatory, and infectious diseases [139-141]. Table 4 provides list of some of the identified non-IDDM loci. Chromosome location Gene symbol Gene name Type of diabetes 11p15.1 Sulphonylurea receptor-1 T2D 2q37.3 19q13.3 11q22.2.-q22.3 SUR1 (ABCC8) CAPN10 GYS1 IL-18 Calpain 10 Glycogen synthase Interleukin-18 T2D T2D T1D 2q36 11p15.1 IRS-1 KCNJ11 6p21 MIC-A T2D T2D, neonatal diabetes T1D 4q24 14q13 3p25 NFKB NFKBIA PPARγ 1p13 PTPN22 10q25.3 TCF7L2 Insulin receptor substrate-1 Potassium inwardly-rectifying channel, subfamily J, member 11 Major histocompatibility complex (MHC) class I chain – related gene A Nuclear factor κB Inhibitor of nuclear factor κB Peroxisome proliferator – activated receptor γ Protein thyrosine phosphatase non-receptor type 22 Transcription factor 7-like 2 (T-cell specific, HMG-box) T1D, T2D T1D, T2D T2D T1D T2D Table 4: Some of the non-IDDM loci associated with both types of diabetes o IDDM1 locus: HLA The first candidate locus studied and found to be strongly associated with T1D was the human leukocyte antigen (HLA) region on chromosome 6p21.3 [142]. It accounts for 40% - 50% of the genetic susceptibility [143], through a large variety of protective and 43 predisposing haplotypes. Other important loci associated with T1D have much smaller effects than HLA. Human leukocyte antigens belong to the most polymorphic genes in the human genome [144]. Such diversity brings a positive evolutionary advantage over the pathogens. The human MHC is divided into three regions: class I, class II, and class III. The class I genes are located telomeric in the complex. They have single polypeptide chain associated with β2-microglobuline. The class I region includes HLA-A, -B and -C genes. The antigens are expressed on the surface of almost all tissue cells and their function is to present peptides to CD8+ cytotoxic T cells. HLA class II genes are located at the centromeric end of MHC [145]. Class II molecules composed of heavy α-chain and light βchain are expressed on antigen presenting cells, APC (monocytes, macrophages, B lymphocytes, dendritic cells and activated T lymphocytes). The HLA class II region includes HLA-DR, -DQ and -DP subregions. Major function of antigens is to present peptides to CD4+ T cells. The MHC class III region contains many genes with varying functions in immune system. Figure 8: Overview of the extended HLA complex on the chromosome 6 (modified from Louka and Solid, 2003). 44 Most T1D-relevant polymorphisms are amino acid changes in exon 2 of the βchain of DR and both α and β-chains of DQ. Thus, the most common T1D-predisposing haplotypes in Caucasians are DRB1*0301-DQA1*0501-DQB1*0201 and DRB1*0401DQA1*0301-DQB1*0302, abbreviated as DR3-DQ2 and DR4-DQ8, respectively [128, 146]. Interestingly, heterozygosity for DQ2/DQ8 which (because of linkage disequilibrium almost always implies DR3/DR4 heterozygosity) confers the highest T1D risk in Caucasians [147]. This genotype is present only in 3% of the general population, but in 30% of T1D patients, conferring a 15-fold relative risk and an earlier onset of disease. Majority of the Caucasians suffering from T1D has at least one of these two haplotypes. Conversely, a subset of HLA alleles provides dominant protection against diabetes, the most common is the HLA-DQ6 haplotype, DRB1*1501-DQA1*0102DQB1*0602. It is found in less than 1% of diabetic children and in almost 20% of the general population [145]. Another dramatically protective allele is DRB1*1401, associated with DQA1*0101-DQB1*0503. In combination with a predisposing haplotype, the carrier of the protective haplotype remains at low risk [148]. At a molecular level, risk alleles differ structurally from protective alleles. Most characteristic is the absence of an aspartic acid molecule at position 57 of the β-chain of the DQ molecule. This reverses the electric charge of the peptide-binding groove of the HLADQ8 molecule, thereby possibly altering the binding of peptide epitopes [149]. Published crystal structure of DQ8 with an insulin peptide B (residues 9–23 that represent an important T cell epitope) draws to the attention the hypothesis that these class II alleles primarily influence diabetes risk by the peptides they bind and present to T lymphocytes [150]. 45 DRB1 *0301 *0401 *0405 *0801 *1601 *0101 *0403 *0701 *1501 *1401 *0701 DQA1 DR3 DR4 DR4 DR8 DR2 DR1 DR4 DR7 DR2 DR6 DR7 *0501 *0301 *0301 *0401 *0102 *0101 *0301 *0201 *0102 *0101 *0201 DQB1 *0201 *0302 *0302 *0402 *0502 *0501 *0302 *0201 *0602 *0503 *0303 Diabetes association DQ2 DQ8 DQ8 DQ4 DQ5 DQ5 DQ8 DQ2 DQ6 DQ5 DQ9 Strongly positive Strongly positive Strongly positive Positive Positive Positive Neutral or weakly negative Negative Strongly negative Strongly negative Strongly negative Table 5: Class II HLA haplotypes associated with susceptibility for or protection against type 1 diabetes o IDDM2 locus: INS-VNTR The second strongest association with T1D has been that of the INS-VNTR (variable number of tandem repeats) polymorphism at the promoter region of the insulin gene (INS), designated IDDM2 [55]. IDDM2 was mapped to the short arm of chromosome 11 between the genes for tyrosine hydroxylase (TH) and insulin-like growth factor 2 (IGF2) on 11p15.5 [55, 151-153] (Figure 9). Alleles of the INS-VNTR are classified based on their size (which corresponds to number of the 14-15 nucleotides long repeats) into three groups: class I (represented by short alleles with 28 – 63 repetitions), class III (long alleles, 141 – 209 repeats) and middle class II alleles being exclusively of African/Asian origin with an average length 80 repeat units [55, 154]. Next to this, also sequence polymorphism within the repeating motive has been identified [154-157] that enables finer sub-classification of each of the class. The role of INS-VNTR in the development of type 1 diabetes mellitus is generally accepted [55, 158-160] and the risk associated with the INS-VNTR class I alleles as well as the dominant protective effect of class III alleles for the T1D phenotype are for the most part unquestioned. 46 Figure 9: Insulin VNTR minisatellite repeats. The INS -VNTR is located 363 bp upstream from the insulin gene (INS) transcription starting site. It is also 5 kb upstream from a second potential target for regulation, the insulin-like growth factor 2 (IGF2), and 10 kb downstream of the tyrosine hydroxylase gene (TH). The accepted scheme of insulin expression in the thymus and pancreas explains the process of development of antibodies against insulin by the finding that the class I/I genotype is responsible for lower insulin mRNA expression in the fetal thymus but increased expression in both the fetal and adult pancreas and, the class III/III genotype is associated with higher expression levels in the thymus, which could more efficiently induce negative selection of insulin-specific T-lymphocytes [161, 162]. It might also be noted that the –23HphI mutation affects the consensus sequence for the intron splicing machinery. A similar mutation in the CFTR (cystic fibrosis transmembrane conductance regulator) gene is able to cause serious disease [163], therefore, the potential affect of the mutation should not be underestimated. Moreover, just recently Kralovicova and colleagues have shown that variants of the INS –23HphI SNP 47 (termed by the authors as IVS1-6A/T) can affect pre-mRNA splicing [164]. This suggests a functional role for –23HphI SNP. IVS+5insTTGC IVS2+11C/T IVS1-6A/T E3+168C/A E3+155C/T E2+53G/A Figure 10: Insulin gene variants that affect pre-mRNA splicing. A: Schematic representation of the INS reporter construct, tested DNA variants, and alternatively spliced mRNA isoforms. Exons 1–3 are shown as black, gray, and white boxes, respectively. Thick lines denote introns, colorful lines represent mRNA isoforms identified by sequencing (numbered in circles). The designation of DNA variants involves the distance (in nucleotides) from the splice sites. From Kralovicova, 2006 In light of this finding, an intriguing question arises of which one of the two polymorphisms, VNTR or –23HphI (or perhaps neither?) is the primary functional polymorphism at the IDDM2 locus that confers risk for diabetes mellitus. The latest experiments leave little doubt that the intragenic variability in the insulin gene itself is responsible for various defects in insulin “processing and handling” and may play a role in insulin resistance, diabetes mellitus and/or obesity. o Non-IDDM locus: MIC-A Major histocompatibility complex (MHC) class I chain –related gene A (MIC-A) is located within the MHC class I region of the chromosome 6. It contains the long open reading frame encoding three distinct extracellular domains (α1, α2 and α3) a transmembrane segment, and a cytoplasmatic tail. It has been reported that MIC-A may be recognized by a subpopulation of intestinal γδ T cells [133] and may play a role in the activation of a subpopulation of NK cells that express the NKG2D receptor [134]. 48 Sequence analysis of the MIC-A gene showed a trinucleotide repeat (GCT) microsatellite polymorphism within the transmembrane region [165, 166]. So far, six alleles of the exon 5 of the MIC-A gene, which consist of 4, 5, 6, 9 and 10 repetitions of GCT, or five repetitions of GCT with an additional nucleotide insertion (GGCT), have been identified [166, 167]. The polymorphism of the MIC-A gene and its location in the HLA region warrant studies aimed at identifying an association with the risk for autoimmune diseases. The MIC-A gene has already been found to confer genetic risk for Behçet’s disease [168], ankylosing spondylitis [169], and autoimmune Addison’s disease [170]. Recent works also support the findings that MIC-A is associated with autoimmune diabetes mellitus [132, 171]. o Non-IDDM locus: PTPN22 Lymphoid-specific phosphatase (LYP) encoded by PTPN22 gene belongs to a family of tyrosine phosphatases (PTPs) involved in preventing spontaneous T cell activation by dephosphorylating and inactivating T cell receptor–associated kinases and their substrates [136, 137]. A functional PTPN22 polymorphism, C1858T, was found to be associated with type 1 diabetes in different Caucasian populations. The minor allele of this variant confers predisposition to T1D, as observed in a case-control study with European American and Sardinian cohorts [172]. The C1858T substitution changes amino acid from arginine to tryptofan at codon 620 (R620W). This amino acid is a part of proline-rich motif participating in binding to SH3 domain of Csk kinase that suppresses the mediators of T cell signaling [137]. It has been shown that mutant 620W protein does not bind to Csk [172]. The PTPN22 1858T allele has been found to be associated with several autoimmune disorders: rheumatoid arthritis [173], systemic lupus erythematosus [174], Wegener's granulomatosis [175] and myasthenia gravis [176] among them. Confirmation of the PTPN22 association with T1D was performed in several independent populations [177179]. 49 o Non-IDDM loci: NFKB and NFKBIA Nuclear factor κB (NFκB) is a transcription factor that has been shown to be involved in the regulation of many genes that encode mediators of the immune response, embryo and cell lineage development, cell apoptosis, inflammation, cell cycle, oncogenesis, viral replication, and a variety of autoimmune diseases [138, 180, 181]. NF-κB is formed by homo or heterodimers of five NF-κB family members [182]. These dimers are usually present in an inactive form in the cytoplasm, where they remain bound to a group of related inhibitory κB (IκB) proteins [182, 183]. The activation occurs upon various stimuli leading to a range of different reactions even specific for different cell types [138, 184]. At the molecular level, NFκB is activated through phosphorylation of an inhibitor of NFκB (IκB). Phosphorylated IκB is released from NFκB/IκB complex, allowing the translocation of NFκB molecule into the nucleus. Once in the nucleus, NFκB binds to the DNA consensus sequence (5’ GGGACTTTCC 3’) of various genes, thereby activating their transcription [185, 186]. The activating stimuli include reactive oxygen species and advanced glycation end products, which are toxic products of nonenzymatic glycation caused by long-term hyperglycemia and oxidative stress. Based on several studies it has been suggested that NFκB may work as a potential predisposing factor in T1D. It was shown in human and rat primary β-cells that after blocking of NF-κB activation [187] apoptosis has been prevented and thus NFκB-induced β-cell destruction confirmed in several studies [188-191]. Paradoxically, NF-κB-regulated genes have been shown to inhibit apoptosis in diverse cell types [192-195]. Genes coding for both NFκB and IκB proteins are interesting with regards to their plausible role in β-cell death. Near the NFKB1 gene located on the chromosome 4q23-q24 [196], a polymorphic CA dinucleotide repeat microsatellite polymorphism with 18 described alleles has been identified [197]. In the NFKBIA that mapps to 14q13 locus, an A/G single nucleotide polymorphism at the 3’ UTR (untranslated region) has been identified [198]. Both of the polymorphisms have been tested for their potential impact on amino acid sequence, structure, mRNA stability or function of resulting proteins. 50 1.5.1.3 Type 2 diabetes mellitus (T2D) 1.5.1.3.1 Incidence and prevalence of T2D Type 2 diabetes prevalence is increasing in an epidemic fashion worldwide [87], with a predicted doubling of the prevalence to 325 million affected persons in 25 years. This increase has been ascribed to a collision between genes and increasing globalization of the Western lifestyle, but few genetic variants have been consistently associated with T2D [199-201]. The majority (~97%) of all diabetic patients have type 2 diabetes. The association of genetic factors with T2D is well documented in many parts of the world. A strong genetic predisposition to T2D was suggested by 3 lines of evidence [202]: (1) 58% of monozygotic co-twins of diabetic twins were themselves diabetic compared with an expected prevalence of 10%; (2) only 1 of 15 originally disease-discordant, monozygotic twin pairs remained discordant for diabetes; and (3) 65% of non-diabetic monozygotic co-twins of diabetic twins had elevated glucose values. Because concordance for diabetes was less than 100% for twins aged 52-65 years and because twins varied in age at onset of disease, non-genetic factors may also influence diabetes development, as proposed also by a population-based study in Denmark [203]. Physiological variability in glucose metabolism and insulin action and secretion even in nondiabetics has been demonstrated. Intrapopulation studies have shown that there may be an important but delicate phenotypic difference in glucose metabolism in a given population. In a study performed on a population of Swedish male nondiabetics, the subjects were found to be either low or high insulin responders, a difference that was attributed in part to physical fitness. In Australian Aborigines, major differences in insulin response have been reported for populations living in the desert and in the coastal regions [204]. Comparison of the phenotypic characteristics of the disease has given more information on the nature of the genetic factors in this disease. Studies on Asian Indians with T2D showed that they are more insulin resistant than are Caucasians with T2D, even when the degree of obesity is comparable [205]. This suggests that there may be distinct subtypes of type 2 diabetes in different ethnic groups depending on the phenotype of the diabetes gene. All of these examples suggest physiological differences in glucose 51 metabolism, which may in turn be due to genetic differences even within a single population. 1.5.1.3.2 Metabolic syndrome Metabolic syndrome is the term frequently coined for type 2 diabetes and obesity [206]. Metabolic syndrome is defined by American Heart Association and the US National Heart, Lung and Blood Institute as “multiple, interrelated risk factors of metabolic origin that appear to directly promote the development of atherosclerotic cardiovascular disease”. It is believed that this constellation of risk factors (such as obesity, dyslipidemia, hypertension, elevated plasma glucose and proinflammatory state) is strongly associated with the occurrence of type 2 diabetes. Metabolic syndrome is also known as metabolic syndrome X, syndrome X, insulin resistance syndrome, Reaven’s syndrome or CHAOS (Australia). Nevertheless, concept of metabolic syndrome has been disputed recently by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes. 1.5.1.3.3 Environmental risk factors o Location Several studies have demonstrated the importance of residence on the prevalence of T2D. Japanese living in Brazil have higher prevalence of type 2 diabetes compared to those in Japan [207]. Similarly, Japanese Americans living in Hawaii and Los Angeles have a higher prevalence of type 2 diabetes than native Japanese [208]. Also differences in prevalence in terms of rural vs. urban population have been observed: The age-adjusted prevalence of diabetes mellitus was significantly higher in the urban population compared to rural population [209-211]. There seem to be two reasons for this rural vs. urban differences in prevalence: First, people in the urban setting may be more affluent and eat more junk food in contrast to people in the rural setting. Second, people in the rural setting are more likely to be involved in more physical activity compared to their urban counterparts, thereby reducing the likelihood of developing type 2 diabetes. 52 o Obesity Obesity has long been recognized as one of the risk factors for T2D development. Its prevalence is rapidly rising to epidemic proportions around the world at an alarming rate. According to the WHO more than 1 billion people are overweight and at least 300 million of these are obese [206]. The WHO has concluded that the fundamental causes of this epidemic are sedentary lifestyles, high-fat, energy-dense diets, and increased urbanization. In the U.S., results from the National Health and Nutrition Examination Survey conducted in 1999 – 2000 indicate that a staggering 64.5% of US adults are either overweight or obese, defined as having body mass index (BMI) of 25 or more. From 1960 to 2000, the prevalence of obesity in US adults (defined as BMI greater or equal to 30) has risen from 13.3% to 30.9%, with the prevalence of extreme obesity (BMI greater than or equal to 40) increasing from 0.8% to 4.7% in this 40-year period. Moreover, this trend is expected to continue. o Physical activity It has been shown that regular physical activity increases insulin sensitivity and glucose tolerance [212]. Besides that, it has been recently confirmed that physical activity reduces the risk of type 2 diabetes [213]. A study performed on an African American population in the United States reported that the prevalence of diabetes increases with the degree of inactivity and obesity [208]. o Aging. Severe and prolonged stress. Drugs Aging was proposed as one of recently identified factors playing a role in diabetes pathogenesis. It has been previously hypothesized [214] that defects in mitochondrial oxidative and phosphorylation capacity might be a contributing factor to the increased triglyceride content in muscle and liver [215] and that mitochondrial function is reduced with aging and may contribute to age-related development of insulin resistance [216]. Moreover, malfunctioning of biological clocks (located in suprachiasmatic nucleus, SCN) was used to explain a growing number of younger subjects and even children prone to develop type 2 diabetes. While elderly patients have an impaired function of the SCN due to the degeneration of neurons, it is believed that in younger subjects the clock loses its 53 "feeling" for internal and external rhythms owing to modern lifestyle, which may lead to the metabolic syndrome and type 2 diabetes [217]. Severe and prolonged stress related to today’s modern life may also be associated with glucose intolerance and may hence increase the risk of diabetes [218]. The development of diabetes after severe and prolonged stress seems to be caused by the activation of the adrenal hormones, notably the glucocorticoids that have been noted to cause glucose intolerance. Drugs like corticosteroids and some oral contraceptive steroids may cause glucose intolerance and T2D in susceptible individuals [219]. The role of other drugs like diuretics and β-adrenoceptor blocking agents in the etiology of glucose intolerance and T2D is, however, questionable. 1.5.1.3.4 Genetic risk factors The genetic factors contributing to T2DM are much less explored than those involved in T1D etiology; though not all of the molecules involved in the action of insulin have been studied, some recent large-scale T2D genetic studies have succeeded in identifying several “T2D-loci” (Table 4). Among the well-established candidates are genes encoding peroxisome proliferator-activated receptor γ (PPARG) [200], which is a receptor playing crucial regulatory role in adipogenesis, lipid and carbohydrate metabolism, cell differentiation and proliferation [220], in glucose homeostasis and insulin sensitivity [221, 222]; calpain 10 (CAPN10) [223] gene, polymorphism of which was found to be associated with reduced muscle mRNA levels and insulin resistence [224]. Among other susceptibility loci can be mentioned TCF7L2 (Transcription factor 7-like 2) [225-228] that after heterodimerization with β-catenin induces transcription of number of genes including intestinal proglucagon [228], and NFKB and NFKBIA genes. Next to their role in autoimmune processes leading to T1D, a possible link between NFκB and the development of insulin resistance and type 2 diabetes has also been discussed [229-233]. Other candidate genes with potential role in the pathology of T2D resulting from their physiological functions are IRS-1 and GYS1. IRS-1 codes for insulin receptor substrate-1 protein linking the tyrosine-phosphorylated insulin receptor to the downstream elements of the insulin signaling pathway [234]. The Gly972Arg polymorphism was found to be associated with decreased fasting insulin levels in T2D patients [235] and impaired insulin secretion [236]. 54 XbaI polymorphism within GYS1, gene for glycogen synthase, has been described as associated with insulin resistance [237, 238]. Interestingly, INS-VNTR has been intensively studied not only for its association with T1D, but also for its involvement in T2D, birth size, obesity and polycystic ovary syndrome [55, 239, 240]. These associations stem from the potential impact that VNTR has on the transcriptional regulation of the insulin gene in both the thymus and pancreas [161, 162]. Even though INS-VNTR has been reported to play a role in the development of T2D and discussed the risk of class III alleles, some studies show conflicting data [241-243]. More and more evidence is pointing to genomic mutations in various ionconducting channels as being responsible for many genetic diseases [244-246]. The inwardly rectifying Kir6.2 channel (KCNJ11) and regulatory sulfonylurea receptor SUR1 (ABCC8) represent two subunits of the pancreatic β-cell ATP-sensitive K+ channel complex [199, 247-249]. This complex plays a key role in glucose-stimulated insulin secretion, which makes it a potential candidate for a genetic defect contributing to the development of T2D [250, 251]. After several initial reports with inconsistent data [247, 252] the association between the E23K polymorphism of KCNJ11 and the susceptibility to T2D was recently suggested [199] and finally confirmed by meta-analysis [249]. Moreover, in cases of permanent neonatal diabetes, several mutations responsible for amino acid substitutions in both, the Kir6.2 (KCNJ11) and SUR1 (ABCC8) genes have been described, as well as in congenital hyperinsulinaemia in humans, which is a rare genetic disorder characterized by higher insulin levels in parallel with low blood glucose [253]. These findings illustrate the importance of both genes for the development of diabetes caused by a defect in insulin secretion [254-256]. Furthermore, functional studies show that E23K SNP is involved in increasing the “open probability” of the Kir6.2 channel, which should lead to diminished insulin secretion [257], and provide supporting evidence for KCNJ11 being a diabetogene. 55 Figure 11: The SUR1 receptor consists of two polypeptides, a potassium channel that is encoded by the KIR6.2 gene (KCNJ11) and an ATP-cassette binding protein encoded by the actual SUR1 gene (ABCC8). From Brinkmann, 2006 1.5.2 Other autoimmune diseases 1.5.2.1 Systemic lupus erythematosus Systemic lupus erythematosus (SLE) is an autoimmune rheumatic disease, characterized by the presence of autoantibodies. Virtually every organ or system can be involved, but commonly, SLE affects the skin, joints, haematopoietic system, kidneys, lungs and central nervous system. The American College of Rheumatology has developed classification criteria for SLE. For a diagnosis to be made, four of the following 11 criteria must be met: malar rash, discoid rash, photosensitivity, oral ulcers, arthritis, serositis, renal disorder, neuropsychiatric disorder, hematologic disorder, immunologic disorder, and antinuclear antibody [258]. · Incidence and prevalence Worldwide, a conservative estimate states that over 5 million people have lupus. The prevalence of SLE worldwide varies greatly. The disease, similar to many rheumatic 56 disorders, affects predominantly females (4:1 over males). From population-based epidemiologic studies, it has been estimated that one in 3,450 women (independent of race) in the United Kingdom, one in 250 black women in the United States, one in 1,000 Chinese women, and one in 4,200 white women in New Zealand may have SLE [259-262]. · Autoantibodies B lymphocytes from patients with SLE display a lack of self-tolerance, and an inappropriate overproduction of antibodies. The presence of antinuclear autoantibodies (ANA) is the immunological hallmark of SLE. ANA are directed against single-stranded and double-stranded DNA, histones, small nuclear ribonucleoproteins (snRNPs), the Ro (SS-A) and the Lo (SS-B) ribonucleoprotein particles, Sm (Smith antigen), ENA (extractable nuclear antigen). A positive ANA is a sensitive test found in 98% of patients with SLE. Anti-DNA antibodies are seen in approximately 60% of patients with SLE [263]. Next to ANA, 10-65 % of patients are positive for anti-erythrocyte antibodies, 10-15% of patients develop anti-phospholipids and anticoagulants antibodies and almost 30% express rheumatoid factor (RF), which are immunoglobulins specific to the Fc region of IgG. Complexes of antibodies and their antigens can damage tissues by activating complement and by engaging Fc receptors on macrophages and other inflammatory cells. · Immune dysfunction, hormonal risk factors There is a presence of overactive B cells in SLE, which produce an abundance of autoantibodies. Nevertheless, the development and survival of these cells is dependent on and supported by help of T cells and may also require an inappropriate lack of T cell suppression. Some B cell activators, such as B-lymphocyte stimulator (BLyS), are reported to be upregulated as well [264]. Further, the association between genetic complement deficiencies and lupus development has triggered speculations about a possible role for complement in SLE pathogenesis [265]. Defective clearance of apoptotic fragments may provide the link between complement dysfunction and SLE [266]. There is a whole body of evidence that cytokines may be involved in lupus etiology. Interleukin 10 (IL-10) that stimulates B cell proliferation and antibody production seems to be a perfect candidate [267, 268]; on the other hand, in some studies [269] but not others 57 [270] tumor necrosis factor a (TNF-a) investigation suggested a protective role of this cytokine in SLE development. Next to this, impaired secretion of tumor necrosis factor β (TNF-β) is believed to explain at least in part the overproduction of antibodies seen in lupus [271-273]. The SLE affects predominantly women, most frequently in the years between their menarche and menopause, which suggests the immunomodulatory role of sex hormones in the development of the SLE. Estrogen was found to act as a potent disease stimulator in lupus-prone mice [274]. Moreover, an opposite, protective role of androgens in autoimmunity development has been confirmed [275]. From other hormonal dysbalances in SLE patients, increased serum prolactin levels were detected [276]. · Environmental risk factors The search for environmental triggers looked for culprits among viruses. Nevertheless, neither Epstain-Barr virus, nor cytomegalovirus, parvovirus B19 nor the retroviruses have been proved guilty [277]. In contrast, ultraviolet light has been proposed to function as one of the potential disease activator. As hypothesized, anti-Ro antibodies are particularly associated with the development of a photosensitive rash. UV light exposure causes the release of proinflammatory cytokines and increases the rate of keratinocyte apoptosis, which leads to extracellular exposure of autoantigens including Ro, and subsequent keratinocyte cytotoxicity [278]. 58 Chemical/physical factors Dietary factors Infectious agents Hormones and environmental oestrogens Ÿ Aromatic amines Ÿ Hydrazines Ÿ Drugs (procainamide, hydralazine, chlorpromazine, isoniazid, phenytoin, penicillamine) Ÿ Tobacco smoke Ÿ Hair dyes Ÿ Ultraviolet light Ÿ L-canavanine (alfalfa sprouts) Ÿ High intake of saturated fats Ÿ ?Bacterial DNA/endotoxins Ÿ ?Retroviruses Ÿ Hormonal replacement therapy, oral contraceptive pills Ÿ ?Prenatal exposure to oestrogens Table 6: Environmental factors that may be relevant in the pathogenesis of systemic lupus erythematosus (from Mok CC and Lau CS, 2003) · Genetic risk factors The presence of genetic factors contributing to SLE pathogenesis is clearly documented by the 25% - 50% concordance between affected monozygotic twins compared to only 2% - 5% in dizygotic pairs [279, 280]. It was estimated that at least four susceptibility genes are required for SLE to occur [281]. In the Caucasian population an association of HLA-DR2 and HLA-DR3 with SLE has been observed, with a relative risk for the development of diseases of approximately 2-5 [280, 282]. A second area of potential linkage, derived from affected sibling pair studies, was mapped to the chromosome 1q region [283]. In general, genes expected to be involved in SLE pathogenesis are primarily those of immune functions. Areas of interest include genes encoding proteins participating in antigen presentation (HLA genes), apoptosis, Fc receptor, B and T cell function, and production of cytokines and complement [284-289]. 59 Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ HLA genes Non-HLA genes Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ Ÿ DR2, DR3 (relative risk 2–5) DR2, DR3, DR7, DQw1, DQw2, DQA1, DQB1, B8 (anti-Ro) DR3, DR8, DRw12 (anti-La) DR3, DQw2, DQA1, DQB1, B8 (anti-Ro and anti-La) DR2, DR3, DR7, DQB1 (anti-DNA) DR2, DR4, DQw5, DQw8, DQA1, DQB1 (anti-U1 ribonuclear protein) DR2, DR4, DR7, DQw6, B61 (anti-Sm) DR4, DR7, DQ6, DQ7, DQw7, DQw8, DQw9 (anticardiolipin or lupus anticoagulant) Complement genes (C2, C4, C1q) Mannose binding lectin Tumour necrosis factor α (TNFa) T cell receptor (TCR) Interleukin 6 (IL-6) CR1 Immunoglobulin Gm and Km FcγRIIA (IgG Fc receptor) FcγRIIIA (IgG Fc receptor) poly-ADP ribose polymerase (PARP) Heat shock protein 70 (Hsp70) Humhr 3005 Runt-related transcription factor 1 (RUNX1) Prolactin (PRL) Protein tyrosine phosphatase nonreceptor 22 (PTPN22) Cytotoxic T lymphocyte-associated protein 4 (CTLA4) Programmed cell death 1 gene (PDCD1) Table 7: Genes involved in human systemic lupus erythematosus. Updapted from Mok CC and Lau CS, 2003 · The role of prolactin in the (auto)immunity Neuropeptide hormone prolactin (PRL) is produced mainly by lactotroph cells of the anterior pituitary gland. Nevertheless, extrapituitary production has been described as well, especially by immune cells such as lymphocytes and monocytes, endometrium and epithelial cells [290]. The interplay between neuroendocrine and immune system has been confirmed in both humans and experimental animal models. The association of the prolactin and the immune system was first proposed based on the atrophy of the thymus after hypophysectomy when the hypophysectomized rats preserved 10 - 20% residual lactogenic activity [291]. Nowadays, the role of PRL in maintaining and restoring immune 60 homeostasis is well documented [292, 293]. PRL is a member of the cytokine/haematopoietic peptide family and via its downstream target IRF-1 (interferon regulatory factor 1) participates in the maturation and differentiation of immune cells [294296]. Prolactin and its production by lymphocytes have been suggested to play a distinct role in the pathogenesis of systemic autoimmune diseases such as rheumatoid arthritis and systemic lupus erythematosus [297-299]. Moreover, high serum prolactin levels were observed in group of patients with psoriasis and link between keratinocytes hyperproliferation and prolactin has been proposed [300]. Good response to bromocriptine therapy (dopamine agonist) that decreased prolactin levels in patients with SLE and PsA [301, 302] has been demonstrated. PRL gene is located on the short arm of chromosome 6 (6p22.2 – p21.3), telomeric to HLA region. The peripheral PRL production is driven by an alternative promoter located 5,840 base pairs (bp) upstream of the pituitary transcription start site and transcription in lymphocytes requires a specific binding site [303]. The extrapituitary promoter contains a G/T single nucleotide polymorphism at the position -1149 [304]. The -1149 G/T SNP is a part of a conserved GATA sequence, site important for binding of GATA family transcriptional factor. Higher PRL mRNA expression associated with the G allele in lymphocytes was reported [304]. Moreover, high frequency of the G allele was described in patients with SLE [305] but was not confirmed in other works [306]. Figure 12: Genomic organization of prolactin gene. Multiple promoters and multiple transcription start sites (®) are indicated as well as SNP polymorphisms 61 tested for their potential association with SLE diseases. From Mellai M et al., 2003. Exons are represented by black boxes, untranslated regions are shaded. 1.5.2.2 Rheumatoid arthritis The rheumatoid arthritis is a chronic, recurrent, systemic inflammatory disease primarily involving joints. Both environmental and genetic risk factors are reported to be implicated [307, 308]. · Autoantibodies Autoantibodies are detected in approximately 2/3 of patients with RA and predict more severe disease [309-311]. Two major types of autoantibodies used clinically to characterize RA are rheumatoid factor and anti–cyclic citrullinated peptide (CCP) antibody directed against peptides, in which arginine has been posttranslationally modified to become citrulline [312]. These autoantibodies are strongly correlated but may represent distinct clinical subsets of RA. · Immunologic pathogenesis The cause of rheumatoid arthritis is still unknown. It was proposed that RA requires susceptibility to the disease through genetic endowment with specific markers and an infectious event that triggers an autoimmune response. As in other autoimmune disorders, the "mistaken identity" theory suggests that an offending organism causes an immune response that leaves behind antibodies specific to that organism. This subsequently leads to the activation of the serum complement cascade and the recruitment of the phagocytic macrophages that begin immune attack against synovium. T cells recognition of antigen plays an important role in pathogenesis of RA as evidenced by T cells prominent presence in the inflammatory infiltrate in the rheumatoid synovium. Surprisingly, the synovial tissue does not present much of T cell-derived cytokines. In contrast, there is a wide range of macrophage-derived products including proinflammatory cytokines such as TNFa and IL-1, IL-6, IL-8 and transforming growth factor β (TGFβ). These cytokines further exacerbate the inflammation of the synovium, leading to edema, vasodilatation, and can activate synovial fibroblasts and other cells to 62 produce matrix metalloproteinases involved in the degradation of cartilage [313]. Once the inflammatory reaction is established, the synovium thickens, the cartilage and the underlying bone begin to disintegrate and evidence of joint destruction appears. Based on the abovementioned facts, it is suspected that T cells do play a critical role in the initiation of the synovitis; however, macrophages represent the major agent involved in its propagation. · Environmental risk factors Some infectious organisms mentioned in the context of RA triggers have been Mycoplasma, Erysipelothrix, parvovirus B19 and rubella, but these associations have not been supported in epidemiological studies. Nor has convincing evidence been presented for other types of triggers such as food allergies. There is also no clear evidence that physical and emotional effects, stress and improper diet could be a trigger for the disease. Many negative findings suggest that either the trigger is different from patient to patient, or that the trigger might in fact be a chance event, as hypothesized by Edwards et al. [314]. Epidemiological studies have confirmed a potential association between RA and two herpesvirus infections: Epstein-Barr virus (EBV) and Human Herpes Virus 6 (HHV-6) [315]. Individuals with RA are more likely to exhibit an abnormal immune response to the Epstein-Barr virus [316] [317]. The HLA-DRB1*0404 was shown to be associated with low frequencies of T cells specific for the EBV glycoprotein 110 and thus predisposing to RA development Balandraud [318]. Further, cigarette smoking seems to be one of the most clearly defined environmental risk factor for rheumatoid arthritis [319]. · Genetic risk factors Rheumatoid arthritis has a complex mode of inheritance. The genetic contribution to a susceptibility to rheumatoid arthritis has been shown in studies of twins [320] and families [321] and in genomewide linkage scans [322]; the relative risk of RA detected in monozygotic twins is 2.0–62.0 and in siblings of affected individuals 2.0–17.0. Rheumatoid arthritis is strongly associated with the HLA-DR4 (most specifically DR*0401 and DR*0404) [323-325]. Actually, 90% of patients with RA have been shown to express the HLA-DR4/DR1 alleles compared to only 40% of unaffected controls. The genetic 63 association with HLA-DR4 is believed to play a major role in switch of acute inflammation into permanent chronic inflammatory state. Based on the newly discovered associations, a similar role has been hypothesized for the PTPN22 gene [173, 326] and for rs3761847 SNP that is in linkage disequilibrium with two additional genes, TRAF1 (encoding tumor necrosis factor receptor-associated factor 1) and C5 (complement component 5) located on chromosome 9, all involved in regulating immune responses. Also other SNPs characterized by high level of statistical significance in genomewide scan were detected; some of these loci contained candidate genes of known biologic relevance to rheumatoid arthritis, including CD40, bradykinin receptor 1 (BDKRB1), and the 17q chemokine gene cluster containing CCL1, CCL8, and CCL13 [327]. Nonetheless, their significance in RA pathogenesis remains to be tested. Peripheral production of prolactin and its recognized cytokine-like role in immunity made the PRL a great candidate for RA-associated gene [297, 299]. Analysis of mRNA expression profile of immortalized lymphoblastoid cell lines derived from 11 pairs of RA-discordant monozygotic twins revealed 747 over- and 416 underexpressed genes in RA twin compared to healthy co-twin [328]. Among three most significantly overexpressed genes were FLJ90650 (LVRN) encoding laeverin (a novel enzyme with sequence homology to CD13), HSD11B2 gene for 11β-hydroxysteroid dehydrogenase type 2 (a steroid pathway enzyme), and CYR61 coding for cysteine-rich, angiogenic inducer 61, which is a known angiogenic factor. Moreover, products of all these genes were abundantly expressed in RA synovial tissues. Next to these, some other genes known to be involved in RA pathogenesis were differentially expressed: proapoptotic CASP7 and FADD were underexpressed, while antiapoptotic TIAF1 gene was found to be overexpressed [328]. Several additional genes in association with RA reported recently and promising candidate genes are listed in Table 8. 64 Chromosome location 6p21.3 9q33.q34.1 2q32.2-q32.3 2q33 1p36.13 Gene symbol Gene name HLA-DRB1 TRAF1 – C5 locus STAT4 CTLR4 PADI4 Human leukocyte antigen, class II Tumor necrosis factor receptor-associated factor 1; Complement component 5 Signal transducer and activator of transcription 4 Cytotoxic T-lymphocyte associated protein 4 Citrullinating enzyme peptidylarginine deiminase 4 Protein thyrosine phosphate non-receptor type 22 CD40 antigen Chemokine, CC motif, ligand 1; Chemokine, CC motif, ligand 8; Chemokine, CC motif, ligand 13 Bradykinin receptor 1 Solute carrier family 22 (organic transporter), member 4 Runt-related transcription factor 1 1p13 20q12-q13.2 17q11.2 PTPN22 CD40 CCL1 – CCL8 – CCL13 locus 14q32.1-q32.2 5q31.1 BDKRB1 SLC22A4 (OCT1) RUNX1 (AML1) 21q22.3 Table 8: List of some genes and potential candidates involved in etiology of rheumatoid arthritis 1.5.2.3 Psoriatic arthritis Psoriasis is a chronic and relapsing inflammatory disease of the skin associated with various immunologic abnormalities. Approximately 10% - 30% of psoriasis patients suffer from joint afflictions, indicative of psoriatic arthritis (PsA), which is a chronic, recurrent, asymmetric, erosive polyarthritis. · Incidence and prevalence Prevalence of psoriasis varies from ~ 0.2% of the population in Japan; the commonest is in Scandinavia and Northern Europe where it reaches 3% [329]. Genetic factors appear to play a role in disease caution. Psoriasis is found in family members of approximately 15% of patients. The concordance of psoriasis in monozygotic twins is 65– 72%, versus 15–30% in dizygotic twins. Determination of concordance in older twin pairs from a national twin registry in Denmark revealed nearly 90–100% heritability [330]. In an Australian study the monozygotic twin concordance rate is lower (35% for monozygotic twins and 12% for dizygotic twins), giving an estimated heritability of 80% [331]. Psoriatic arthritis can develop at any age, however it tends to appear about 10 years after the first signs of psoriasis, usually between the ages of 30 and 50, but it can also affect children. Men and women are equally affected by this condition. The onset of the arthritis 65 may be acute or insidious and is usually preceded by skin disease. In about one in seven cases the arthritis symptoms may occur before any skin involvement [313]. · Immunologic pathogenesis The cause of psoriasis and PsA is unknown. Evidence for immunopathogenesis in psoriatic arthritis includes the presence of antibodies directed against skin antigens and activated T cells in skin and synovium, however, the rheumatoid factor is absent. Psoriatic arthritis has histopathologic features that are more characteristic of other forms of spondyloarthritis than rheumatoid arthritis indicating distinct disease mechanisms. Matrix metalloproteinases are strongly expressed in psoriatic arthritis synovium. Animal models with features of psoriatic arthritis were developed to help to study precise mechanisms involved in the etiology of PsA [332-336]. · Environmental risk factors It is hypothesized that psoriasis occurs due to a combination of genetic predisposition and environmental assaults. These can include mechanical, ultraviolet and chemical injury, infection, psychological stress and smoking or certain medications [329, 337]. The most compelling of these factors is infection with group A streptococci [338]. Streptococcal throat infections frequently precede occurrences of guttate psoriasis that can then lead to chronic plaque psoriasis. A recent study revealed that all patients with guttate psoriasis carried the HLA-C*0602 allele. There are also claims that chronic plaque psoriasis may be aggravated by infection [339]. Psoriasis also occurs in association with human immunodeficiency virus (HIV) infection [340]. · Genetic risk factors A variety of genetic factors have been associated with PsA [329, 341]. These include alleles in the MHC locus such as HLA-B13, B17, B27 (almost 50% of patients with psorasis and spondylitis [313]), B57, HLA-C6, MHC class I chain-related gene A, allele A9 (MICA-A9), the caspase-activating recruitment domain 15 (CARD15) on chromosome 16q, and polymorphisms in TNF gene. The stress releasing hormone and T cell mediated cytokine prolactin may be involved in the immunological background of PsA 66 [302]. Alenius and colleagues found an association with the TNFB localized in the HLA region, but they did not uncover any associations with any of the HLA-B or HLA-C alleles previously shown to be associated with PsA [342]. Nevertheless, the most impressive genetic association recently described is the strong link between the presence of certain activating killer immunoglobulin-like receptors (KIR) on natural killer (NK) cells and PsA [343]. It was shown that subjects were most susceptible to PsA if they had certain activating KIRs combined with the absence of HLA ligands for corresponding homologous inhibitory KIRs. Activating KIRs have also been associated with an increased risk of psoriasis [344]. In a revised model Nelson et al. reported that the presence of the two activating receptors KIR2DS1 and KIR2DS2 increases the risk for PsA, and this risk is enhanced if the inhibitory HLA class I ligands for the inhibitory KIR2DL are missing [345]. Some other studies brought controversial data [346-348], which may reflect the multifactorial pattern of PsA inheritance [349]. Moreover, PsA is a heterogeneous disease with multiple manifestations (erosive polyarthritis, axial disease, oligoarthritis, distal joint arthritis with or without spondylitis) and contributing genetic factors may differ in the subgroups. 67 1.6 THE JOINT MARKER OF ENTEROPATHY-TYPE T-CELL LYMPHOMA AND AUTOIMMUNE DIABETES MELLITUS During the last several years some evidences appear that lymphoproliferative disorders and autoimmune diseases can share some aspects of their pathogenesis. Involved mechanisms are frequently the same in both diseases: environmental factors such as the use of cytotoxic or immunosuppressive agents, genetic factors favoring a common genetic susceptibility, and immunologic factors such as immunoregulatory disturbances of the immune system. In this study, such a common mechanism for enteropathy-type T-cell lymphoma and autoimmune diabetes mellitus is the occurrence of celiac disease (CD) [113, 350-354]. Celiac disease is a condition characterized by an increased immunologic responsiveness to gluten, dietary wheat gliadin. Recent research revealed that CD is an autoimmune disorder that may affect as many as 1% people in Western countries [355, 356]. As far as pathogenesis is concerned, not only an extrinsic trigger (the gluten peptides), but also the autoantigen, the enzyme tissue transglutaminase (tTG), has been identified [357]. tTG facilitates the breakdown of proline-rich, therefore proteolysis by gastric and pancreatic enzymes-resistant gluten. When antibodies to tTG are generated, enterocytes are destroyed and the common CD symptoms diagnosed. Other associated antibodies frequent in CD are the anti-gliadin antibodies (AGA) and antibodies to endomysium (EMA). Celiac disease’s histological features resemble those typically found in ETL: increased intraepithelial lymphocytes (often defined as more than 40 lymphocytes per 100 erythrocytes) [57], villous atrophy and crypt hyperplasia. Refractory celiac disease (RCD) is clinically defined as persistent villous atrophy with crypt hyperplasia and increased intraepithelial lymphocytes despite a strict gluten-free diet for more than 12 months or, when there are severe persistent symptoms requiring clinical intervention regardless of the duration of gluten-free diet [72]. Both phenotypic and immunologic features indicate RCD may be a pre-malignant phenotype. Further evidences for the association of CD and ETL include the demonstration of gluten sensitivity in ETL patients [358] and also the observations that in patients with 68 celiac disease, a gluten-free diet may be protective against the development of lymphoma [359]. Moreover, in ETL, TCR β and γ genes are clonally rearranged [56], which again confirms the relationship between the two conditions. It has been long recognized that several autoimmune disorders are more prevalent in patients with celiac disease than in general population. T1D is reported as the most frequent condition associated with celiac disease [113, 360]. On the other hand, the CD-specific autoantibodies often accompany autoimmune process in type 1 diabetes as well. Next to T1D, additional autoimmune diseases show gluten-sensitivity: SLE, arthritis, polymyositis, thyroiditis and others. There are numerous immunogenetic theories trying to explain this association by antigenic mimicry, damage-induced neoantigen exposure, altered intestinal permeability, idiotype network dysregulation or epitope spreading [360]. It is worth of noting that the genetic susceptibility to CD, T1D and also ETL significantly overlaps: a certain degree of susceptibility to these diseases is associated with the HLA-DQA1*0501-DQB1*0201 (HLADQ2) and also DQA1*0301-DQB1*0302 (HLA-DQ8) haplotypes [361]. Additionally, nonHLA genes seem to participate in common etiology as well. Of candidate genes for CD investigated during the recent years, locus on chromosome 2q33 containing CTLA4, CD28, and ICOS genes has given a constant association [362-365]. Among other genes that seem to be shared in T1D and CD etiology are CCR3, MIC-A, IL-2/IL-12 locus [366], and also analyses of several polymorphisms within the genes encoding cytokines brought positive results. The reason for the association of ETL with CD has not yet been fully elucidated. A potential mechanism that drives the autoimmune celiac disease into lymphoma development has been reported by Reines [367] who suggests that autoimmunity in autoimmune diseases is basically antineoplastic. According to the author and based on several theories and models, individuals suffering from autoimmune disease have inherited many prematurely aging cells. These damaged cells adapt to in vivo conditions by beginning to transform into cancer cells. As long as they have not fully transformed, these cells will continue to signal ‘danger’ to the innate immune system. Thus, the clinical 69 outcome of this competition between immunity and incipient neoplasia varies upon the degree of tumor-proneness or resistance of the individual. In the ETL, IL-15 (highly expressed in the CD involved epithelium) has been shown to preferentially stimulate the survival and expansion of the clonal abnormal intraepithelial T cells from patients with RCD [368] and invoke INFγ and cytotoxic agencies such as granzyme B and perforin production by these cells, which are directed against intestinal epithelial cells. Eventually, stepwise appearance of genetic alteration(s) characteristic for ETL seals doom. Figure 13: Multistage development of ETL. The percentage in parentheses is the approximate proportion of CD4− and CD8− or CD8+ and CD56+ IELs at various clinicopathological stages of the development of ETL. From Isaacson & Du, 2002 It thus seems that gluten may represent some common point of concurrence between ETL and T1D. It is apparent that one environmental trigger may under specific stimulating extrinsic and intrinsic conditions lead to different pathological phenotypes. This divergence 70 could be caused by several factors starting from the amount of gluten exposure, the degree of inflammatory cytokine response, the number and type of produced antibodies, and ending by genetic background of the individual. 71 CHAPTER 2 AIMS OF THE STUDY AND METHODOLOGY 2.1 AIMS The aim of presented work was to contribute to the understanding of molecular mechanisms underlying pathogenesis of several polygenic disorders by 1) identifying (new) quantitative trait loci in polygenic disease(s) with up to now insufficiently explored genetic component contributing to disease etiology 2) testing the contribution of previously identified candidate genes to pathogenesis of diseases with known QTLs and finding new interactions and subtyping associations 72 2.2 METHODOLOGY 1) To further delineate 9q34 region, previously established as the most frequent aberration occurring in enteropathy-type T-cell lymphoma, and to identify probable candidate genes (paper No. 1), microsatellite analysis and gDNA-QPCR were employed. 2) A. To characterize the genetic background of patients with different types of diabetes mellitus (T1D in children, T1D in adults, LADA, T2D) by performing association studies and investigating polymorphisms in HLA, insulin (INS), Kir6.2 (KCNJ11), NFKB1 and NFKBIA genes (papers No. 2 – 8), PCR-SSP, PCR-fragment analysis and PCR-RFLP methods were used. In our research, we were particularly interested in i) age-related differences in immunogenetic predisposition to DM ii) genetic discrimination of adult-onset T1D and LADA to eventually confirm heterogeneity of group of autoimmune diabetes mellitus in adults iii) investigation of association of quantitative trait loci mentioned above and biochemical and immunological markers specific for DM B. Determination of gene expression of diabetes associated HLA-DRB1*04 and NFKB1 genes in antigen presenting (APC) cells of T1D patients (paper No. 8) was performed by RT-QPCR using TaqMan chemistry. C. A potential association between prolactin gene and three system autoimmune disorders (systemic lupus erythematosus, rheumatoid arthritis, psoriatic arthritis) was studied by PCR-RFLP (papers No. 9 and 10). 73 CHAPTER 3 RESULTS 3.1 LIST OF ORIGINAL PAPERS 1. Cejkova P, Zettl A, Baumgärtner AK, Chot A, Ott G, Müller-Hermelink HK, Starostik P: Amplification of NOTCH1 and ABL1 gene loci is a frequent aberration in enteropathy-type T-cell lymphoma. Virchows Arch 2005 Apr; 446(4):416-20. 2. Cejkova P, Novota P, Cerna M, Kolostova K, Novakova D, Kucera P, Novak J, Andel M, Weber P, Zdarsky E: HLA DRB1, DQB1 and insulin promoter VNTR polymorphisms: interactions and the association with adult-onset diabetes mellitus in Czech patients. Int J Immunogenet 2008 Apr; 35(2):133-40. 3. Cejkova P, Novota P, Cerna M, Kolostova K, Novakova D, Kucera P, Novak J, Andel M, Weber P, Zdarsky E: KCNJ11 E23K polymorphism and diabetes mellitus with adult onset in Czech patients. Folia Biol (Praha) 2007 53(5):173-5. 4. Cerna M, Kolostova K, Novota P, Romzova M, Cejkova P, Pinterova D Pruhova S, Treslova L, Andel M: Autoimmune Diabetes Mellitus with Adult Onset and Type 1 Diabetes Mellitus in Children Have Different Genetic Predispositions. Ann N Y Acad Sci 2007 Sep;1110:140-50 5. Cerna M, Novota P, Kolostova K, Cejkova P, Zdarsky E, Novakova D, Kucera P, Novak J, Andel M: HLA in Czech adult patients with autoimmune diabetes mellitus: comparison with Czech children with type 1 diabetes and patients with type 2 diabetes. Eur J Immunogenet 2003 30:401-407 6. Novota P, Cerna M, Kolostova K, Cejkova P, Zdarsky E, Novakova D, Kucera P, Novak J, Andel M: Diabetes mellitus in adults: association of HLA DRB1 and 74 DQB1 diabetes risk alleles with GADab presence and C-peptide secretion. Immunol Lett 2004; 95:229-232 7. Novota P, Cejkova P, Cerna M, Andel M: Genetic risk factors in autoimmune diabetes mellitus, their significance and fiction. Cas Lek Cesk 2004; 143(3):159-63. Review in Slovak. 8. Kolostova K, Pinterova D, Novota P, Romzova M, Cejkova P, Pruhova S, Lebl J, Treslova L, Andel M, Cerna M: HLA, NFKB1 and NFKBIA Gene Polymorphism Profile in Autoimmune Diabetes Mellitus Patients. Exp Clin Endocrinol Diabetes 2007 115:124-129 9. Fojtikova M, Cerna M, Cejkova P, Ruzickova S, Dostal C: Extrapituitary prolactin promoter polymorphism in Czech patients with systemic lupus erythematosus and rheumatoid arthritis. Ann Rheum Dis 2007 May;66(5):706-7 10. Stolfa J, Fojtkova M, Cejkova P, Cerna M, Sedova L, Dostal C: Polymorphism of the prolactin extrapituitary promoter in psoriatic arthritis. Rheumatol Int 2007 Sep; 27(11):1095-6. 75 3.2 COMMENTARY ON THE ORIGINAL PAPERS Paper No. 1 focused on the amplification of 9q34 that was identified as the most frequent aberration occurring in ETL, and on further delineation of the amplified region and identification of candidate gene(s). Though in multiple cases the allelic imbalances in the 9q34 locus were discontinuous, not showing any consistent pattern, detailed microsatellite analyses on 26 ETL cases revealed allelic imbalance in ABL1 and NOTCH1 gene loci (the former flanked by microsatellites D9S290–D9S1847, the latter by D9S158). Similar results provided TaqMan-based quantitative real-time PCR (QPCR) showing amplification of ABL1 and NOTCH1 exons in 50% and 65% of cases, respectively. Amplifications of the NOTCH1 gene were more frequent than of the ABL1 gene; moreover, the analyzed NOTCH1 exon consistently displayed higher levels of amplification than ABL1 coding sequences. Both ABL1 and NOTCH1 are hematopoietically significant genes and their participation in several tumor diseases is documented, which was further confirmed by our results indicating that both genes in question are targeted by the amplification; nevertheless, the data favor NOTCH1 as the primary target of genomic DNA amplification in ETL. Papers No. 2 – 8 deal with determination of genetic predisposition to different types of diabetes mellitus. The strongest association with autoimmune diabetes mellitus has been ascribed to HLA genes designated IDDM1 (papers No. 2 and 4 – 8; paper No. 7 is a review in Slovak language). Autoimmune diabetes patients manifested after 35 years of age can be divided into two different groups having partly different HLA class II predisposition: adult onset type 1 diabetes mellitus (T1D), carrying DRB1*03 and DRB1*04 risk alleles (P < 0.05, OR = 3.1 for both), and increased frequency DQB1*0302 compared to healthy control subjects (20.5% vs. 12.8%, P = NS), and a latent autoimmune diabetes in adults (LADA) with a sole DRB1*03 risk allele (P < 0.01, OR = 3.4). Further, we can confirm the difference in HLA allele frequencies between glutamic acid decarboxylase antibodies (GADA) positive/negative groups and C-peptide (CP) negative/positive groups in DM patients diagnosed after 35 years of age. The DRB1*03, DRB1*04 and DQB1*0302 alleles are associated with decrease of CP levels and 76 hence may represent risk for CP (~ insulin) production, while DRB1*06 seems to be protective (P < 0.05, OR = 0.4). Next to this, we found DRB1*03 allele as a significant marker of autoantibody (GADA) development (P < 0.0001, OR = 4.2). Like in adult-onset T1D, the HLA analysis in T1D manifested in childhood revealed three associated alleles: HLA-DRB1*03, HLA-DRB1*04 and HLA-DQB1*0302. These alleles confer much higher relative risk for childhood than adult onset (DR*03: OR = 3.3, P < 0.0001; DR*04: OR = 22.9, P < 0.0001; DQ*0302: OR = 46.5, P < 0.0001). According to data presented in our studies it can be concluded that HLA-association is agedependent. As expected, the HLA investigation did not show any association with type 2 diabetes mellitus. Analysis of gene expression of HLA-DRB1*04 in circulating antigen presenting cells (Paper No. 8) revealed significant increase of DRB1*04 mRNA in adult T1D compared to T1D manifested in children. We can hypothesize either that the increased expression of HLA-DR transcript is an age dependent factor and its elevated levels may reflect an accumulation of environmental stress or, that the HLA-DR molecules function as a protective factor and in comparison to the HLA-DQ molecules suppress the autoimmune process. Nonetheless, other regulatory factors (also on the epigenetic basis) may be involved in the selective regulation of HLA genes [369]. In addition to the HLA, Paper No. 2 studied an association of IDDM2 locus (INS-VNTR) with three types of DM: adult-onset T1D, LADA and T2D. Rather than an association with disease itself an association with a specific QTL has been outlined: we were able to show that A allele and AA genotype are risk factors involved in fasting Cpeptide secretion since we have observed increased allele and genotype frequencies in CP negative patient group (P < 0.004, OR = 3.3 for A allele and OR = 3.54 for AA genotype). Conversely, the T allele and TT genotype frequencies were decreased in a group of diabetic patients with low or absent C peptide secretion; however, only T allele frequency reached statistical significance (13.8% in CP negative vs. 34.6% in CP positive patient group, P < 0.008). The INS –23HphI A allele thus appears to be associated with a decrease in secretory activity of pancreatic β-cells. 77 With regards to autoantibody production, we have not observed any association between INS-VNTR (–23HphI) polymorphism and levels of GADA. The analysis of potential interaction between the HLA and the INS-VNTR loci suggested that the simultaneous effect of the two gene polymorphisms predisposes affected individuals to an increased risk of diabetes development. The strongest additive effect was notable in T1D for combination of –23HphI AA genotype and HLA DRB1*03 allele. Their simultaneous presence was found in 37.5% cases, whereas in only 9.2% in healthy control subjects (P < 0.007). Next to the HLA, Paper No. 8 studies the association of NFκB (NFKB1) and its inhibitor IκB (NFKBIA) gene polymorphisms with three subgroups of autoimmune diabetes mellitus: T1D in children, T1D in adults, and LADA. We detected 15 alleles of the NFKB1 polymorphism (CA-repeats) in the Czech population. The alleles ranged in size from 114 -142 bp corresponding to 10 – 25 CA dinucleotides. Only the frequency of the A7 allele of NFKB1 (size 132 bp, 20 repeats) has been significantly increased in T1D in adults when compared to healthy subjects (OR = 10.69, P < 0.01). With regards to the A/G variation in 3’UTR region of the NFKBIA, gene encoding IκB, which is a inhibitor of NFκB, significant increase in the frequency of AA genotype was found in LADA group (OR =2.7, P < 0.001). There is also an evidence that heterozygous AG genotype may protect against autoimmune diabetes with late onset, according to the results acquired in the groups of LADA (non-significantly) and adult T1D patients (OR = 0.44, P < 0.01). For the lack of any evidence of functional significance of polymorphisms tested in this study, we assayed the NFKB1 gene expression on the mRNA level in circulating peripheral blood mononuclear cells in diabetic patients with different disease onset. However, the TaqMan-based quantitative real-time PCR did reveal no difference in expression of NFKB1 gene in APC between groups with different NFKBIA and NFKB1 genotypes. Our data reflect the high probability of association Rel/NF-κB family genes with an autoimmune diabetes course, but the function of the genetic variations remains to be further elucidated. 78 Paper No. 3 describes genetic analysis of the E23K polymorphism of KCNJ11 gene in the Czech diabetic patients diagnosed after the age of 35. In spite of several works reporting association of K allele with type 2 diabetes mellitus, we failed to confirm it. Moreover, we were not able to prove any significant correlation between the polymorphism in question and C-peptide secretion. This would be of particular interest given to physiological function of the K channel of pancreatic β-cells. Although we did detect an increase in frequencies of K allele and KK genotype in CP negative patient group, these differences were below statistical significance. Possible explanation for the results observed is that the study suffers from a low statistical power to detect a possible effect. Nevertheless, with some level of caution it might be proposed that the Kir6.2 E23K SNP is a genetic marker associated rather directly with secretory activity of β-cells of pancreas than with the whole disease entity. Case-control approach has been employed also in Papers No. 9 and 10 addressing a role of prolactin and its -1149 G/T SNP in the extrapituitary promoter in the pathogenesis of systemic autoimmune diseases. Paper No. 9 does not report any differences in genotype and allele frequencies of the PRL -1149 G/T polymorphism in patients suffering from systemic lupus erythematosus compared to group of healthy people, and no correlation with common SLE autoantibodies was found. Nonetheless, we were able to detect an association between G allele and articular affection (OR = 2.56, P < 0.01) and besides, age-related differences in genetic predisposition have been observed. In contrast to the above, increased GT heterozygous genotype was detected in patients wit rheumatoid arthritis (OR = 1.8, P < 0.05). Similarly to SLE, we did not identify the association of the PRL -1149 G/T SNP with psoriatic arthritis and its characteristic clinical features (see Paper No. 10), which suggests that non-physiological stress-induced serum prolactin levels found in PsA patients might be due to prolactin release from the pituitary gland rather than its lymphocyte production. 79 3.3 ORIGINAL PAPERS 80 Original paper No. 1 AMPLIFICATION OF NOTCH1 AND ABL1 GENE LOCI IS A FREQUENT ABERRATION IN ENTEROPATHY-TYPE T-CELL LYMPHOMA Cejkova P, Zettl A, Baumgärtner AK, Chot A, Ott G, Müller-Hermelink HK, Starostik P Virchows Arch. 2005 Apr;446(4):416-20 81 Virchows Arch (2005) 446: 416–420 DOI 10.1007/s00428-005-1214-6 ORIGINA L ARTI CLE Pavlina Cejkova . Andreas Zettl . Anne K. Baumgärtner . Andreas Chott . German Ott . Hans Konrad Müller-Hermelink . Petr Starostik Amplification of NOTCH1 and ABL1 gene loci is a frequent aberration in enteropathy-type T-cell lymphoma Received: 21 October 2004 / Accepted: 12 January 2005 / Published online: 9 March 2005 # Springer-Verlag 2005 Abstract We have shown previously that amplification of chromosomal region 9q34 is the most frequent aberration in enteropathy-type T-cell lymphoma (ETL). To determine the minimum amplified 9q34 region and identify possible candidate gene(s), we performed a detailed microsatellite screening and quantitative real-time PCR (QPCR) on 26 ETL cases. Microsatellite analysis revealed allelic imbalance in both ABL1 and NOTCH1 gene loci (microsatellites D9S290–D9S1847 and D9S158 flanking the former and latter genes, respectively) localized in the band 9q34. The results were confirmed by TaqMan-based QPCR showing amplification of ABL1 and NOTCH1 exons in 50% and 65% of cases, respectively. Amplifications of the NOTCH1 gene were more frequent than of the ABL1 gene; moreover, the analyzed NOTCH1 exon consistently displayed higher levels of amplification than ABL1 coding sequences. From 9q34 known genes, NOTCH1 could thus be the primary target of genomic DNA amplification in ETL. Keywords T-cell lymphoma . Genetic analysis . Lymphomagenesis . Coeliac disease . Chromosome 9 . Amplification P. Cejkova . P. Starostik (*) Department of Pathology and Laboratory Medicine, Roswell Park Cancer Institute, Elm & Carlton Streets, Buffalo, NY, 14263, USA e-mail: [email protected] Tel.: +1-716-8458136 Fax: +1-716-8457638 A. Zettl . A. K. Baumgärtner . G. Ott . H. K. Müller-Hermelink Institute of Pathology, Würzburg University, Würzburg, Germany A. Chott Clinical Institute of Pathology, Vienna General Hospital, Vienna, Austria Introduction Enteropathy-type T-cell lymphoma (ETL) is a malignant intestinal lymphoma associated with late presentation, rapid progression, severe side effects (bowel perforation), and absence of specific therapy. Intriguingly, this disease displays the same genetic background as celiac disease [10], namely the HLA genotype DQA1*0501, DQB1*0201 [13], and occurs solely as a sequel of either refractory celiac disease or ulcerative colitis. Little is known about specific aberrations in this disease. Unlike the majority of lymphomas presenting with a typical primary translocation, e.g., t(14;18) in follicular lymphoma, no translocations have yet been found in ETL [4, 24, 30]. In previous studies, we identified the amplification of 9q34 to be the most frequent aberration occurring in ETL. Besides several putative geneencoding sequences, the 9q34 region also encompasses gene loci of two important hematopoietic regulatory genes: ABL1 and NOTCH1. In the presented work, we further delineated and narrowed the region in 9q34 implicated in the amplifications and show that both ABL1 and NOTCH1 genes are affected by genomic DNA amplification in 9q34. Materials and methods Patients and samples For the study, 26 ETL cases from cancer registries at the Institute of Pathology of the Würzburg University in Würzburg, Germany, and the Clinical Institute of Pathology, Vienna General Hospital, Vienna, Austria, on which sufficient both tumor and normal (mostly muscle) tissue was available, were selected. The samples were exclusively paraffin-embedded/formalin-fixed blocks. The diagnosis was established according to the criteria of the World Health Organization (WHO) Classification of Lymphoid Tumors [15] by means of morphological and immunophenotypic analyses of tissue sections using standard staining methods. 417 Microscopic dissection and DNA extraction In each case, 16 serial 10-μm-thick tissue sections were cut. The first and last cuts were stained with hematoxylin and eosin to assure high tumor content and as guidance for the following microdissection. Remaining tissue sections were stained with Nuclear-Fast Red to precisely delineate tumorcontaining areas. Under microscope, areas showing ETL were scraped to be used for microsatellite analysis. The collected tissue was deparaffinized with xylene before digestion. DNA extraction was performed using proteinase K and phenol-chloroform according to routine molecular biology protocols. A polymerase chain reaction (PCR)-based clonality analysis assay for TCR-γ gene rearrangement [29] was performed on all DNA samples to assure the control normal DNA did not contain the lymphoma clone. Microsatellite analysis Microsatellite analysis was performed using a panel of six repeats: D9S1821, D9S290, D9S1847, D9S1818, D9S1826, and D9S158 (Ensembl, Sanger Institute; http://www.sanger. ac.uk). Primer sequences for the amplification of microsatellites were retrieved from Genome Database (http://www. gdb.org). PCR primers were synthesized at MWG Biotech (High Point, NC), and one oligonucleotide of each primer pair was labeled with fluorescent dye phosphoramidites FAM or HEX. Thirty cycles were carried out in a PE-9600 thermal cycler (ABI, Foster City, CA) in a total volume of 20 μL. Aliquots of the PCR reactions were then mixed with size standard and formamide, denatured, and subjected to electrophoresis on an ABI 3100 DNA Sequencer (ABI, Foster City, CA). The automatically collected data were analyzed using GeneScan and Genotyper software as described in the manufacturer’s manual. Only genotypes heterozygous for a given locus were regarded as informative; homozygosity and microsatellite instability (MSI) rendered the particular locus unevaluable for loss of heterozygosity (LOH) or amplification. In heterozygous genotypes, ratios of both alleles in normal and tumor tissues were calculated. If these ratios showed a difference of more than 40%, the genotype was determined to display allelic imbalance. All aberrations were confirmed by repeating the experiments. For further determination of LOH or amplification in a locus, first the unchanged allele was identified (by comparison with other microsatellites showing no change in the same multiplex PCR) and then the ratios of the allele showing decreased or increased signal to the unchanged allele were calculated; first for control DNA and then for the tumor. Increase of the ratio by 40% in the tumor (compared with the control) was considered amplification; a decrease by 40% was considered LOH. Quantitative PCR Quantitative PCRs were performed in triplicate on the ABI 7700 Sequence Detection System instrument (ABI, Foster City, CA) under standard conditions using the TaqMan Universal PCR Master Mix (ABI, Foster City, CA). DNA quantities were normalized using two reference loci located on chromosomes usually unaltered in ETL: D6S310 and D14S79. Primers for target loci (ABL1, NOTCH1) were designed to amplify regions spanning across exon–intron boundaries. TaqMan probes labeled on the 5′ end with 6′ carboxy fluorescein (FAM) and on the 3′ end with TAMRA and primers were synthesized by MWG Biotech (High Point, NC). Primer sequences: ABL1 (intron 6–7/exon 7) forward primer 5′AGGAGCTCTCATGGGTGAACA 3′, reverse primer 5′ GTTCTCCCCTACCAGGCAGTT 3′, probe (FAM)-5′ CCTTTCTTAGAGATCTTGCTGCCC 3′(TAMRA); NOTCH1 (intron 22–23/exon 23) forward primer 5′ GCTTGGGCCACTGACGAA 3′, reverse primer 5′ AGTCGTCCACGTTGATCTCACA 3′, probe (FAM)-5′ TGCACACCTGCGGGGCCA 3′-(TAMRA); D6S310 forward primer 5′ CCAGGGCTACACTTTCAGGTTT 3′, reverse primer 5′ GGAAGGGCTAGGAAGTGCTATG 3′, probe (FAM)-5′ TGCCTTCTGCAATCTGGATCCCAAA 3′-(TAMRA); D14S79 forward primer 5′ TGAGACGG AGTCTCGCTCTGT 3′, reverse primer 5′ TCCAAAAGT CAGAGGTTTCAGTGA 3′, probe (FAM)-5′ CACGCC ATTGCACTCCAGCCTG 3′-(TAMRA). Sample DNAs were compared with standard DNA (calibrator) pooled from 23 unrelated normal individuals using the relative quantification ΔΔCt technique [19]. DNA copy numbers greater than 2.8 were considered to be gains and those less than 1.2 losses. Results We previously performed comparative genomic hybridization (CGH) and microsatellite studies on a group of 26 ETL specimens and detected several consistent abnormalities. The most frequent aberration identified in this lymphoma was the amplification of chromosomal region 9q34. This amplification was detected by CGH in 58% of the examined lymphomas [32] and in 40% by microsatellite analysis [2]. To analyze this amplification further we carried out a detailed analysis of the 9q34 region using six microsatellite markers: D9S1821, D9S290, D9S1847, D9S1818, D9S1826, and D9S158. These repeats cover approximately 10 Mbp of the amplified region (Ensembl, Sanger Institute; http://www.sanger.ac.uk) and are on average about 2 Mbp apart. Microsatellite results revealed allelic imbalance in 16 of 26 studied cases (62%) (Fig. 1). To reliably distinguish LOH from genomic amplification, multiplex PCR reactions with the marker showing allelic imbalance and minimally one other marker used as an internal control were performed. Additionally, the microsatellite analysis results were compared with CGH data, which were available on 58% of these tumors. None of the amplifications as determined by microsatellite analysis appeared as loss of signal by CGH. Genotypes displaying allelic imbalance in the 9q34 region centered around the ABL1 gene locus (D9S290–D9S1847) in a subgroup of ETL. However, in 418 other cases, multiple discontinuous allelic imbalances involving both the ABL1 and NOTCH1 loci could be seen. There are several genes or putative gene-coding sequences localized in the 9q34 chromosomal band. Two of them, ABL1 and NOTCH1, are genes very well known for their key roles in hematopoietic malignancies and would be thus primary candidates for gene(s) dysregulated in ETL. To further ascertain which of the known genes located between or in close vicinity to repeats showing the most aberrations would be a good candidate for genomic DNA amplification and to confirm the microsatellite analysis results we performed a TaqMan-based QPCR. Using primers spanning exon–intron boundaries, we analyzed ABL1 exon 7 and NOTCH1 exon 23 for copy number amplifications. The results revealed that NOTCH1 genomic DNA was more frequently amplified then the ABL1 gene locus (65% and 50%, respectively, Fig. 1). NOTCH1 also consistently showed higher levels of amplification than ABL1 (median 4.1 and 2.82 DNA copies, respectively, Fig. 2). It thus seems that the NOTCH1 gene locus could be the primary target of genomic DNA amplification in the region 9q34 in ETL. Discussion Fig. 1 Genetic aberrations in 9q34 by microsatellite analysis and quantitative real-time polymerase chain reaction (QPCR). Rows individual cases, columns microsatellites or QPCR assay targets analyzed (with their position on chromosome 9 depicted according to the Ensembl database). Black genomic DNA amplification; falling stripes homozygote; ascending stripes heterozygote; vertical stripes microsatellite instability; NA no PCR amplificate. Only cases displaying allelic imbalances or those positive for genomic DNA amplification by QPCR are shown Fig. 2 Amplification of ABL1 and NOTCH1 gene loci by quantitative real-time polymerase chain reaction (QPCR). X axis represents each case; Y axis shows genomic DNA copy number of NOTCH1 (exon 23) and ABL1 (exon7) gene loci (empty boxes and black triangles, respectively; log scale). S=standard pooled DNA showing two DNA copies for both analyzed loci Previously, we reported amplification of 9q34 to be the most frequent aberration found in ETL. In this study, we further narrowed down the minimum amplified region in the chromosomal band 9q34. The results of microsatellite analysis performed on the lymphomas actually favored the ABL1 as primary target of the amplifications. However, in multiple cases, the allelic imbalances were discontinuous, not showing any consistent pattern. We therefore analyzed ABL1 exon 7 and NOTCH1 exon 23 for copy number amplifications by means of QPCR using TaqMan probes. Interestingly, the number of amplification-positive cases went up as the QPCR detected the ABL1 or NOTCH1 gene amplifications in cases negative by microsatellite analysis. The percentage of cases positive for the ABL1 amplification in- 419 creased from 38.5% (D9S290) or 30.8% (D9S1847) to 50%; the NOTCH1 amplification-positive cases went up from 15.4% (D9S158) to 65.4%. Both these hematopoietically significant genes thus seem to be targeted by the amplifications. Such amplifications of genomic DNA could be one mechanism of how to induce their permanent overexpression, resulting in constitutive signaling that could predispose to ETL. ABL1 is a ubiquitously expressed tyrosine kinase that is fused to BCR in CML and precursor B-ALL cases with the t (9;22)(q34;q11) translocation [6, 7], to ETV6 in leukemias with the t(9;12)(q34;p13) translocation [11] and to NUP214 in episomes consisting of ABL1 and NUP214 amplicons in T-cell acute lymphoblastic leukemia (T-ALL) [12]. Exclusive amplification of ABL1 was described in the CML-derived cell line K562 [5], in a patient with CML in lymphoid blast crisis [18], and in three cases of secondary AML [28]. Recently, amplification involving the ABL1 gene was described in several cases of T-ALL. The fusion gene products of the above-mentioned translocations deregulate pathways controlling cell growth and cell cycling. In normal cells, the ABL1 oncogene has been implicated in processes of cell differentiation, cell division, cell adhesion, and stress response. Activity of the ABL1 protein is negatively regulated by its SH3 domain, and deletion of the SH3 domain turns ABL1 into an oncogene. Its DNA-binding activity is regulated by CDC2-mediated phosphorylation, suggesting a cell cycle function for ABL1. So far, it is not completely clear how ABL1 contributes to oncogenesis. Several possible ways of signaling through downstream targets involving PI3-kinase, RAS, RAF and the proapoptotic protein BAD [27], JAK2 tyrosine kinase [31], MYC and anti-apoptotic BCL2 [16] were outlined. Up to this day, it has not been fully elucidated whether only a quantitative change in expression (overexpression) of wild-type ABL1 is sufficient to render the same or similar phenotype as fused BCR-ABL1 or other mutants of the ABL1 gene itself. Unlike ABL1, the NOTCH1 gene encodes a transmembrane receptor, a critical regulator of multiple developmental pathways. It has the capacity to induce apoptosis in B cells [20], whereas it promotes survival in T cells [8]. Its role in leukemogenesis was identified as early as in 1991 by Ellisen et al. [9] who described a t(7;9)(q34;q34.3) in TALL, causing overexpression of truncated NOTCH1 mRNA missing the anchoring amino terminus under the influence of the T-cell receptor promoter. Such NOTCH1 activation leads to leukemia induction as shown previously [1, 25]. Activated NOTCH1 signaling mediated by the wildtype protein has been shown to play a role in pathogenesis of anaplastic large cell lymphoma (ALCL) [17], where NOTCH1 is highly expressed and its interaction with Jagged1 through homotypic or heterotypic cell–cell interactions was shown to directly contribute to lymphomagenesis. Signaling through nuclear transcriptional repressor CSL, known also as RBP-Jκ, stimulates the transcription of downstream target genes such as HES1 and HES5 hairy enhancer of split and HRT/HERP [14, 22]. However, genetic evidence points also to the existence of a so-far poorly characterized CSL-independent signaling pathway [3, 23]. Recently, it was shown that activated NOTCH1 inhibits p53-induced apoptosis through a PI3K-PKB/Akt-dependent pathway [21, 26]. PI3-kinase thus could represent a common pathway involved in both ABL1 and NOTCH1 downstream signaling. Synergistic activation of this pathway by both ABL1 and NOTCH1 could potentiate their impact on inhibition of apoptosis and facilitate cell cycle deregulation by ABL1. However, such hypotheses require further investigations. The answer will surely yield further insights into molecular mechanisms underlying the development of ETL. Nevertheless, there is still the possibility remaining that a different unknown oncogene is the target of amplifications in the 9q34 chromosomal region. The amplifications in the 9q34 are concentrated around the ABL1 gene between the D9S290 and D9S1847 microsatellites (3.8 Mbp apart). This interval contains numerous genes and ETSs. However, we found multiple discontinuous amplifications in the 9q34 chromosomal band in 27% of analyzed ETLs. These randomly appearing amplifications do not pinpoint any particular specific interval in 9q34 as a locus of the amplified oncogene. We have shown previously that amplification of 9q34 is the most frequent allelic imbalance in ETL. In the present work, we identified two genes affected by the 9q34 amplification, ABL1 and NOTCH1, with the latter the most frequently amplified (two-thirds of cases). This finding is of interest, as it would point future investigations toward exploration of the role of NOTCH1 in the pathogenesis of this disease. 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Am J Pathol 161:1635–1645 Original paper No. 2 HLA DRB1, DQB1 AND INSULIN PROMOTER VNTR POLYMORPHISMS: INTERACTIONS AND THE ASSOCIATION WITH ADULT-ONSET DIABETES MELLITUS IN CZECH PATIENTS Cejkova P, Novota P, Cerna M, Kolostova K, Novakova D, Kucera P, Novak J, Andel M, Weber P, Zdarsky E Int J Immunogenet. 2008 Apr;35(2):133-40. 87 doi: 10.1111/j.1744-313X.2008.00749.x HLA DRB1, DQB1 and insulin promoter VNTR polymorphisms: interactions and the association with adult-onset diabetes mellitus in Czech patients Blackwell Publishing Ltd P. Cejkova,* P. Novota,* M. Cerna,* K. Kolostova,* D. Novakova,† P. Kucera,† J. Novak,‡ M. Andel,‡ P. Weber§ & E. Zdarsky¶ Summary Both the human leucocyte antigen (HLA) DRB1 and the HLA DQB1 gene loci play a role in the development and progression of autoimmune diabetes mellitus (T1DM). Similarly, the insulin promoter variable number tandem repeats (INS-VNTR) polymorphism is also involved in the pathogenesis of diabetes mellitus (DM). We studied the association between each of these polymorphisms and DM diagnosed in patients older than age 35 years. Furthermore, we analysed possible interactions between HLA DRB1/DQB1 and INS-VNTR polymorphisms. Based on C-peptide and GADA levels we were able to distinguish three types of diabetes: T1DM, latent autoimmune diabetes in adults (LADA) and T2DM. INS-VNTR was genotyped indirectly by typing INS –23HphI A/T polymorphism. The genotype and allele frequencies of INS –23HphI did not differ between each of the diabetic groups and group of healthy subjects. We did, however, observe an association between the INS –23HphI alleles, genotypes and C-peptide secretion in all diabetic patients: A allele frequency was 86.2% in the C-peptide-negative group vs. 65.4% in the C-peptide-positive group (Pcorr. < 0.005); AA genotype was found to be 72.4% in the C-peptide-negative group vs. 42.6% in the C-peptidepositive groups (Pcorr. < 0.01). The HLA genotyping revealed a significantly higher frequency of HLA DRB1*03 allele in both T1DM and LADA groups when compared to healthy subjects: T1DM (25.7%) vs. control group * Department of Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Prague, Czech Republic, † Department of Cell and Molecular Immunology, Third Faculty of Medicine, Charles University, Prague, Czech Republic, ‡ Diabetes Center and Department of Internal Medicine, Third Faculty of Medicine, Charles University, Prague, Czech Republic, § Department of Internal Medicine, Geriatrics and General Medicine, University Hospital Brno, Brno, Czech Republic, and ¶ Department of Pharmacology, Third Faculty of Medicine, Charles University, Prague, Czech Republic Received 4 November 2007; revised 4 November 2007; accepted 9 December 2007 Correspondence: Pavlina Cejkova, Department of Cell and Molecular Biology, Institute of Biochemistry, Cell and Molecular Biology, Third Faculty of Medicine, Charles University, Ruska 87, 10034 Prague 10, Czech Republic. Tel: +420 267 102540; Fax: +420 267 102650; E-mail: [email protected] (10.15%), odds ratio (OR) = 3.06, P < 0.05; LADA (27.6%) vs. control (10.15%), OR = 3.37, P < 0.01. The simultaneous presence of both HLA DRB1*04 and INS –23HphI AA genotype was detected in 37.5% of the T1DM group compared to only 9.2% of the healthy individuals group (OR = 5.9, Pcorr. < 0.007). We summarize that in the Central Bohemian population of the Czech Republic, the INS –23HphI A allele appears to be associated with a decrease in pancreatic beta cell secretory activity. HLA genotyping points to at least a partial difference in mechanism, which leads to T1DM and LADA development as well as a more diverse genetic predisposition in juvenile- and adult-onset diabetes. The simultaneous effect of HLA and INS-VNTR alleles/ genotypes predispose individuals to an increased risk of diabetes development. Introduction The aetiology of diabetes mellitus (DM) is very complex, as this disease has both polygenetic and epigenetic factors, which participate in its development. The contribution of each of several genes to this complex disease state is likely to be small; only the combined effect of several susceptibility genes, often in concert with predisposing environmental factors, will lead to the disease phenotype. Moreover, interactions at the gene–gene as well as gene–environment level will play a significant role in development of polygenetic disorders. Diabetes mellitus is represented by two major types: autoimmune DM and metabolic diabetes. Autoimmune diabetes is characterized by insulin deficiency relating to an autoimmune-destruction of pancreatic β-cells (type 1 diabetes mellitus (T1DM) or insulin-dependent, IDDM) (Atkinson & Maclaren, 1994). The strongest genetic association with T1DM identified so far, is that of human leucocyte antigen (HLA) class I and class II, more specifically DRB1*03-DQA1*0501-DQB1*0201 (HLA-DR3 haplotype) and/or HLA DRB1*04-DQA1*0301-DQB1*0302 (HLA-DR4 haplotype) (Huang et al., 1996; Cox et al., 2001). The HLA gene complex was mapped to the short arm of chromosome 6 and designated as IDDM1. The second strongest association has been that of the INS-VNTR (variable number of tandem repeats) polymorphism at the promoter region of the insulin gene (INS), designated as © 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 133–140 133 134 P. Cejkova et al. IDDM2 (Bennett & Todd, 1996). The risk associated with the INS-VNTR class I alleles for the T1DM phenotype is for the most part unquestioned (Bennett & Todd, 1996). Metabolic diabetes, which is characterized by insulin resistance, is also known as type 2 diabetes mellitus (T2DM) or non-insulin-dependent diabetes mellitus (NIDDM) (Porte & Kahn, 2001; Kahn, 2003). The genetic factors contributing to T2DM are much less explored; though not all of the molecules involved in the action of insulin have been studied, some recent large-scale T2DM genetic studies have succeeded in establishing several other ‘DM-loci’. Among the wellestablished candidates are genes encoding peroxisome proliferator-activated receptor γ (PPARG) (Altshuler et al., 2000), calpain 10 (CAPN10) (Horikawa et al., 2000) and Kir6.2 (KCNJ11), representing one of the two subunits of the pancreatic β-cell ATP-sensitive K+ channel (Hani et al., 1998; Yamada et al., 2001; Gloyn et al., 2003). Interestingly, INS-VNTR has been intensively studied not only for its association with T1DM, but also for its association with T2DM, birth size, obesity and polycystic ovary syndrome (Bennett & Todd, 1996; Waterworth et al., 1997; Dunger et al., 1998). These associations stem from the potential impact that VNTR has on the transcriptional regulation of the insulin gene in both the thymus and the pancreas (Vafiadis et al., 1996, 1997). Even though INS-VNTR has been reported to play a role in the development of T2DM, some studies show conflicting data (Huxtable et al., 2000; Hansen et al., 2004; Meigs et al., 2005). The aim of this study is to investigate the INS-VNTR, HLA DQB1 and HLA DRB1 loci with regard to their roles in the development and progression of DM in patients from Central Bohemia of the Czech Republic with disease onset occurring after age 35. Furthermore, we aimed to explore potential interactions between the HLA and the INS-VNTR loci. age or older when first diagnosed with DM. Patients were selected from an epidemiological study conducted at the Center for Diabetes at the Third Faculty of Medicine, Charles University in Prague, and from several outpatient diabetic clinics in Prague and Melnik. The diagnosis of diabetes was established according to the current WHO criteria, which consider both the patients’ clinical features and laboratory data; including the presence of antibodies against glutamic acid decarboxylase GAD65 (GADA) and serum C-peptide levels (American Diabetes Association, 2006). Patients were divided into three groups: (i) late-onset T1DM, which required insulin therapy within 6 months after diagnosis and showed a C-peptidenegative (CP–), GADA–/+profile; (ii) latent autoimmune diabetes in adults (LADA), defined as C-peptide-positive (CP+), GADA+ and having a minimum 6 months between diagnosis and initiation of insulin therapy; (iii) T2DM with CP+, GADA– values. Patients’ clinical data are presented in Table 1. The control group consisted of 124 healthy individuals randomly selected from volunteers at the Blood Donors Center. This study was approved by the Ethical Committee from the Third Faculty of Medicine, Charles University in Prague, and all patients signed the appropriate informed consent forms before being allowed to participate. Fasting C-peptide assessment Concentrations of fasting C-peptide were measured by radioimmunoassay (IRMA, Immunotech Czech Republic) in plasma samples stored at –20 °C. Plasma samples from 49 healthy individuals were analysed to establish control values: average 486 pmol L–1, minimum–maximum values 206–934 pmol L–1. Based on these control test findings, a 200 pmol L–1 or higher was considered physiological and designated CP+. GADA detection Methods Subjects Our study included 300 patients with diabetes recruited from the Central Bohemia region of the Czech Republic. In order to participate, study subjects must be 35 years of Antibody assessment was performed as described previously (Kucera et al., 2003). Briefly, the presence of IgG antibodies against GAD65 was confirmed by commercial ELISA test (Roche Applied Science, Mannheim, Germany), which has a 98% specificity and 69% sensitivity levels. Values lower than 32 ng mL–1 were considered Table 1. Clinical features of diabetic patients BMI (kg m–2) Age at onset (years) Duration of diabetes (years) Mean C-peptide at onset (pmol L–1)a Mean GADA (ng mL–1)b T1DM n = 44 LADA n = 43 T2DM n = 213 27.4 (22.4–36.7) 43 (36–55) 18.6 (2–50) 47.3 (0.5–194) 265.9 (2.76–3000) 31.6 (26.6–45.5) 53 (35–71) 15.4 (4–32) 542.2 (204.5–1522) 554.5 (50.4–2000) 31.4 (18.4–50.1) 53 (35–81) 13.4 (1–65) 796 (202–3369) 7.56 (0.29–49.87) All data shown as mean with minimum and maximum value; a C-peptide cut-off level: 200 pmol L–1; b GADA cut-off level: 50 ng mL–1. BMI, body mass index; GADA, glutamic acid decarboxylase GAD65; LADA, latent autoimmune diabetes in adults; T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 133–140 HLA DRB1, DQB1 and INS-VNTR in diabetes negative. In order to increase the precision, the value of 50 ng mL–1 was used as the lower level cut-off and values higher than 50 ng mL–1 (referred to as GADA+) were considered a reliable sign of autoimmune insulitis. 135 by using an ethidium bromide staining method. The sequences of the original primers are available from the authors upon request. Statistical evaluation Genomic DNA preparation Genomic DNA from peripheral blood samples was extracted by QiaAmp DNA Maxi Kit spin columns (QIAGEN GmbH, Hilden, Germany) according to the manufacturer’s instructions. Analysis of HLA DRB1 and DQB1 alleles HLA DRB1 and DQB1 genotyping was performed by polymerase chain reaction (PCR) with sequence-specific primers (SSP-PCR) (Zetterquist & Olerup, 1992; Olerup et al., 1993) supplied by Genovision (Oslo, Norway). INS-VNTR genotyping The INS –23HphI SNP polymorphism, which is in a 99.7% linkage disequilibrium with both the INS-VNTR class I allele (A allele of –23HphI) and the class III allele (T allele of –23HphI) (Bennett & Todd, 1996), is often used for indirect subtyping of INS-VNTR. The PCRbased restriction fragment length polymorphism (RFLP) method employed originally designed oligonucleotides INSM and INSO, which amplified product 159 bp long under the following PCR conditions. The PCR was carried out using a total volume of 20 μL, of which contained 0.1 μg of genomic DNA, 1.5 mm Mg2+, 1000 nm of each primer, 10 mm dNTPs, 10× PCR buffer with (NH4)2SO4 and 1 U Taq DNA polymerase (Fermentas GmbH, St. LeonRot, Germany). The PCR process started with denaturation at a temperature of 94 °C for 2 min, followed by a 3-step cycle of 94 °C for 20 s, 64 °C for 20 s and 72 °C for 20 s. This process was repeated 35 times. After PCR amplification, Alw26I digestion of the amplified DNA was carried out at a temperature of 37 °C for 2 h and was stopped by heat inactivation (65 °C for 20 min). Following electrophoresis, both the digested and the undigested products were visualized in a 4% agarose gel EpiInfo software (version 3.3, October 2004, Atlanta, Georgia) was used for statistical analysis. Allele and genotype frequencies were determined by using a direct counting method. Statistical differences in allele or genotype distributions were analysed by either a χ2 or a Fisher’s exact probability test. Significance was defined by using a Bonferroni-corrected P-value < 0.05. The strength of the observed associations was estimated by calculating the odds ratios (OR), according to Wolf’s method with a Holdane’s correction. Briefly, it was calculated as (a × d)/ (b × c), where a, b, c and d are the numbers of markerpositive patients, marker-negative patients, markerpositive controls and marker-negative controls, respectively (Thomson, 1981). Results Genotype distribution of INS –23HphI in patients with diabetes and control subjects The case–control study showed no significant difference in INS-VNTR allele frequencies or genotype distribution (tested indirectly by typing –23HphI SNP) between T1DM patients (frequency of A allele ~ INS-VNTR class I allele: 86.2%, Pcorr. = not significant, NS), LADA (64.5%, Pcorr. = NS), T2DM (65.9%, Pcorr. = NS) and the control group (73.7%). All genotype distributions (shown in Table 2) were consistent with Hardy–Weinberg equilibrium. We also compared allele and genotype frequencies within all groups with diabetes and found statistically significant differences between T1DM and the two other types of diabetes: A allele frequency in T1DM (86.2%) vs. LADA (64.5%, Pcorr. = 0.03) and vs. T2DM (64.9%, Pcorr. = 0.01); AA genotype frequency in T1DM (72.4%) vs. LADA (41.9%; Pcorr. = NS) and vs. T2DM (42.7%, Pcorr. = 0.02). Table 2. Frequency of alleles and genotypes of INS –23HphI polymorphism in adult patients with diabetes and control subjects Alleles and genotypes Locus INS –23HphI (A/T) A T AA AT TT Control subjects T1DM Pa LADA Pb T2DM Pc 174 (73.7%) 62 (26.3%) 64 (54.2%) 46 (39%) 8 (6.8%) 50 (86.2%) 8 (13.8%) 21 (72.4%) 8 (27.6%) 0 (0%) NS NS NS NS NS 40 (64.5%) 22 (35.5%) 13 (41.9%) 14 (45.2%) 4 (12.9%) NS NS NS NS NS 216 (65.9%) 112 (34.1%) 70 (42.7%) 76 (46.3%) 18 (11%) NS NS NS NS NS For statistical analysis 2 × 2 contingency tables comparing type 1 diabetes mellitus with control subjects were used. For statistical analysis 2 × 2 contingency tables comparing latent autoimmune diabetes in adults with control subjects were used. c For statistical analysis 2 × 2 contingency tables comparing type 2 diabetes mellitus with control subjects were used. a b © 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 133–140 136 P. Cejkova et al. either allele or genotype frequencies when we compared the GADA+ and the GADA– groups (Table 3). INS –23HphI and levels of C-peptide and GADA We studied the INS –23HphI genotype distribution and allele frequencies in patients, who were divided according to their fasting C-peptide and GADA levels (Table 3). There was a statistically significant difference in both allele and genotype frequencies between the CP+ and the CP– patient groups. This effect was independent of (lack of) antibody production. The predisposing A allele was detected in 86.2% of CP– patients vs. only 65.4% of CP+ patients (Pcorr. = 0.004, OR = 3.27). The AA genotype was identified in 72.4% of CP– patients vs. 42.6% in CP+ patients (Pcorr. = 0.008, OR = 3.54). In contrast to the above findings, we observed no significant difference in HLA DRB1 and HLA DQB1 genes in patients with diabetes and control subjects Table 4 presents DQB1 and DRB1 allele frequencies and compares each of the groups with diabetes with the control group. Only the HLA DQB1*03 alleles were subjected to further subtyping. We observed an increase in the frequency of DQB1*0302 alleles in T1DM (20.5%) when comparing to the control group (12.8%). However, this difference did not reach statistical significance after Bonferroni correction was applied. Nevertheless, we saw Table 3. Frequency of alleles and genotypes of INS –23HphI polymorphism between patients with diabetes characterized by C-peptide and GADA levels Alleles and genotypes Locus INS –23HphI (A/T) A T AA AT TT CP– (n = 29) CP+ (n = 195) P (OR) GADA– (n = 180) GADA+ (n = 44) P (OR) 50 (86.2%) 8 (13.8%) 21 (72.4%) 8 (27.6%) 0 (0%) 256 (65.4%) 134 (34.6%) 83 (42.6%) 90 (46.1%) 22 (11.3%) 0.004; OR = 3.27 (1.44–7.7) 0.004; OR = 0.31 (0.13–0.69) 0.008; OR = 3.54 (1.4–9.2) NS NS 245 (68.1%) 115 (31.9%) 83 (46.1%) 79 (43.9%) 18 (10%) 61 (69.3%) 27 (30.7%) 21 (47.7%) 19 (43.2%) 4 (9.1%) NS NS NS NS NS CP–, C-peptide-negative; CP+, C-peptide-positive; GADA, glutamic acid decarboxylase GAD65; NS, not significant; OR, odds ratio. Table 4. Frequency of alleles and genotypes of HLA DQB1 and HLA DRB1 in control subjects and patients with T1DM, LADA and T2DM diagnosed after the of age 35 Locus Control subjects (n = 124) T1DM (n = 41) 30 (37.5%) 25 (31.25%) 15 (20.5%)d 0 (0%) 14 (17.5%) 11 (13.75%) (n = 37) 10 (13.5%) 19 (25.7%) HLADQB1 alleles DQB1*02 DQB1*03 DQB1*0302 DQB1*04 DQB1*05 DQB1*06 HLADRB1 alleles DRB1*01 DRB1*03 61 (24.8%) 88 (35.8%) 24 (12.8%)d 3 (1.2%) 38 (15.4%) 56 (22.8%) (n = 107) 19 (9.2%) 21 (10.15%) DRB1*04 21 (10.15%) 19 (25.7%) DRB1*07 DRB1*08 DRB1*09 DRB1*10 DRB1*11 DRB1*12 DRB1*13 DRB1*14 DRB1*15 DRB1*16 33 (15.9%) 6 (2.9%) 2 (1.0%) 1 (0.5%) 29 (14.0%) 2 (1.0%) 29 (14.0%) 7 (3.4%) 33 (15.9%) 4 (1.9%) 8 (10.8%) 2 (2.7%) 0 (0%) 2 (2.7%) 5 (6.8%) 0 (0%) 4 (5.4%) 0 (0%) 4 (5.4%) 1 (1.3%) Pa NS NS NSd NS NS NS NS < 0.05; OR = 3.06 (1.45–6.44) < 0.05; OR = 3.06 (1.45–6.44) NS NS NS NS NS NS NS NS NS NS LADA (n = 31) 18 (30%) 20 (33.4%) 4 (8.2%)d 2 (3.3%) 12 (20%) 8 (13.3%) (n = 29) 5 (8.6%) 16 (27.6%) 7 (12.1%) 4 (6.9%) 3 (5.2%) 1 (1.7%) 0 (0%) 10 (17.3%) 0 (0%) 4 (6.9%) 1 (1.7%) 6 (10.3%) 1 (1.7%) Pb NS NS NSd NS NS NS NS < 0.01; OR = 3.37 (1.53–7.45) NS NS NS NS NS NS NS NS NS NS NS T2DM (n = 158) Pc 73 (23.2%) 106 (33.7%) 23 (8.6%)d 8 (2.5%) 60 (19.0%) 68 (21.6%) (n = 90) 16 (8.9%) 15 (8.3%) NS NS NSd NS NS NS 24 (13.3%) NS 29 (16.1%) 8 (4.4%) 3 (1.7%) 3 (1.7%) 25 (13.9%) 2 (1.1%) 16 (8.9%) 15 (8.3%) 19 (10.6%) 5 (2.8%) NS NS NS NS NS NS NS NS NS NS NS NS For statistical analysis 2 × 2 contingency tables comparing T1DM with control subjects were used. For statistical analysis 2 × 2 contingency tables comparing LADA with control subjects were used. c For statistical analysis 2 × 2 contingency tables comparing T2DM with control subjects were used. d For statistical analysis 2 × 2 contingency tables were used that compared HLADQB1*0302 allele vs. non-HLADQB1*0302 alleles (only subtyped alleles). LADA, latent autoimmune diabetes in adults; NS, not significant; T1DM, type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus. a b © 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 133–140 HLA DRB1, DQB1 and INS-VNTR in diabetes 137 a significant increase in the frequency of HLA DRB1*03 and HLA DRB1*04 alleles in patients with T1DM (both alleles detected in 25.7% in T1DM vs. only 10.15% in control, Pcorr. < 0.05, OR = 3). Furthermore, the LADA group showed higher levels of DRB1*03 when compared to the control group of healthy individuals (27.6% vs. 10.15%, respectively; Pcorr. < 0.01, OR = 3.4). genotype was detected in the healthy control group in 39.8% of subjects, whereas only 16.7% of individuals showed this absence in the T1DM group (OR = 0.3, P < 0.05, Pcorr. = NS). Those subjects with T1DM possessing a combination of both risk markers DRB1*04 and –23HphI AA showed a significant risk increase (OR = 5.9, Pcorr. < 0.007). Data are summarized in Table 6. HLA allele frequencies and levels of C-peptide and GADA Discussion In order to study HLA DQB1 and DRB1 allele frequencies in association with these clinical features, all patients with diabetes had to be divided according to C-peptide and GADA levels. We did a separate comparison with each of the patient groups and the healthy control subjects and our findings highlighted two risk alleles in the CP– patient group: DRB1*03 (27.9% vs. 10.15% in control; Pcorr. < 0.001, OR = 3.43) and DRB1*04 (25.6% vs. 10.15% in control; Pcorr. < 0.001, OR = 3.04). In the GADA+ group there was one predisposing allele detected, DRB1*03 (Pcorr < 0.0001, OR = 4.24) (Table 5). We performed extensive genotyping for two HLA genes and the INS –23HphI single nucleotide polymorphism in 300 diabetic patients, all of whom developed their disease after age 35. This association was studied by comparing patients’ allele and genotype frequencies with that of a healthy control group consisted of 124 subjects. The contributing coauthors have published their preliminary findings elsewhere (Cerna et al., 2003; Novota et al., 2004). The INS –23HphI promoter polymorphism did not show to have any association with any group of patients with DM diagnosed after age 35. Since the role of INSVNTR in the development of T1DM is generally accepted (Bennett & Todd, 1996; Perez et al., 2003; Barratt et al., 2004; Guja et al., 2004), a possible explanation for the above discrepancy centres around the age of onset. It is likely that the late onset of the disease may be caused, or at least modified, by gene loci different from the ones associated with juvenile onset, as shown in HLA (Cerna et al., 2003; Murao et al., 2004; Cerna et al., in press). HLA DQB1 is considered to be the main T1DM predisposing HLA DRB1*03 and HLA DRB1*04 interactions with INS –23HphI The simultaneous combination of the DRB1*03 and the INS –23HphI T allele was found in 26.9% of the LADA patients and in only 7.1% of the control subjects (OR = 4.8, P < 0.005, Pcorr. = NS). The simultaneous absence of the DRB1*03 allele and the INS –23HphI AA Table 5. Allele frequencies of HLA DQB1 and HLA DRB1 polymorphisms in diabetic patients characterized by C-peptide and GADA levels Locus HLADQB1 alleles DQB1*02 DQB1*03 DQB1*0302 DQB1*04 DQB1*05 DQB1*06 Locus HLADRB1 alleles DRB1*01 DRB1*03 DRB1*04 DRB1*07 DRB1*08 DRB1*09 DRB1*10 DRB1*11 DRB1*12 DRB1*13 DRB1*14 DRB1*15 DRB1*16 CP– (n = 49) CP+ (n = 153) GADA– (n = 182) GADA+ (n = 57) Controls (n = 124) 35 (37.2%) 30 (31.9%) 19 (24.7%)b 1 (1.1%) 17 (18.1%) 11 (11.7%); OR = 0.4c 92 (24.3%) 127 (33.5%) 34 (12.1%)b 10 (2.6%) 73 (19.3%) 77 (20.3%) 85 (23.6%) 121 (33.6%) 39 (12.4%)b 8 (2.2%) 75 (20.85%) 71 (19.7%) 42 (37.8%) 35 (31.5%) 13 (14.9%)b 3 (2.7%) 14 (12.6%) 17 (15.4%) 61 (24.8%) 88 (35.8%) 24 (12.8%)b 3 (1.2%) 38 (15.4%) 56 (22.8%) CP– (n = 43) CP+ (n = 122) GADA– (n = 111) GADA+ (n = 51) Controls (n = 107) 8 (7.8%) 33 (32.3%); OR = 4.24e 16 (15.7%) 6 (5.9%) 5 (4.9%) 1 (1%) 0 (0%) 14 (13.7%) 0 (0%) 8 (7.8%) 1 (1%) 9 (8.8%) 1 (1%) 19 (9.2%) 21 (10.15%) 21 (10.15%) 33 (15.9%) 6 (2.9%) 2 (1.0%) 1 (0.5%) 29 (14.0%) 2 (1.0%) 29 (14.0%) 7 (3.4%) 33 (15.9%) 4 (1.9%) 11 (12.8%) 24 (27.9%); OR = 3.43d 22 (25.6%); OR = 3.04d 8 (9.3%) 3 (3.5%) 0 (0%) 2 (2.3%) 6 (7%) 0 (0%) 4 (4.6%) 0 (0%) 4 (4.6%) 2 (2.3%) 21 (8.6%) 31 (12.7%) 33 (13.5%) 33 (13.5%) 12 (4.9%) 4 (1.6%) 4 (1.6%) 35 (14.4%) 2 (0.8%) 22 (9%) 16 (6.6%) 25 (10.3%) 6 (2.5%) 24 (10.8%) 22 (9.9%) 36 (16.2%) 34 (15.3%) 10 (4.5%) 3 (1.3%) 5 (2.2%) 27 (12.2%) 2 (0.9%) 17 (7.7%) 15 (6.8%) 20 (9%) 7 (3.2%) a P-values are corrected (Bonferroni). For statistical analysis 2 × 2 contingency tables were used that compared HLA DQB1*0302 allele vs. non HLA DQB1*0302 alleles (only subtyped alleles). c Pcorr. < 0.05; d Pcorr. < 0.001; e Pcorr. < 0.0001. CP–, C-peptide-negative; CP+, C-peptide-positive; GADA–, glutamic acid decarboxylase GAD65 negative; GADA+, glutamic acid decarboxylase GAD65 positive; HLA, human leucocyte antigen. b © 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 133–140 138 P. Cejkova et al. Table 6. Combination of –23HphI T and HLA DRB1*03 in LADA and healthy control subjects None of the two markers Only one of the two markers Both of the two markers Control subjects (n = 98) LADA (n = 26) OR (95% CI) P-value Pcorr. 41 (41.8%) 39 (39.8%) 7 (7.1%) 7 (26.9%) 15 (57.7%) 7 (26.9%) 0.5 (0.2–1.45) 2.1 (0.8–5.4) 4.8 (1.3–17.7) NS NS < 0.005 NS NS NS Correction factor used: 15. Combination of –23HphI AA and HLA DRB1*03 in T1DM and healthy control subjects None of the two markers Only one of the two markers Both of the two markers Control subjects (n = 98) T1DM (n = 24) OR (95% CI) P-value Pcorr. 39 (39.8%) 48 (49%) 11 (11.2%) 4 (16.7%) 14 (58%) 6 (25%) 0.3 (0.1–1.0) 1.4 (0.5–4.0) 2.6 (0.8–9.1) < 0.05 NS NS NS NS NS Correction factor used: 15. Combination of –23HphI AA and HLA DRB1*04 in T1DM and healthy control subjects None of the two markers Only one of the two markers Both of the two markers Control subjects (n = 98) T1DM (n = 24) OR (95% CI) P-value Pcorr. 35 (35.7%) 54 (55.1%) 9 (9.2%) 6 (25%) 9 (37.5%) 9 (37.5%) 0.6 (0.2–1.8) 0.5 (0.2–1.3) 5.9 (1.8–19.9) NS NS < 0.001 NS NS < 0.007 Correction factor used: 15. CI, confidence interval; HLA, human leucocyte antigen; LADA, latent autoimmune diabetes in adults; NS, not significant; OR, odds ratio; T1DM, type 1 diabetes mellitus. factor in children. However, with the progression of age the genetic predisposition is switched to HLA DRB1, which has been confirmed by our data. The HLA profile in the tested groups with diabetes revealed only a slight increase in DQB1*02 and DQB1*0302 alleles, and a decrease in DQB1*06 allele in T1DM. The strongest predisposing factors was shown to be DRB1*03 and DRB1*04 alleles. The results obtained from our work are, for the most part, in agreement with data published elsewhere and support the hypothesis that there are different genetic predisposing factors for juvenile and adult-onset DM. Nevertheless, despite the lack of association between the INS –23HphI SNP and diabetes, we did observe a statistical significant difference in allele and genotype frequencies between adult-onset T1DM and other types of diabetes (LADA, T2DM). This finding contributes to a long history of discussion on genetic profiles of lateonset T1DM and LADA. It is believed that genetics may answer the question, whether T1DM in adults with rapid progression and LADA diabetes with a less aggressive progress are two distinct forms of autoimmune diabetes with late onset (Tuomi et al., 1999; Hosszufalusi et al., 2003; Fourlanos et al., 2005; Palmer et al., 2005).To date, results have shown discrepancies (Tuomi et al., 1999; Desai et al., 2006). In this work, the INS –23HphI A allele was detected more often in T1DM patients than in the LADA group, and the difference was statistically significant (Pcorr. = 0.03). The disparity between the two diabetic groups reflects the pancreatic beta cells secretory activity, shown by C-peptide levels, since the presence or absence of C-peptide secretion is the major criteria for discrimination between these two types of autoimmune diabetes. With regard to HLA, DRB1*03 was identified as the main predisposing allele in autoimmune LADA diabetes (Pcorr. < 0.01). DRB1*03 and DRB1*04 alleles were identified as the genetic risk factors in adult-onset T1DM (both Pcorr. < 0.05). The DRB1*04 is in linkage disequilibrium with DQB1*0302 allele, for which we observed higher (but not significant) frequencies. Data presented here thus support the generally accepted theory that there are partially different mechanisms in the development and/or progression of T1DM and LADA diabetes. A quantitative trait study revealed an association between C-peptide secretion and INS-VNTR polymorphism alleles. We observed a positive correlation between the INS-VNTR class III allele (–23HphI T allele) and the fasting C-peptide levels (34.6% in CP+ compared to 13.8% in the CP– group of patients). This finding correlates with the association between the INS-VNTR and the anti-insulin antibody production detected in our study (data not shown). This further suggests a protective effect by the INS-VNTR class III allele (–23HphI T allele) and an increased risk effect by the INS-VNTR class I allele © 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 133–140 HLA DRB1, DQB1 and INS-VNTR in diabetes (–23HphI A allele) on insulin peptide structure and secretion. Our findings fit the accepted scheme of insulin expression in the thymus and pancreas, explaining the process of development of antibodies against insulin. The class I/I genotype was found to be responsible for lower insulin mRNA expression in the fetal thymus, but increased expression in both the fetal and the adult pancreas. The class III/III genotype was associated with higher antigen levels in the thymus, which could more efficiently induce negative selection of insulin-specific T-lymphocytes (Vafiadis et al., 1996, 1997). Other than this, we were unable to find any association between INS-VNTR and the presence of the GAD65 antibody. We also analysed the distribution of the HLA DQB1 and HLA DRB1 alleles with regards to their association with GADA and C-peptide levels. The two risk HLA alleles were detected in the CP– patient group. Both DRB1*03 and DRB1*04 alleles showed similar OR (3.43 and 3.04, respectively), suggesting equal predisposing effects. The DRB1*03 allele was also identified as a risk allele in GADA+ patients with the OR value higher than 4. These results are in agreement with published data (Gambelunghe et al., 2000). Finally, we aimed to investigate the additive effect of the two candidate loci, IDDM1 and IDDM2. Data presented in Table 6 show that the combined effect of both the HLA class II allele (DRB1*04) and the INS –23HphI AA genotype should increase the risk of developing T1DM by almost twofold. Therefore, one can say that both the HLA class II allele DRB1*04 and the INS –23HphI AA genotype are important in conferring genetic susceptibility to T1DM. To conclude, it seems that the IDDM2 quantitative trait locus represented by the INS-VNTR does not associate by itself with the disease, but rather with a specific phenotype. Our results suggest that either INS-VNTR, or INS –23HphI or both are genetic factor(s) that influence pancreatic beta cell secretory activity, which can eventually result in a pathological phenotype. HLA class II genotyping confirmed different immunogenetic aetiopathogenesis of T1DM and LADA disease. It also suggested a diverse mechanism that launches clinical signs of autoimmune diabetes in children and adults. We have shown that the simultaneous effect of IDDM1 and IDDM2 loci predispose to an increase in risk of disease development. 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Human Immunology, 34, 64. © 2008 The Authors Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 133–140 Original paper No. 3 KCNJ11 E23K POLYMORPHISM AND DIABETES MELLITUS WITH ADULT ONSET IN CZECH PATIENTS Cejkova P, Novota P, Cerna M, Kolostova K, Novakova D, Kucera P, Novak J, Andel M, Weber P, Zdarsky E Folia Biol (Praha). 2007 53(5):173-5 96 Original Article KCNJ11 E23K Polymorphism and Diabetes Mellitus with Adult Onset in Czech Patients (diabetes mellitus / Kir6.2 / insulin / C-peptide) P. ČEJKOVÁ1, P. NOVOTA1, M. ČERNÁ1, K. KOLOŠTOVÁ1, D. NOVÁKOVÁ2, P. KUČERA2, J. NOVÁK3, M. ANDĚL3, P. WEBER4, E. ŽDÁRSKÝ5 1 Department of Cell and Molecular Biology, 2Department of Cell and Molecular Immunology, 3Diabetes Center and Department of Internal Medicine, Charles University in Prague, 3rd Faculty of Medicine, Prague, Czech Republic 4 Department of Internal Medicine, Geriatrics and General Medicine, University Hospital Brno, Czech Republic 5 Department of Pharmacology, Charles University in Prague, 3rd Faculty of Medicine, Prague, Czech Republic Abstract. In this work, we studied the association of the E23K polymorphism of the Kir6.2 ATP-sensitive potassium channels in 212 Czech patients with diabetes mellitus who were diagnosed after the age of 35. Patients were classified into T1DM, LADA and T2DM groups based on C-peptide and GADA levels. Carriers of the predisposing Kir6.2 E23K K allele showed no increased risk of either type of diabetes mellitus development. On the other hand, we found a correlation between E23K SNP of the KCNJ11 gene and C-peptide levels, which may be considered a measure of pancreatic β-cell activity, although this correlation was not statistically significant. In conclusion, we failed to confirm the Kir6.2 E23K as a genetic marker for T1DM, LADA and T2DM in the Central Bohemian population of the Czech Republic. Introduction Diabetes mellitus (DM) with manifestation after the age of 35 is a very heterogeneous disease in which both Received January 31, 2007. Accepted June 22, 2007. This study was supported by the Research Programme of the Ministry of Education, Youth and Sports of the Czech Republic MSM 0021620814. genetic and environmental factors contribute to its development. The inwardly rectifying K+channel (Kir) is one of two subunits in the pancreatic β-cell ATP-sensitive potassium channel complex. This complex plays a key role in glucose-stimulated insulin secretion, which makes it a potential candidate for a genetic defect contributing to the development of type 2 DM (T2DM). KCNJ11, a gene encoding Kir6.2 subunit, therefore belongs to well-established candidate genes of T2DM (Porte and Kahn, 2001; Kahn, 2003). Furthermore, functional studies show that E23K SNP is involved in increasing the “open probability” of the Kir6.2 channel, which should lead to diminished insulin secretion (Schwanstecher et al., 2002), and provide supporting evidence for KCNJ11 being a diabetogene. After several initial reports with inconsistent data (Hani et al., 1998; Yamada et al., 2001) the association between the E23K polymorphism and the susceptibility to T2DM was recently confirmed (Gloyn et al., 2003). The aim of this study was to investigate the Kir6.2 E23K gene polymorphism with regard to its possible role in the development and progression of DM in patients with disease onset occuring after 35 years of age, who were collected from the Central Bohemian population of the Czech Republic. Corresponding author: Pavlína Čejková, Department of Cell and Molecular Biology, 3rd Faculty of Medicine, Charles University, Ruska 87, 100 34 Prague 10, Czech Republic. Phone: (+420) 261 102 540; Fax: (+420) 261 102 650; e-mail: pavlina.cejkova@lf3. cuni.cz Material and Methods Abbreviations: CP – C-peptide, DM – diabetes mellitus, GADA – glutamic acid decarboxylase antibodies, LADA – latent autoimmune diabetes in adults, PCR – polymerase chain reaction, RFLP – restriction fragment length polymorphism, SNP – single nucleotide polymorphism, T1DM – type 1 diabetes mellitus, T2DM – type 2 diabetes mellitus. Two hundred and twelve diabetic patients (117 women and 95 men) from Central Bohemia were recruited, who were at least 35 years old at the time of DM diagnosis. The diagnosis of diabetes was established according to the current WHO criteria including the pres- Folia Biologica (Praha) 53, 173-175 (2007) Subjects 174 Vol. 53 P. Čejková et al. Table 1. Clinical features of diabetic patients genotyped for Kir6.2 gene E23K polymorphism BMI [kg/m2] Age at onset [years] Duration of diabetes [years] Mean C-peptide at onset [pmol/L]¹ Mean GADA [ng/mL]² T1DM N = 16 LADA N = 24 T2DM N = 172 28 (22.4-36.7) 43 (36-55) 16.7 (3-32) 66.2 (4-197.0) 223.8 (2.76-3000) 32.5 (26.6-45.5) 54 (35-71) 14 (4-29) 677 (277.8-1752.3) 375.5 (50.6-2800) 31.2 (18.4-50.1) 54 (35-81) 15.1 (1-65) 756.2 (202-3082) 7.69 (0.29-49.9) All data shown as mean with min and max value; ¹C-peptide cut-off level: 200 pmol/l; ²GADA cut-off level: 50 ng/ml ence of antibodies against glutamic acid decarboxylase GAD65 (GADA) and serum C-peptide levels (CP) (Report, 2003). Patients were divided into three groups: 1) late onset T1DM which required insulin therapy within 6 months after diagnosis: CP-, GADA-/+; 2) latent autoimmune diabetes in adults (LADA): CP+, GADA+ and defined as having a minimum 6-month long period between diagnosis and initiation of insulin therapy; 3) T2DM: CP+, GADA- (Table 1). The control group consisted of 113 healthy individuals randomly selected from volunteers at the Blood Donors Center. Genomic DNA from peripheral blood samples was extracted by QiaAmp DNA Mini Kit spin columns (QIAGEN GmbH, Hilden, Germany). original primers used in the Kir6.2 assays are available from the authors on request. PCR-RFLP We did not detect any statistically significant difference (P = 0.05) between T2DM patients and the control group. Similar results were obtained for both T1DM and LADA patient groups (Table 2a): the frequency of the K allele was 46.9% (Pcorr = NS) in T1DM, 29.2% (Pcorr = NS) in LADA and 36.9% (Pcorr = NS) in T2DM patients as compared to 36.7% in the control group. Likewise, the genotype distribution did not differ between the three groups and was consistent with Hardy-Weinberg equilibrium. Table 2b presents comparison of allele and genotype frequencies of the KCNJ11 E23K polymorphism in all diabetic patients classified based on C-peptide levels (cut-off level 200 pmol/l). The K allele was increased in CP- compared to CP+ diabetic patients (46.9% vs. 36%, respectively), and similarly, the KK homozygous genotype was found more often in CP- (25%) than CP+ Genotyping was performed using the PCR-based restriction fragment length polymorphism (RFLP) method. Originally designed oligonucleotides ATPB and ATPC amplified product of the size of 169 bp under the following PCR conditions: initial denaturation at 94°C for 3 min, then 40 times denaturation at 94°C for 15 s, annealing at 56°C for 15 s and extension at 72°C for 15 s. To determine the presence of the E (glutamic acid) or K (lysine) allele, a HinfI digestion was performed at 37°C for 1 h followed by heat inactivation. Within the reverse primer ATPC, an artificial HinfI site was introduced and the resulting fragments were: undigested 169 bp (K/K homozygote), digested 145 + 24 bp (E/E homozygote). The digested and undigested products were visualized using ethidium bromide staining after electrophoresis on 4% agarose gel. The sequences of the Statistical evaluation Epi Info software (Version 3.3, October 2004, Atlanta, Georgia) was used for statistical analysis. Statistical differences in allele or genotype distributions were analysed by either the χ2 or Fisher’s exact probability test. Significance was defined using a Bonferroni corrected P value lower than 0.05. The strength of the observed associations was estimated by calculating the odds ratios (OR). Results Table 2a. Frequency of alleles and genotypes of Kir6.2 E23K polymorphism in control subjects and patients with T1DM, LADA and T2DM diagnosed after the of age 35 Genes Kir6.2 E23K (E/K) E K EE EK KK Alleles and genotypes Control subjects T1DM P LADA P T2DM P 143 (63.3%) 83 (36.7%) 48 (42.5%) 47 (41.6%) 18 (15.9%) NS NS NS NS NS 34 (70.8%) 14 (29.2%) 12 (50%) 10 (41.7%) 2 (8.3%) NS NS NS NS NS 217 (63.1%) 127 (36.9%) 66 (38.4%) 85 (49.4%) 21 (12.2%) NS NS NS NS NS 17 (53.1%) 15 (46.9%) 5 (31.2%) 7 (43.8%) 4 (25%) Kir6.2 E23K Polymorphism and Diabetes Mellitus in Adults Vol. 53 Table 2b. Frequency of alleles and genotypes of Kir6.2 E23K polymorphism between C-peptide-positive and C-peptide-negative diabetic patients Locus Kir6.2 E23K (E/K) E K EE EK KK Alleles and genotypes CP- (N = 16) CP+ (N = 196) P 17 (53.1%) 15 (46.9%) 5 (31.2%) 7 (43.8%) 4 (25%) NS NS NS NS NS 251 (64%) 141 (36%) 78 (39.8%) 95 (48.5%) 23 (11.7%) (11.7%) patients. Nevertheless, these differences were not statistically significant. Discussion Despite reports published elsewhere (Willer et al., 2007), with regard to the allele and genotype frequencies of the Kir6.2 E23K polymorphism, we cannot confirm recently published data (Love-Gregory et al., 2003; Florez et al., 2004; Willer et al., 2007) and the association between the genomic variations of Kir6.2 E23K SNP and the development of common T2DM. Although the E23K variant in KCNJ11 showed no association with diabetes in Czech (for the K allele, OR 1.01 [95% CI 0.71–1.43], P = NS), 95% CI around the OR overlaps in meta-analysis of European populations (Gloyn et al., 2003; van Dam et al., 2005), suggesting that our results are not inconsistent with the previous studies. Considering the physiological function of the ATPdependent K channel of pancreatic β cells, we tested to see whether there was a correlation between C-peptide levels and genotypes/alleles of the KCNJ11 E23K polymorphism. As shown in Table 2b, the K allele and KK genotype were observed more frequently in patients with low C-peptide levels. Although this finding did not reach statistical significance, it suggests an association of the K allele of E23K Kir6.2 polymorphism with decreased pancreatic β-cell secretory activity. In conclusion, the presented study did not confirm that the Kir6.2 E23K SNP is a genetic marker of diabetes mellitus in Czech diabetic patients and rather proposes its direct impact on the secretory activity of pancreatic β cells. Aknowledgements The authors would like to thank Mr. Thomas Secrest for a professional language and style correction. References Florez, J. C., Burtt, N., de Bakker, P. I., Almgren, P., Tuomi, T., Holmkvist, J., Gaudet, D., Hudson, T. J., Schaffner, S. F., 175 Daly, M. J., Hirschhorn, J. N., Groop, L., Altshuler, D. (2004) Haplotype structure and genotype-phenotype correlations of the sulfonylurea receptor and the islet ATP-sensitive potassium channel gene region. Diabetes 53, 1360-1368. Gloyn, A. L., Weedon, M. N., Owen, K. R., Turner, M. J., Knight, B. A., Hitman, G., Walker, M., Levy, J. C., Sampson, M., Halford, S., McCarthy, M. I., Hattersley, A. T., Frayling, T. M. (2003) Large-scale association studies of variants in genes encoding the pancreatic β-cell KATP channel subunits Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 52, 568-572. Hani, E. H., Boutin, P., Durand, E., Inoue, H., Permutt, M. A., Velho, G., Froguel, P. (1998) Missense mutations in the pancreatic islet β-cell inwardly rectifying K+ channel gene (KIR6.2/BIR): a meta-analysis suggests a role in the polygenic basis of type II diabetes mellitus in Caucasians. Diabetologia 41, 1511-1515. Kahn, S. E. (2003) The relative contributions of insulin resistance and β-cell dysfunction to the pathophysiology of type 2 diabetes. Diabetologia 46, 3-19. Love-Gregory, L., Wasson, J., Lin, J., Skolnick, G., Suarez, B., Permutt, M. A. (2003) E23K Single nucleotide polymorphism in the islet ATP-sensitive potassium channel gene (Kir6.2) contributes as much to the risk of type II diabetes in caucasians as the PPARγ Pro12Ala variant. Diabetologia 46, 136-137. Porte, D., Kahn, S. E. (2001) β-cell dysfunction and failure in type 2 diabetes - potential mechanisms. Diabetes 50, S160S163. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (2003) Diabetes Care 26, S5-S20. Schwanstecher, C., Meyer, U., Schwanstecher, M. (2002) K(IR)6.2 polymorphism predisposes to type 2 diabetes by inducing overactivity of pancreatic β-cell ATP-sensitive K(+) channels. Diabetes 51, 875-879. van Dam, R. M., Hoebee, B., Seidell, J. C., Schaap, M. M., de Bruin, T. W. A., Feskens, E. J. M. (2005) Common variants in the ATP-sensitive K+ channel genes KCNJ11 (Kir6.2) and ABCC8 (SUR1) in relation to glucose intolerance: populationbased studies and meta-analyses. Diabet. Med. 22, 590-598. Willer, C. J., Bonnycastle, L. L., Conneely, K. N., Duren, W. L., Jackson, A. U., Scott, L. J., Narisu, N., Chines, P. S., Skol, A., Stringham, H. M., Petrie, J., Erdos, M. R., Shift, A. J., Enloe, S. T., Sprau, A. G., Smith, E., Tong, M., Doheny, K. F., Pugh, E. W., Watanabe, R. M., Buchanan, T. A., Valle, T. T., Bergman, R. N., Tuomilehto, J., Mohlke, K. L. Collins, F. S., Boehnke, M. (2007) Screening of 134 single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes replicates association with 12 SNPs in nine genes. Diabetes 56, 256-264 Yamada, Y., Kuroe, A., Li, Q., Someya, Y., Kubota, A., Ihara, Y., Tsuura, Y., Seino, Y. (2001) Genomic variation in pancreatic ion channel genes in Japanese type 2 diabetic patients. Diabetes Metab. Res. Rev. 17, 213-216. Original paper No. 4 AUTOIMMUNE DIABETES MELLITUS WITH ADULT ONSET AND TYPE 1 DIABETES MELLITUS IN CHILDREN HAVE DIFFERENT GENETIC PREDISPOSITIONS Cerna M, Kolostova K, Novota P, Romzova M, Cejkova P, Pinterova D, Pruhova S, Treslova L, Andel M Ann. N. Y.Acad. Sci 2007 Sep;1110:140-50 100 Autoimmune Diabetes Mellitus with Adult Onset and Type 1 Diabetes Mellitus in Children Have Different Genetic Predispositions MARIE CERNA,a KATARINA KOLOSTOVA,a PETER NOVOTA,a MARIANNA ROMZOVA,a PAVLINA CEJKOVA,a DANIELA PINTEROVA,a STEPANKA PRUHOVA,c LUDMILA TRESLOVA,b AND MICHAL ANDELb a Department of Cellular and Molecular Biology, Centrum for Diabetes, Metabolism and Nutrition Research of Third Faculty of Medicine, Charles University in Prague, Ruska 87, Praha, Czech Republic b Second Internal Clinics, Centrum for Diabetes, Metabolism and Nutrition Research of Third Faculty of Medicine, Charles University in Prague, Ruska 87, Praha, Czech Republic c Department of Pediatrics, Centrum for Diabetes, Metabolism and Nutrition Research of Third Faculty of Medicine, Charles University in Prague, Ruska 87, Praha, Czech Republic ABSTRACT: Type 1 diabetes with manifestation after 35 years of age is defined by CP <200 pmol/L and institution of insulin therapy within 6 months after diagnosis. Latent autoimmune diabetes mellitus in adults (LADA) manifesting after 35 years of age is defined by minimum 6 months after diagnosis without insulin therapy and C peptide (CP) >200 pmol/L and antiGAD > 50 ng/mL. We aimed to find a possible genetic discrimination among different types of autoimmune diabetes. To accomplish this goal, we analyzed DNA samples from 31 LADA patients, 75 patients with adult-onset type 1 diabetes mellitus, 188 type 1 diabetic children, and 153 healthy adult individuals. We studied five genetic loci on chromosomes 6, 11, 4, and 14: HLA DRB1 and DQB1 alleles, major histocompatibility complex (MHC) class I–related gene-A (MIC-A) microsatellite polymorphism, interleukin (IL)-18 single nucleotide polymorphism, the microsatellite polymorphism of nuclear factor kappa B gene (NF-B1), and the single nucleotide polymorphism of a gene for its inhibitor (NF-BIA). HLA-DR3 was detected as the predisposition allele for LADA (OR = 4.94, P < 0.0001). Further we found a statistically significant increase of NF-BIA AA genotype (OR = 2.68, ∗P < 0.01). ∗ On the other hand, DRB1 04, which is linked with DQB1 0302, was Address for correspondence: Marie Cerna, Department of Cellular and Molecular Biology, Third Faculty of Medicine, Charles University in Prague, Ruska 87, 100-00, Praha 10, Czech Republic. Voice: +420-267-102-657; fax: +420-267-102-650. [email protected] C 2007 New York Academy of Sciences. Ann. N.Y. Acad. Sci. 1110: 140–150 (2007). doi: 10.1196/annals.1423.016 140 CERNA et al. 141 observed as a risk factor in patients with type 1 diabetes mellitus (T1DM) onset after 35 years of age (OR = 10.47, P < 0.0001 and OR = 9.49, P < 0.0001, respectively). There was also an association with MIC-A5.1 (OR = 2.14, P < 0.01). Statistically significant difference was found in the distribution of IL-18 promoter –607 (C/A) polymorphism between LADA and T1DM in adults (P < 0.01). We conclude that all subgroups of autoimmune diabetes have partly different immunogenetic predisposition. KEYWORDS: T1DM; LADA; IDDM; HLA; autoantibodies INTRODUCTION Autoimmune diabetes mellitus, also called type 1 diabetes mellitus (T1DM), is usually caused by autoimmune destruction of islet -cells in the pancreas.1 The destruction of islet -cells causes insulin deficiency and thereby a dysregulation in anabolism and catabolism. T1DM has features characteristic of most of the autoimmune diseases: (1) It is associated with specific human leukocyte antigens (HLA) class II haplotypes (particularly HLA DRB1∗ 04–DQB1∗ 0302, but also HLA DRB1∗ 03–DQB1∗ 0201). (2) Autoantibodies against autoantigens of -cells circulating in the body are found (GAD65, IA-2, ICA, IAA). (3) Mononuclear infiltration of the pancreatic islet can be detected histologically. There are indications that -cell damage may be induced at any age and that the timing of the clinical presentation is highly variable, with the youngest patients diagnosed in infancy and the oldest at a later age.2 Epidemiological data suggest that in 30–50% of cases clinical signs of T1DM may develop after the age of 20 years. Such patients have many features of classic T1DM, but previous studies indicate that subjects presenting with the disease in adulthood are characterized by a longer symptomatic period before diagnosis, better preserved -cell function, and a reduced frequency of insulin autoantibodies (IAAs). Genetic predisposition to T1DM is less marked (decreased prevalence of HLA class II susceptibility alleles), and there is probably also a possible contribution of protective genes toward -cell destruction.3 Some studies have suggested that later disease onset is characterized by a higher frequency of DRB1∗ 03.4 Other studies found DRB1∗ 04 associated with later onset of T1DM.5 It was observed that the prevalence of the HLA- DQB1∗ 0201/0302 (DR3/DR4) genotype reflects the age at onset of diabetes, and the 0302 genotype is associated with insulin dependency. Aims of the Study The aims of this study are to analyze the genetic predisposition for autoimmune diabetes mellitus with onset after 35 years of age to discern (a) whether it is a heterogeneous group of diseases, in which we can further identify subtypes 142 ANNALS OF THE NEW YORK ACADEMY OF SCIENCES of diabetes, or (b) whether diabetes with adult onset if it has a different immunogenetic predisposition compared to T1DM with childhood onset. PATIENTS Two hundred eighty-one patients suffering from diabetes mellitus with manifestation after 35 years of age were divided into three groups: 1. T1DM: CP ≤200 pmol/L. insulin <6 m. (N = 64) 2. Latent autoimmune diabetes in adults (LADA): CP ≥200 pmol/L. GADA ≥ 50 mg/mL insulin >6 m. (N = 31) 3. T2DM: CP ≥200 pmol/L, GADA ≤ 50 ng/mL (N = 131) LADA is considered to be a subtype of T1DM. Other tested groups were 188 children withT1DM and 159 healthy adult individuals. METHODS We have analyzed five genetic loci localized on chromosomes 4, 6, 11, and 14 in patients with childhood- and adult-onset autoimmune diabetes compared to healthy control subjects:6–11 Genes • • • • • Alleles HLA class II MIC-A (MHC class I-related gene-A) NF-B1 (nuclear factor- B) NF-BIA (inhibitor I-B) IL-18 (interleukin-18) DRB1 and DQB1 A microsatellite polymorphism GCT in exon 5 CA repeating polymorphism in regulation region A single nucleotide polymorphism A/G in 3 UTR A promoter polymorphism –137 G/C, –607 C/A PREDISPOSITION GENES AND THEIR PRODUCTS HLA Class II Alleles An association between HLA and autoimmune diabetes mellitus has been recognized for more than 20 years. Many studies have verified that DQB1∗ 0302 is a strong susceptibility gene and that the heterozygous combination of DQA1∗ 0301-DQB1∗ 0302 on the HLA-DR4 haplotype and DQA1∗ 0501DQB1∗ 0201 on the HLA-DR3 haplotype results in a synergistically increased risk of T1DM. DQA1∗ 0301-DQB1∗ 0302 is the most prevalent risk-conferring haplotype in Caucasians, detected in 74% of T1DM patients followed by CERNA et al. 143 DQA1∗ 0501-DQB1∗ 0201, detected in 52% of Caucasian patients.12 Also, there is agreement that the DQB1∗ 0602 allele, linked with DR2, is a strong protective gene.13 The protective effect of DQB1∗ 0602 is dominant to the susceptibility effects of DQB1∗ 0302-DQA1∗ 0301 and DQB1∗ 0201-DQA1∗ 0501.14 A single copy of this DQB1∗ 0602 allele is adequate to confer significant negative association. HLA-DQ genes are of primary importance, but HLADR genes modify the risk conferred by HLA-DQ.15 The risk associated with an HLA genotype is defined by the particular combination of susceptible and protective alleles. The frequencies of predisposition alleles, DQB1∗ 0302 and DQB1∗ 0201, are usually increased, whereas frequency of the protective DQB1∗ 0602 allele is usually decreased.16,17 Major Histocompatibility Complex (MHC) Class I Chain-Related Gene A (MIC-A) A number of observations indicate that class II genes cannot explain all of the MHC associations with T1DM. A role for MHC complex genes other than class II genes has been suggested. Several studies have supported a role for both MHC HLA class III and class I genes in T1DM predisposition. The most studied class III genes are tumor necrosis factor and lymphotoxin (TNF- and TNF-). The strongest evidence for susceptibility genes in the class I region comes from recent systematic assessment of microsatellite markers spanning this region. Among them, one is the gene MIC-A, which is located within the MHC class I region of chromosome 6. It contains long open reading frames encoding three distinct extracellular domains (1, 2, and 3), a transmembrane segment, and a cytoplasmatic tail. It has been reported that MIC-A may be recognized by a subpopulation of intestinal T cells,18 and may play a role in the activation of a subpopulation of NK cells that express the NKG2D receptor.19 Sequence analysis of the exon 5 encoding the transmembrane segment showed a trinucleotide repeat (GCT) microsatellite polymorphism within this region.20 So far, six alleles of the exon 5 of the MIC-A gene, which differ in number of (GCT) repetitions at position 296, have been identified. These alleles contain 4, 5, 6, 9, 10 repetitions of GCT, and five repetitions of GCT with an additional nucleotide insertion (GGCT). The MIC-A alleles have been accordingly named A4, A5, A6, A9, A10, and A5.1, respectively.21 Because of the proximity of the MIC-A locus to HLA-B and HLA-C, MIC-A polymorphisms have been investigated for associations with a variety of HLA class I-associated diseases. Some of these studies show a positive association between MIC-A and the disease in question, but this may be due to its linkage disequilibrium with HLA-B. However, most disease association studies have only concentrated on the MIC-A transmembrane region, focusing on the six variants 144 ANNALS OF THE NEW YORK ACADEMY OF SCIENCES so far identified. Recent studies of MIC-A transmembrane polymorphism focused on the investigation of its relationship to autoimmune diabetes mellitus showed an association of the A5 allele with the susceptibility to young-onset T1DM.22,23 Conversely, this allele does not confer increased risk for adultonset T1DM.24 NF- B and Its Inhibitor The gene encoding NF-B, NF-B1, on chromosome 4 with a microsatellite polymorphism (CA) inregulation region (18 alleles), and the gene encoding inhibitor of NF-B, NF-BIA, on chromosome 14 with single nucleotide polymorphism (A/G) in 3 UTR, are other candidates of genetic predisposition to diabetes mellitus for the reason of their relationship to inflammatory processes.25,26 NF-B is a transcription factor, which is implicated in the regulation of many genes that encode mediators of immune response, embryo and cell lineage development, cell apoptosis, inflammation, cell cycle progression, oncogenesis, viral replication, and various autoimmune diseases. The activation of NF-B is thought to be part of a stress response as it is activated by a variety of stimuli, including reactive oxygen species (ROS) and advanced glycation end products (AGEs), which are toxic products of nonenzymatic glycation due to long-term hyperglycemia and oxidative stress. At the cellular level NF-B is activated through phosphorylation of inhibitor of NF-B (IB), what triggers subsequent translocation of NF-B molecules into the nucleus, where they are bound with a consensus sequence (5 GGGACTTTCC-3 ) of various genes, activating their transcription. Interleukin (IL)-18 On chromosome 11 is located the gene encoding IL-18, with two single nucleotide polymorphisms inpromoter region at position –607 and –137. The cytokine IL-18, the Th1 cytokine, and a member of the IL-1 superfamily, elicits several biological activities that initiate and promote host defense and inflammation following infection or injury.27,28 It is a pleiotropic cytokine predominantly secreted by activated monocytes/macrophages involved in the regulation of innate and acquired immune response, playing a key role in autoimmune, inflammatory, and infectious diseases. IL-18 acts as a proinflammatory factor and, in synergy with IL-12, promotes development of Th1 lymphocyte response by induction of interferon- (IFN- ) production, modulates activity of natural killer cells, increases TNF- and IL-1 production by macrophages, upregulates the expression of adhesion molecules, and induces nitric oxide production in the area of chronic inflammation.29 It was reported by Nicoletti et al.30 that IL-18 serum levels are increased in the subclinical stage of T1DM in first-degree relatives of T1DM patients. The importance CERNA et al. 145 of IL-18 for the occurrence of T1DM is underscored by the observation that the murine IL-18 gene maps to an interval on chromosome 9 in the vicinity of the diabetes susceptibility gene from the nonobese diabetic (NOD) mouse, idd2.31 Several polymorphisms in the promoter region of human IL-18 gene, located at chromosome 11q22.2–q22.3, have been identified.32 Kretowski et al.33 reported the role of IL-18 gene polymorphism at positions –607 and –137 in the predisposition to T1DM in Polish population. RESULTS The most significant risk factor is on chromosome 6, human leukocyte antigen class II alleles, HLA DRB1 and DQB1. Odds ratio for single predisposition HLA alleles has been significantly different in different groups of patients. For DRB1∗ 03 allele, it has been the highest in LADA patients (OR = 4.94 with CI 95% between 1.83 and 13.49), for DRB1∗ 04 and DQB1∗ 0302 alleles, it has been the highest in diabetic children (OR = 22.87 with CI 95% between 10.89 and 48.87, and OR = 46.48 with CI 95% between 16.15 and 140.23, respectively). In the case of T1DM with adult onset, predisposition alleles have been similar as in children, but odds ratio has been lower and proved a stronger influence of DR alleles than DQ alleles (OR = 10.47 with CI 95% between 4.53 and 24.65, and OR = 9.49 with CI 95% between 3.91 and 23.59, respectively). Analysis of gene expression of HLA-DRB1∗ 04 in antigen-presenting cells of peripheral blood has described significantly increased quantity of mRNA of this allele in T1DM with adult onset in comparison to T1DM in children and to control groups of adults and children (FIG. 1). One HLA hypothesis points to a possible role of HLA-DR molecules as a protective factor, which in comparison to HLA-DQ molecules suppresses autoimmune reactions. Higher quantity of DR molecules in adult patients may suggest breaking the autoimmune process. On chromosome 6 is also located the MIC-A gene. Its exon 5, coding transmembrane region, has been tested for a microsatellite polymorphism (GCT) with six alleles. The results in this study suggest that MIC-A5.1 allele is associated with genetic susceptibility to adult onset of T1DM (corrected P = 0.009; OR = 2.14 with CI 95% 1.29–3.55), whereas it is increased, but not significantly in LADA. A different allele, MIC-A5, is associated with T1DM in children. Since NF-B is a transcription factor which is implicated in the regulation of many genes involved in inflammatory processes, a microsatellite polymorphism (CA) in the regulation region of NF-B1 gene and a single nucleotide polymorphism (A/G) in 3 UTR of the NF-BIA gene are other candidates of genetic predisposition to autoimmune diabetes mellitus. A statistically significant difference in allele frequencies of the NF-B1 gene has been observed only in T1DM with adult onset. The frequency of A7 allele (size 132 bp, 20 146 ANNALS OF THE NEW YORK ACADEMY OF SCIENCES 4.5 Relative RNA amount 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 T1DM Children T1DM Adults Controls FIGURE 1. Relative quantification data from quantitative analysis of HLA-DRB1∗ 04 mRNA levels in the tested groups of patients and controls.The expression of HLA-DRB1∗ 04 was found significantly higher in T1DM adult patients. The comparison was done with two specific control groups (adult and child controls with HLA-DRB1∗ 04 allele). The relative RNA amount for tested groups of type 1 diabetes (T1DM) with adult onset and in childhood was significantly different (P-value 0.034; comparison by Kruskall–Wallis test and Dunn’s multiple comparison test). The difference between patient groups and control groups was not significant by this type of testing. repeats) has been significantly increased in comparison to that of a control healthy group (P < 0.01; OR = 10.0 with CI 95% 1.25–215.6). There was no difference in A and G allele frequencies of the NF-BIA gene between the control group and patients; the difference has been observed in distribution of genotypes. There is evidence that the heterozygous genotype, in comparison to a control healthy group, is protective for autoimmune diabetes with onset in adulthood: 55.6% in controls, 45.2% in LADA (nonsignificant decrease), and 35.9% in T1DM with adult onset (P < 0.01; OR = 0.4 with CI 95% 0.24–0.86). A statistically significant increase of the AA genotype frequency of the NF-BIA gene has been detected in autoimmune diabetes type LADA (32.3%, P < 0.01; OR = 2.7 with CI 95% 1.06–6.75) and in type 2 diabetes (42.3%, P < 0.001; OR = 2.6 with CI 95% 1.52–4.41) in comparison to a control healthy group (16.9%). In T1DM with adult onset, the genotype frequency has been also increased, but it has not reached a statistical significance. On the other hand, the values for diabetes in children are not different from control ones. The fact that polymorphism of the NF-B system CERNA et al. 147 TABLE 1. Overview of predisposition genes in autoimmune diabetes with different onset Gene LADA after 35 years of age HLA DRB1∗ 03 MIC-A NF-BIA NF-B1 IL-18 (OR = 5) Allele A5.1a Homozygote AA − Homozygote −607 CC ↑ (increased frequency) a Observation T1DM after 35 years of age DRB1∗ 04 DQB1∗ 0302 (OR = 10) Allele A5.1 Homozygote AAa Allele A7 Homozygote −607 CC ↓ (decreased frequency) T1DM in children DQB1∗ 0302 (OR = 46) Allele A5 − − Allele −137 C was not statistically significant. is associated not only with autoimmunity, but also with metabolic disease, documents its central role in cell signaling. Promoter polymorphism of IL-18 gene is not associated with any group of diabetes, but a statistical significant difference has been found between autoimmune diabetes type LADA and adult-onset T1DM. CONCLUSIONS We can summarize our observations in these findings: The main and unique HLA predisposition allele in autoimmune diabetes type LADA has been detected as HLA-DR3 (OR = 5.0, P < 0.0001). Other HLA alleles have not been found to have a significant association with the disease. Furthermore, the statistically significant increase of the AA genotype frequency of the NF-BIA gene (OR = 2.7, P < 0.01) has been present in comparison to a control healthy group. Contrary to this, the genetic risk factor in adult-onset T1DM has been detected as the DRB1∗ 04 allele (OR = 10.0, P < 0.0001), which is in linkage disequilibrium with other predisposition allele, DQB1∗ 0302 (OR = 10.0, P < 0.0001). This group is also associated with microsatellite polymorphisms of MIC-A and NF-B1 genes (MIC-A5.1 allele: OR = 2.14, P < 0.01; NF-B1-A7 allele: OR = 10.0, P < 0.01). The statistically significant difference has been observed in the distribution of the promoter polymorphism at position –607 (C/A) of IL-18 gene between both groups of patients (P < 0.01), which supports our conclusions about partly different etiopathogenesis of these two groups of adult patients (TABLE 1). Taken together, these observations suggest a generally accepted conclusion that the presence of predisposing genes in autoimmune diabetes decreases with age, probably because of the increasing influence of environmental factors. Autoimmune diabetes with manifestation in adults (after 35 years ) may have 148 ANNALS OF THE NEW YORK ACADEMY OF SCIENCES a partly different immunogenetic etiopathogenesis than autoimmune diabetes with manifestation in childhood. Allele polymorphism of the NF-B system presents a genetic risk factor only in autoimmune diabetes with adult onset, but not in diabetes of childhood. Furthermore, we can hypothesize that T1DM in children or adults might have different predisposition genes than those of LADA. Compared to fast-progressing adult-onset T1DM, slowly progressing adult-onset T1DM (LADA) can involve protective genes leading to slow, progressive -cell destruction and insulin deficiency. The different mechanisms reflect different HLA association in these diseases. Allele polymorphism of the HLA system is associated with autoimmune diabetes in both categories, but for each group of patients the specific association has been described. The most significant association is with DQB1 alleles in T1DM, but with DRB1 alleles in LADA. ACKNOWLEDGMENTS This survey was funded by the Research Design of the Ministry of Education and Youth of the Czech Republic (Identification code: MSM 0021620814: Prevention, diagnostics and therapy of diabetes mellitus, metabolic and endocrine damage of organism). REFERENCES 1. AMERICAN DIABETES ASSOCIATION. 2006. Diagnosis and classification of diabetes mellitus. Diabetes Care 29 Suppl 1: S43–S48. 2. SABBAH, E., K. SAVOLA, T. EBELING, et al. 2000. Genetic, autoimmune, and clinical characteristics of childhood- and adult-onset type 1 diabetes. Diabetes Care 23: 1326–1332. 3. POZZILLI, P. & U. DI MARIO. 2001. 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Active stage of autoimmune diabetes is associated with the expression of a novel cytokine, IGIF, which is located near Idd2. J. Clin. Invest. 99: 469–474. 32. GIEDRAITIS, V., B. HE, W.X. HUANG, et al. 2001. Cloning and mutation analysis of the human IL-18 promoter: a possible role of polymorphisms in expression regulation. J. Neuroimmunol. 112: 146–152. 33. KRETOWSKI, A., K. MIRONCZUK, A. KARPINSKA, et al. 2002. Interleukin-18 promoter polymorphisms in type 1 diabetes. Diabetes 51: 3347–3349. Original paper No. 5 HLA IN CZECH ADULT PATIENTS WITH AUTOIMMUNE DIABETES MELLITUS: COMPARISON WITH CZECH CHILDREN WITH TYPE 1 DIABETES AND PATIENTS WITH TYPE 2 DIABETES Cerna M, Novota P, Kolostova K, Cejkova P, Zdarsky E, Novakova D, Kucera P, Novak J, Andel M Eur J Immunogenet 2003 30:401-407 111 HLA in Czech adult patients with autoimmune diabetes mellitus: comparison with Czech children with type 1 diabetes and patients with type 2 diabetes Blackwell Publishing Ltd. M. Cerna,* P. Novota,* K. Kolostova,* P. Cejkova,* E. Zdarsky,* D. Novakova,† P. Kucera,† J. Novak‡ & M. Andel‡ Summary Type 1 diabetes results from an autoimmune insulitis, associated with HLA class II alleles. The evidence about HLA allele association is not clear in patients diagnosed after 35 years of age. In this study we have analyzed HLA alleles of DQB1 and DRB1 genes by sequence specific primer (SSP)-PCR technique in adult patients with disease onset after 35 years of age. Two hundred and eighty-one patients were divided into three groups according to the insulin therapy, the level of C peptide (CP), and GAD antibodies (anti-GAD). Group 1 (type 1 diabetes in adults) was characterized by CP less than 200 pmol / L and antiGAD more or less than 50 ng /mL (n = 80). All of them had insulin therapy within 6 months after diagnosis. Group 2 latent autoimmune diabetes mellitus in adults (LADA) was defined by a minimum 6-month-long phase after diagnosis without insulin therapy, and was characterized by CP more than 200 pmol / L and anti-GAD more than 50 ng/mL (n = 70). Group 3 (type 2 diabetes) was characterized by CP more than 200 pmol / L and anti-GAD less than 50 ng/mL (n = 131). None ever had insulin therapy. In group 1, there was increased frequency of DRB1*04 (45.0% vs. controls 14.1%, OR = 5.0, P < 0.0005) and DQB1*0302 alleles (43.3% vs. controls 11.1%, OR = 6.1, P < 0.00005). There was increased frequency of DRB1*03 and DQB1*0201, and decreased frequency of DQB1*0602 (3.3% vs. controls 20.2%), but it was not significant. In group 2, there was a significantly increased frequency of DRB1*03 only (50.0% vs. controls 21.2%, OR = 3.7, P < 0.05). Compared with children with type 1 diabetes and adults with type 2 diabetes (group 3), we * Department of Cell and Molecular Biology, † Department of Cell and Molecular Immunology, ‡ Diabetes Center and Department of Medicine II, Third Medical Faculty of Charles University, Prague, Czech Republic Received 22 October 2001; revised 21 July 2003; accepted 15 September 2003 Correspondence: Marie Cerna, Department of Cell and Molecular Biology, Third Medical Faculty of Charles University, Prague, Ruská 87, 100 34 Praha 10, Czech Republic. Tel.: + 420 2 67 102667; Fax: + 420 2 67 102650; E-mail: [email protected] conclude that the presence of predisposing DQB1 alleles in adults with type 1 diabetes decreases with the age, probably due to environmental factors. Only the DRB1*03, but not the DQB1 gene, becomes the main predisposing allele in LADA patients. These findings suggest that the presence of HLA-DQB1*0302 identifies patients at high risk of requiring insulin treatment. Type 1 diabetes mellitus (DM) in children or adults may have partly different immunogenetic etiopathogenesis than LADA. Introduction Autoimmune diseases form a complex group of approximately 40 diseases affecting 5–7% of the general population. Type 1 diabetes mellitus (DM) is one of the most frequent and potentially severe with a prevalence of approximately 0.5% (Sinha et al., 1990). Type 1 diabetes mellitus develops as a result of selective autoimmune destruction of pancreatic β-cells, a process called insulitis. The presence of antibodies against isletcells (ICA), insulin (IAA), glutamic acid decarboxylase (GAD-Ab) and tyrosine-phosphatase (IA2-Ab), and infiltration of pancreatic islands by B and T lymphocytes are all evidence of organ specific autoimmunity. The exact cause of insulitis is not completely understood. The etiology is multifactorial and includes both genetic and environmental factors. Many papers have shown that HLA genes, the human main histocompatibility locus on the chromosome 6, are key genetic factors (Thomson et al., 1988). The studies have proved that type 1 diabetes is associated with certain HLA class II alleles, namely DRB1*0301, DRB1*0401, DRB1*0405, DQB1*0201, DQB1*0302, DQA1*0301. Some alleles are also protective, namely DQB1*0602, DQB1*0603, DQB1*0301. The HLA frequencies in Czech population are similar as those of the other Caucasian populations (Cerna et al., 1992; Drabek et al., 1998; Loudova et al., 1999). Serological typing of the HLA system in young patients with type 1 diabetes in the Czech population has also proved HLA predisposition antigens similar to other Caucasians (Dvorakova & Majsky, 1979, Vondra et al., 1996). The association of HLA alleles with type 1 diabetes changes with age of onset. Generally, the younger the © 2003 Blackwell Publishing Ltd, European Journal of Immunogenetics 30, 401–407 401 402 M. Cerna et al. patients the more significant is the HLA association (Tait et al., 1995; Lohmann et al., 1997; Graham et al., 1999). The evidence about HLA allele association is not clear, however. Some studies have suggested that later disease onset is characterized by a higher frequency of DRB1*03 (Lohmann et al., 1997; Zevaco-Mattei et al., 1999; Gambelunghe et al., 2000). Other studies found DRB1*04 associated with later onset of type 1 diabetes mellitus (Tait et al., 1995; Tuomi et al., 1999). Analyses in non-diabetic offspring of probands with latent autoimmune diabetes mellitus in adults (LADA) concluded that LADA is a familial disease involving gene defects leading to a slow progressive β-cells destruction and insulin deficiency (Vauhkonen et al., 2000). The aims of our study were to analyze the frequency distribution of HLA DRB1 and DQB1 alleles in the adult onset diabetes in a Czech population and compare this data with those from controls, diabetic children and the healthy Czech population. Patients and methods Patients We investigated 281 patients (172 women) with diagnosis of diabetes after 35 years of age. Their average age at disease onset was 53 years (range 35–81). The patients came consecutively for consultation to our diabetes centre and collaborating diabetes clinics. All patients had fasting C peptide, GAD and IA2 antibodies measured at the time of investigation. Body mass index (BMI) was calculated from weight and height. The therapy was recorded (Table 1). We divided our patients into three groups: type 1 diabetes in adults, LADA, and type 2 diabetes. Patients with autoimmune diabetes (group 1 and 2) were negative for C peptide or positive for autoantibodies. All of them had insulin therapy. LADA (group 2) was defined by a minimum 6-month-long phase after diagnosis without insulin therapy. Patients with type 2 diabetes (group 3) were positive for C peptide and negative for autoantibodies. None ever had insulin therapy. Group 1, type 1 diabetes in adults, comprised 80 patients (56 women) with an average BMI of 27 (SD = 4.6) and average duration of diabetes 16 years (SD = 14.2). All patients had fasting C peptide secretion less than 200 pmol/L. Anti-GAD more than 50 ng/mL was present in 50% of patients, anti-IA2 more than 0.9 U/mL was present in 15% of patients. Ten per cent of patients were positive for both antibodies, but 45% of patients were without these two antibodies. Group 2, LADA, comprised 70 patients (40 women) with an average BMI of 32 (SD = 4.8) and average duration of diabetes 14 years (SD = 7.0). All patients had fasting C peptide secretion more than 200 pmol/L. Anti-GAD more than 50 ng/mL was present in 100% of patients and anti-IA2 more than 0.9 U/mL was present in 11% of patients. Type 2 diabetic patients (group 3) comprised 131 persons (76 women) with an average BMI of 31 (SD = 5.5) and average duration of diabetes 13 years (SD = 6.6), with fasting C peptide secretion more than 200 pmol / L and anti-GAD less than 50 ng /mL. None ever had insulin therapy. For comparison we used the results of HLA class II analysis of Czech diabetic children with type 1 diabetes mellitus (Cinek et al., 2001). HLA typing performed in children was done by the same technique as in this study (SSP-PCR supplied by Genovision, Oslo, Norway). We have compared the patient data with the data of the healthy Czech population, which was published earlier (Cerna et al., 1992). The controls were genotyped in a previous study by oligonucleotide hybridization. We tested several control samples by the sequence specific primer (SSP)-PCR method used in this study to prove that we get the same results by both techniques. The study was approved by the ethical committee of Third Medical Faculty of Charles University at Prague. All patients gave Features Group 1 (n = 80) Group 2 (n = 70) Group 3 (n = 131) Female Age at disease onset (years)a Duration of DM (years)a BMI (kg/m2)a Insulin therapy (number) Fasting C peptideb (pmol/L)a Anti-GADc (ng/mL)a 61% 43 (36 –56) 16 (4 –27) 27 (22–37) Yes 80/80 – 63 (4–197) +/– 193 (3–3000) 57% 52 (35 –71) 14 (4 –29) 32 (27 –46) Yes 70/70 + 609 (51–2800) + 379 (210 –1753) 58% 53 (35 –81) 13 (1–22) 31 (18 –50) No 0/131 + 772 (1–50) – 8 (202–3370) Table 1. Clinical features in 281 Czech adult patients with diabetes a Average value with ranges in parentheses; + positive value ≥ 200 pmol / L, – negative value < 200 pmol /L; c + positive value ≥ 50 ng /mL, – negative value < 50 ng /mL; Group 1 (type 1 diabetes in adults): C peptide < 200 pmol /L, anti-GAD > or < 50 ng /mL; Group 2 (LADA): C peptide > 200 pmol / L, anti-GAD > 50 ng /m, > 6 months without insulin; Group 3 (type 2 diabetes): C peptide > 200 pmol / L, anti-GAD < 50 ng /mL. b © 2003 Blackwell Publishing Ltd, European Journal of Immunogenetics 30, 401– 407 HLA in Czech patients with diabetes mellitus informed consent to the use of their blood samples in this study. 403 Analysis of HLA DRB1 and DQB1 alleles HLA DRB1 and DQB1 genes were typed using PCR with sequence specific primers (SSP-PCR) (Zetterquist & Olerup, 1992; Olerup et al., 1993) supplied by Genovision. Fasting C peptide We used an immunoradiometric method (Immunotech, Prague, Czech Republic). The serum samples from 49 healthy individuals were analyzed and the results are as follows: average value 486 pmol / L, median 449 pmol/L, standard deviation 170 pmol / L, and minimal-maximal value 206–934 pmol / L. The level less than 200 pmol/L has been taken as decreased C peptide secretion. GAD antibodies The presence of IgG antibodies against GAD has been detected by the ELISA method (Roche Molecular Biochemicals, Mannheim, Germany). The test has 98% of specificity and 69% of sensitivity (Verge et al., 1998). A level less than 32 ng/mL is considered as negative. To increase the precision, in our study, a level less than 50 ng /mL has been taken as normal. A level more than 50 ng /mL has been considered as a reliable sign of autoimmune insulitits. IA2 antibodies The presence of IgG antibodies against IA2 has been detected by the ELISA method (Roche Molecular Biochemicals). A level less than 0.9 U/mL is considered as negative. Statistics Allele, gene, and haplotype frequencies were calculated using standard methods. The significance of the differences in allele frequencies were evaluated by χ2 with Yates’ correction and P-values were calculated. When a hypothesis that had not been tested was analyzed, the P-values were corrected by multiplying by the number of alleles tested. The strength of the observed associations was estimated by calculating odds ratios [relative risk (RR)] using the method by Woolf. When one of the RR values is 0 or close to 0, RR can be calculated by using Woolf’s method with Haldane’s correction. Results Analysis of HLA DRB1 alleles DRB1*04 was significantly increased in group 1 compared with controls (OR = 5.0, P < 0.0005) as was DRB1*03, but not significantly. DRB1*03 was significantly associated with group 2 compared with controls (OR = 3.7, P < 0.05). No differences in HLA DRB1 frequencies from the healthy population were observed in the patients with type 2 diabetes — group 3 (Table 2). Genomic DNA preparation A salting out method (Miller et al., 1988) was used to prepare genomic DNA from peripheral blood leukocytes. The QIAamp spin columns (QIAGEN GmbH, Hilden, Germany) were used for rapid purification. Analysis of HLA DQB1 alleles DQB1*0302 was significantly increased in group 1 compared with controls (OR = 6.1, P < 0.00005), as was DQB1*0201, but not significantly. In the same group, DRB1 alleles Children (n = 261) Group 1 (n = 80) Group 2 (n = 70) Group 3 (n = 131) Control (n = 99) 01 03 04 07 08 09 10 11 12 13 14 15 16 NT NT NT NT NT NT NT NT NT NT NT NT NT 11.7 40.0 45.0**(5.0) 16.7 5.0 0 5.0 11.7 1.7 11.7 1.7 11.7 3.3 16.7 50.0*(3.7) 23.3 13.3 13.3 3.3 0 30.0 0 13.3 3.3 20.0 3.3 16.7 13.3 21.1 26.7 7.8 3.3 3.3 25.6 2.2 15.6 13.3 20.0 4.4 22.2 21.2 14.1 36.4 5.1 1.0 2.0 23.2 10.1 22.2 5.1 23.2 5.1 Data from children with diabetes obtained from Cinek et al. with permission for comparison. Data from healthy control obtained from Cerna et al. are compared with patient groups. Group 1 (type 1 diabetes in adults): C peptide < 200 pmol / L, anti-GAD > or < 50 ng /mL. Group 2 (LADA): C peptide > 200 pmol / L, anti-GAD > 50 ng /mL, > 6 months without insulin. Group 3 (type 2 diabetes): C peptide > 200 pmol / L, anti-GAD < 50 ng /mL. NT, not tested, * P < 0.05, ** P < 0.0005, OR values are in brackets. © 2003 Blackwell Publishing Ltd, European Journal of Immunogenetics 30, 401–407 Table 2. Frequency (%) of DRB1 alleles in Czech patients with diabetes 404 M. Cerna et al. DQB1 alleles Children (n = 261) Group 1 (n = 80) Group 2 (n = 70) Group 3 (n = 131) Control (n = 99) 0201 0202 0301 0302 0303 0304 0401 0402 0501 0502 0503 0504 0601 0602 0603 0604 61.0*10(3.4) NP 5.0*15(0.08) 65.0*15(9.0) NP NP NP NP NP NP 1.0*(0.13) NP NP 1.0*15(0.02) 4.0*5(0.20) NP 41.7 6.7 18.3 43.3**(6.1) 5.0 0 3.3 1.7 33.3 3.3 1.7 0 1.7 3.3 6.7 8.3 42.9 14.3 28.6 14.3 14.3 0 0 3.6 21.4 3.6 0 0 0 10.7 0 7.1 24.4 19.1 31.3 13.7 3.8 2.3 0 5.3 23.7 6.1 6.9 1.5 2.3 16.8 11.5 6.9 30.3 19.2 38.4 11.1 8.1 0 0 4.0 23.2 5.1 5.1 0 3.0 20.2 13.1 5.1 Table 3. Frequency (%) of DQB1 alleles in Czech patients with diabetes Data from children with diabetes used from Cinek et al. with permission for comparison. Data from healthy control used from Cerna et al. are compared with patient groups. Group 1 (type 1 diabetes in adults): C peptide < 200 pmol /L, anti-GAD > or < 50 ng /mL. Group 2 (LADA): C peptide > 200 pmol / L, anti-GAD > 50 ng /mL, > 6 months without insulin. Group 3 (type 2 diabetes): C peptide > 200 pmol / L, anti-GAD < 50 ng /mL. NP, not published, * P < 0.005, ** P < 0.00005, *5 P < 10–5, *10 P < 10−10, *15 P < 10−15. OR values are in brackets. Children (n = 261) DRB1 genotype 03/04 NP DQB1 genotype 0201/0302 36.0*15(22.0) 0201/0301 1.0**(0.1) Group 1 (n = 80) Group 2 (n = 70) Group 3 (n = 131) Control (n = 99) 17.0**(8.3) 6.7 0 2.0 15.0*(7.4) 0 3.6 3.6 0.8 6.1 2.0 8.1 Table 4. Frequency (%) of DQB1 and DRB1 genotypes associated with type 1 diabetes Data from children with diabetes used from Cinek et al. with permission for comparison. Data from healthy control used from Cerna et al. are compared with patient groups. Group 1 (type 1 diabetes in adults): C peptide < 200 pmol /L, anti-GAD > or < 50 ng /mL. Group 2 (LADA): C peptide > 200 pmol / L, anti-GAD > 50 ng /mL, > 6 months without insulin. Group 3 (type 2 diabetes): C peptide > 200 pmol / L, anti-GAD < 50 ng /mL. NP, not published, * P < 0.05, ** P < 0.01, *15 P < 10−15, OR values are in brackets. DQB1*0602 was decreased compared with controls, but not significantly. In group 2, DQB1*0201 was increased compared with controls, but not significantly. DQB1*0602 was non-significantly decreased compared with controls up to 10.7 vs. 20.2%; all carriers had the second DQB1 allele 0201 (30% of all 0602) or 0302 (70% of all 0602) (Table 3). HLA DR4 subtyping In patients with diabetes, we have distinguished three DRB1*04 alleles: DRB1*0401 (50% of all DR4), DRB1*0403 (25% of all DR4) and DRB1*0404 (25% of all DR4). For comparison, in controls, we distinguished five DRB1*04 alleles: DRB1*0401 (50% of all DR4), DRB1*0402 (7% of all DR4), DRB1*0404 (21% of all DR4), DRB1*0405 (14% of all DR4) and DRB1*0408 (7% of all DR4). Comparing the patients with the healthy population, we have not found any association of DRB1*04 subtypes with disease phenotypes. Heterozygosity for DRB1 or DQB1 genotypes We have observed a statistically significant increased prevalence of high risk DRB1*03/DRB1*04 and DQB1*0201/DQB1*0302 heterozygotes among patients in group 1 compared with controls (OR = 8.3, P < 0.01 and OR = 7.4, P < 0.05). This risk became more significant when we divided this group into anti-GAD-positive and anti-GAD-negative patients, because predisposing heterozygotes were particularly associated with the anti-GAD-positive subgroup (25%, OR = 12.4, P < 0.005 both). The protective DQB1*0201/DQB1*0301 genotype has not been found in group 1 of patients with type 1 diabetes mellitus. Other DQB1 genotypes have not been significantly associated with the disease (Table 4). © 2003 Blackwell Publishing Ltd, European Journal of Immunogenetics 30, 401– 407 HLA in Czech patients with diabetes mellitus DQB1 genotype 0201/0302 Children Group 1 Control 0 –4 years (n = 82) 5 –9 years (n = 98) 10 –14 years (n = 81) (n = 80) (n = 99) 45.0 36.0 27.0 15.0*(7.4) 2.0 405 Table 5. Frequency (%) of DQB1* 0201/0302 heterozygotes in dependence on the age of type 1 diabetes manifestation Data from diabetic children used from Cinek et al. with permission for comparison. Data from healthy controls used from Cerna et al. Group 1 (type 1 diabetes in adults): C peptide < 200 pmol / L, anti-GAD > or < 50 ng /mL. Data from each group are compared with each other. * P < 0.05, OR values are in brackets. The HLA class II association and age of onset Comparing our patients with type 1 diabetes with children with type 1, diabetes (Cinek et al., 2001), we found decreasing significance of predisposing alleles with increasing age of onset. The frequency of DQB1*0302 allele in children was 65%, in adult patients of group 1 43.3%, and in healthy controls 11.1% (Table 3). The frequency of DQB1*0201 allele in children was 61%, in adult patients of group 1 41.7%, and in healthy controls 30.3% (Table 3). A decreasing prevalence of genetic factors with increasing age of onset was not only found for predisposing single alleles, but also for DQB1* 0201/ 0302 heterozygotes (Table 5). While the prevalence of 0201/0302 heterozygotes among children diagnosed at the age of 0–4, 5–9, and 10 –14 years was 45, 36 and 27%, respectively, the prevalence of 0201/0302 heterozygotes among adults diagnosed after the age of 35 years was 15% in the patients of group 1. Discussion The classification of diabetes mellitus discriminate type 1, type 2, other specific types and gestational diabetes (Report, 2001). It has been reported that these types of diabetes involve separate genetic factors, suggesting that, in addition to the perceived differences in clinical manifestations and course, their etiologic mechanisms are distinct. The presence of islet cell autoantibodies in adult diabetic subjects who do not require insulin treatment for at least 6 months after the initial clinical diagnosis identifies the so-called latent autoimmune diabetes in the adult (LADA). Glutamic acid decarboxylase autoantibodies (GAD65Ab) are the best immune marker to identify LADA patients. The main goal of our study was to analyze HLA class II antigens in adult patients with autoimmune diabetes mellitus, to prove, if the basic data known from children are valid also in type 1 adult patients, and to compare these results with those from LADA patients. In view of the long and accurate followup in our study, we were able to further subdivide diabetic patients diagnosed after 35 years of age into three groups that differ in the presence of fasting C peptide and GAD antibodies, and insulin therapy, respectively. The association of DQB1*0302 and DRB1*04 that was observed with the group of adult patients with type 1 diabetes with negative C peptide (group 1) suggests that these alleles represent a marker or risk factor for requiring insulin treatment, as was reported (Horton et al., 1999). The highest risk in this group was for DQB1*0302. The odds ratio for DQB1*0302 was 6.1 (ratio 2.6–14.9). The odds ratio for DRB1*04 was lower, 5.0 (ratio 2.2–11.4). The frequencies of DQB1*0201 and DRB1*03 were increased, but not significant. The frequency of DQB1*0201/ DQB1*0302 and DRB1*03/DRB1*04 genotypes in the C-peptide-negative group was statistically different from controls (OR = 7.4, ratio 1.7–33.2 and OR = 8.3, ratio 1.9–36.4), but lower than in children with diabetes. The pattern of predisposing alleles in adults with type 1 diabetes is similar with that in children with type 1 diabetes, but the prevalence seems to be age related. Whether these age-dependent differences reflect different environmental influences remains to be proven. Our observations correspond with results in Sweden, where the prevalence of the HLA-DQB1*0201/0302 (DR3/ DR4) genotype reflects the age at onset of diabetes, and the 0302 genotype is associated with insulin dependency (Tuomi et al., 1999). The association of DRB1*03 only was found with the group of LADA patients with normal C peptide (group 2). The odds ratio for DRB1*03 was 3.7 (ratio 1.8–8.0). What was amazing, but very logical, was the exact match of the presence of C peptide with non-insulin-requirement. Previous studies proved that for early onset of type 1 diabetes, DQB1 alleles are a better marker of genetic predisposition than DRB1 alleles (Todd et al., 1987). Our findings support the observations of some investigators that DRB1 alleles are the main marker for late onset (Lohmann et al., 1997). Certain research groups have described DRB1*04 and DQB1*0302 as the main marker for LADA (Hosszufalusi et al., 2003). Why different predisposing DR alleles are detected in different ethnic groups is not clear. The hypothesis been suggested that HLA-DRB1 does not predispose to autoimmune disease per se, but rather fails to provide efficient protection (Zanelli et al., 2000). According to these facts we can hypothesize that type 1 diabetes mellitus in children or adults may have partly different immunogenetic etiopathogenesis than LADA. The different mechanism reflects different HLA association in these diseases. The most significant association is with DQB1 alleles in type 1 diabetes, but with DRB1 alleles in LADA. © 2003 Blackwell Publishing Ltd, European Journal of Immunogenetics 30, 401–407 406 M. Cerna et al. Patients with type 2 diabetes without signs of autoimmune insulitis (fasting CP normal, GAD and IA2 antibodies negative, without insulin therapy) did not differ in HLA frequencies from healthy Czech controls. The type of autoantibodies to islet cell antigens distinguish between acute-onset type 1 diabetes and LADA because GAD antibodies and ICA indicate slow disease progression, whereas the presence of IA2 antibodies is associated with an acute-onset clinical phenotype (Seissler et al., 1998). That is probably why 50% of patients with type 1 diabetes in adults are negative for anti-GAD. These patients could be positive for other non-tested antibodies or their antibody production has burnt-out over the course of years. Additional testing for IA2 antibodies revealed that 15% of patients in group 1 (20% in anti-GAD+ and 10% in anti-GAD–) and 11% of patients in group 2 are positive for this autoantibody. We conclude that Czech patients with autoimmune diabetes manifested after 35 years of age can be divided into two groups having different HLA class II associations: type 1 diabetes in adults with DQB1*0302 and DRB1*04 risk alleles, and LADA with DRB1*03 risk allele. The occurrence of HLA-DQB1*0302 (0201) and DRB1*04 (03) alleles in adults with type 1 diabetes was less frequent than in children, but more frequent than in patients with type 2 diabetes and healthy controls. Acknowledgements This study was financially supported by the Research Programme of Ministry of Education, Youth, and Sports of Czech Republic, VZ: J13/98: 111200001: The initial stages of diabetes, metabolic, endocrine and environmental damages of the organism (2001). We thank Anezka Koubova and Jana Potockova for excellent technical assistance. References Cerna, M., Fernandez-Vina, M., Ivaskova, E. & Stastny, P. (1992) Comparison of HLA class II alleles in gypsy and Czech populations by DNA typing with oligonucleotide probes. Tissue Antigens, 39, 111. Cinek, O., Kolouskova, S., Snajderova, M., Sumnik, Z., Sedlakova, P., Drevinek, P., Vavrinec, J. & Ronningen, K.S. (2001) HLA class II genetic association of type 1 diabetes mellitus in Czech children. Pediatric Diabetes, 2, 98. Drabek, J., Vranova, K., Ambruzova, Z., Petrek, M., Bartova, A. & Weigl, E. (1998) Class I typing by PCR-SSP in Olomouc. Bone Marrow Transplant, 22, S23. Dvorakova, L. & Majsky, A. (1979) HLA antigens in offspring of both diabetic parents. Casopis Lekaru Ceskych, 118, 1294. Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report. (2001) Diabetes Care, 24, S5. Gambelunghe, G., Forini, F., Laureti, S., Murdolo, G., Toraldo, G., Santeusanio, F., Brunetti, P., Sanjeevi, C.B. & Falorni, A. (2000) Increased risk for endocrine autoimmunity in Italian type 2 diabetic patients with GAD65 autoantibodies. Clinical Endocrinological (Oxford), 52, 565. Graham, J., Kockum, I., Sanjeevi, C.B., Landin-Olsson, M., Nystrom, L., Sundkvist, G. et al. (1999) Negative association between type 1 diabetes and HLA DQB1*0602-DQA1*0102 is attenuated with age at onset. Swedish Childhood Diabetes Study Group. European Journal of Immunogenetics, 26, 117. Horton, V., Stratton, I., Bottazzo, G.F., Shattock, M., Mackay, I., Zimmet, P., Manley, S., Holman, R. & Turner, R. (1999) Genetic heterogeneity of autoimmune diabetes: age of presentation in adults is influenced by HLA DRB1 and DQB1 genotypes (UKPDS 43). UK Prospective Diabetes Study (UKPDS) Group. Diabetologia, 42, 608. Hosszufalusi, N., Vatay, A., Rajczy, K., Prohaszka, Z., Pozsonyi, E., Horvath, L. et al. (2003) Similar genetic features and different islet cell autoantibody pattern of latent autoimmune diabetes in adults (LADA) compared with adult-onset type 1 diabetes with rapid progression. Diabetes Care, 26, 452. Lohmann, T., Seissler, J., Verlohren, H.J., Schroder, S., Rotger, J., Dahn, K., Morgenthaler, N. & Scherbaum, W.A. (1997) Distinct genetic and immunological features in patients with onset of IDDM before and after age 40. Diabetes Care, 20, 524. Loudova, M., Sramkova, I., Cukrova, V., Sieglova, Z. & Brdicka, R. (1999) Frequencies of HLA-DRB1-DQB1 and -DPB1 alleles in Czech population. Folia Biologica (Praha), 45, 27. Miller, S.A., Dykes, D.D. & Polesky, H.F. 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(1988) Genetic heterogeneity, modes of inheritance, and risk estimates for a joint study of Caucasians with insulin-dependent diabetes mellitus. American Journal of Human Genetics, 43, 799. Todd, J.A., Bell, J.I. & McDevitt, H.O. (1987) HLA-DQβ gene contributes to susceptibility and resistance to insulin-dependent diabetes mellitus. Nature, 329, 599. Tuomi, T., Carlsson, A., Li, H., Isomaa, B., Miettinen, A., Nilsson, A. et al. (1999) Clinical and genetic characteristics of type 2 diabetes with and without GAD antibodies. Diabetes, 48, 150. Vauhkonen, I., Niskanen, L., Knip, M., Ilonen, J., Vanninen, E., Kainulainen, S., Uusitupa, M. & Laakso, M. (2000) Impaired insulin secretion in non-diabetic offspring of probands with latent autoimmune diabetes mellitus in adults. Diabetologia, 43, 69. Verge, C.F., Stenger, D., Bonifacio, E., Colman, P.G., Pilcher, C., Bingley, P.J. & Eisenbarth, G.S. (1998) Combined use of autoantibodies (IA-2 autoantibody, GAD autoantibody, insulin autoantibody, cytoplasmic islet cell antibodies) in type 1 diabetes: Combinatorial Islet Autoantibody Workshop. Diabetes, 47, 1857. Vondra, K., Vrbikova, J., Ivaskoval, E., Pobisova, Z., PorsovaDutoit, I., Skibova, J., Stolba, P., Voborska, M. & Sterzl, I. (1996) Thyroglobulin and microsome autoantibodies and © 2003 Blackwell Publishing Ltd, European Journal of Immunogenetics 30, 401– 407 HLA in Czech patients with diabetes mellitus their clinical significance in adult type I diabetics. Vnitrni Lekarstvi, 42, 767. Zanelli, E., Breedveld, F.C. & de Vries, R.R. (2000) HLA association with autoimmune disease: a failure to protect? Rheumatology, 10, 1060. Zetterquist, H. & Olerup, O. (1992) Identification of the HLA-DRB1*04-DRB1*07, and -DRB1*09 alleles by PCR 407 amplification with sequence-specific primers (PCR-SSP) in 2 hours. Human Immunology, 34, 64. 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Diabetic Medicine, 16, 201. © 2003 Blackwell Publishing Ltd, European Journal of Immunogenetics 30, 401–407 Original paper No. 6 DIABETES MELLITUS IN ADULTS: ASSOCIATION OF HLA DRB1 AND DQB1 DIABETES RISK ALLELES WITH GADAB PRESENCE AND C-PEPTIDE SECRETION Novota P, Cerna M, Kolostova K, Cejkova P, Zdarsky E, Novakova D, Kucera P, Novak J, Andel M Immunol Lett. 2004; 95:229-232 119 Immunology Letters 95 (2004) 229–232 Diabetes mellitus in adults: association of HLA DRB1 and DQB1 diabetes risk alleles with GADab presence and C-peptide secretion Peter Novotaa,∗ , Marie Cernaa , Katarina Kolostovaa , Pavlina Cejkovaa , Emanuel Zdarskya , Dana Novakovab , Petr Kucerab , Jan Novakc , Michal Andelc a Department of Cell and Molecular Biology, The 3rd Faculty of Medicine, Charles University at Prague, Czech Republic Department of Cell and Molecular Immunology, The 3rd Faculty of Medicine, Charles University at Prague, Czech Republic Diabetes Center and Department of Internal Medicine II, The 3rd Faculty of Medicine, Charles University at Prague, Czech Republic b c Received 29 June 2004; received in revised form 21 July 2004; accepted 29 July 2004 Available online 23 August 2004 Abstract In our study, we investigated the relationship of HLA class II alleles to antibody production against glutamic acid decarboxylase (GADab) and to C-peptide secretion (CP) in diabetic patients. A group of 334 patients (190 women) diagnosed after 35 years of age and 99 control subjects were studied. Patients were divided into four groups according to concentrations of CP and GADab, respectively (CP high/low, GADab positive/negative). HLA DQB1 and DRB1 alleles were genotyped by SSP-PCR. The significance of DQB1 and DRB1 risk alleles was evaluated by examination of their odds ratios computed by testing 2 × 2 tables considering Bonferonis’ corrected P < 0.05 as significant. We found strong association between the HLA DRB1*03 risk allele and presence of GADab, and close relationship of the HLA DRB1*04 and HLA DQB1*0302 risk alleles with decreased CP level. Taken together we conclude that the DRB1*04 and DQB1*0302 alleles are associated with progressive decrease of CP level, while DRB1*03 is a significant genetic marker of autoantibody (GADab) development. © 2004 Elsevier B.V. All rights reserved. Keywords: HLA/DQ/DR; Glutamic acid decarboxylase (GAD); C-peptide (CP); Diabetes mellitus (DM) 1. Introduction Diabetes mellitus with onset after the age of 35 years [1] is a heterogeneous group representing at least two major groups of diseases: insulin deficient (type 1 diabetes mellitus, T1DM) and insulin resistant diabetes mellitus (type 2 diabetes mellitus, T2DM) [2,3]. The etiology of the former is an autoimmune disorder and the etiology of the latter is a metabolic disorder. However, about 10% of patients initially ∗ Corresponding author. Present address: Department of Cell and Molecular Biology, Department of Immunogenetics (Z4), DNA laboratory, Institute for Clinical and Experimental Medicine, Vı́deňská 1958/9, 14021 Prague 4, Czech Republic. Tel.: +420 261 362 350; fax: +420 241 721 603. E-mail address: [email protected] (P. Novota). 0165-2478/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.imlet.2004.07.016 diagnosed as patients with type 2 diabetes manifest autoimmune markers of -cell destruction. These subjects have a slowly progressive form of autoimmune diabetes mellitus and are considered having latent autoimmune diabetes mellitus in adults (LADA) [4–7]. The most important gene loci defining risk to T1DM are located within the HLA gene region. HLA DQ molecules are of primary importance but HLA DR gene products modify the risk conferred by HLA DQ. The risk associated with an HLA genotype is defined by the particular combination of susceptible and protective alleles. The complicated analysis of HLA genotypes is simplified by strong linkage disequilibrium between HLA DRB1, DQA1 and DQB1 loci. Childhood T1DM is characterized by an abrupt onset and ketosis and is 230 P. Novota et al. / Immunology Letters 95 (2004) 229–232 associated with HLA DRB1*04-DQA1*0301-DQB1*0302, and a high frequency of insulin and thyrosin-phosphatase autoantibodies [7,8]. The term latent autoimmune diabetes in adults was introduced to define adult diabetic patients initially non-insulinrequiring but with immune markers of type 1 diabetes that, in a number of cases, progress to insulin dependency [6]. It is characterized by the presence of glutamic acid decarboxylase 65 autoantibodies (GADab) and/or islet cell antibodies (ICA), and is associated with HLA DRB1*03DQA1*0501-DQB1*0201 and/or with HLA DRB1*04DQA1*0301-DQB1*0302 haplotype [5,9]. In patients diagnosed after 35 years of age, the relation of HLA risk alleles and GADab and/or C-peptide secretion (CP), generally used as a marker of insulin production, is not completely clear. In this study, we investigated the relationship between the presence of diabetes risk HLA alleles, and the presence of GADab and/or CP in diabetic patients diagnosed after 35 years of age. 2. Patients and methods 2.1. C-peptide level Levels of CP were measured on an empty stomach (fasting C-peptide). For detection, we used an immunoradiometric method (Immunotech, Prague, Czech Republic). The serum samples from 99 healthy individuals provided control values: average value 486 pmol/l; median 449 pmol/l; standard deviation 170 pmol/l; and minimal–maximal value 206–934 pmol/l. According by values lower than 200 pmol/l were taken as decreased C-peptide secretion (marked as low CP, CP negative, or CP−). 2.2. GAD antibodies The presence of IgG antibodies against GAD was detected by the ELISA method (Roche Molecular Biochemicals, Mannheim, Germany). The test had 98% of specificity and 69% of sensitivity. The cut-off point of the test was 32 ng/ml. Levels lower than this cut-off point were considered negative (marked as GADab negative, or GADab−), levels higher than 32 ng/ml were considered positive (marked as GADab positive, or GADab+) [10]. 2.3. Genomic DNA preparation The QIAamp spin columns (QIAGEN GmbH, Hilden, Germany) were used for extraction and purification of genomic DNA from peripheral blood leukocytes. 2.4. Analysis of HLA DRB1 and DQB1 alleles HLA DRB1 and DQB1 alleles were genotyped using PCR with sequence specific primers (SSP-PCR) supplied by Genovision (Oslo, Norway) [11,12]. 2.5. Statistics Allele and gene frequencies were calculated using standard methods. The significance of the differences in allele frequencies were evaluated by χ2 and P values were calculated with Bonferoni correction. The strength of the observed associations was estimated by calculating odds ratios (OR) using the method by Woolf. 2.6. Group of patients We investigated 334 patients (190 women) with diabetes mellitus with disease onset after 35 years of age. All of them were Caucasians and residents of the Middle region of Czech Republic. Diagnosis of diabetes was established according to the current WHO definitions and criteria for diagnosing diabetes [13]. The T1DM (49), LADA (48), and T2DM (237) patients were included. Patients with autoimmune diabetes (T1DM and LADA) were negative for C-peptide and/or positive for autoantibodies. All of them had insulin therapy. LADA was defined by a minimum 6 months long phase after diagnosis without insulin therapy. Patients with T2DM were positive for C-peptide. None ever had insulin therapy. The age at disease onset and duration of DM were recorded. For detailed characterization of patient groups see Table 1. Table 1 Clinical characteristics of investigated patient groups Number of patients Number of female Body mass index (BMI) Age of onset of DM (years) Duration of DM (years) Diagnosis T1DM, N = 49 LADA, N = 48 T2DM, N = 237 CP+ (≥200 pmol/l) CP− (≤200 pmol/l) GADab+ (≥32 ng/ml) GADab− (≤32 ng/ml) 285 153 31.3 (22.2; 50.1) 53 (35; 81) 12.35 (0; 31) 49 32 28.0 (22.4; 36.7) 44 (36; 56) 14.46 (1; 29) 97 58 31.1 (22.4; 45.5) 52 (35; 71) 13.27 (1; 31) 237 136 31.1 (22.2; 50.1) 53 (35; 81) 12.48 (0; 31) 50.5% (49/97) 49.5% (48/97) 0.0% (0/97) 0.0% (0/237) 0.0% (0/237) 100.0% (237/237) 0.0% (0/285) 16.8% (48/285) 83.2% (237/285) 100.0% (49/49) 0.0% (0/49) 0.0% (0/49) Values of BMI, age of onset of disease, and duration of DM are calculated as average. In brackets are minimal and maximal values. P. Novota et al. / Immunology Letters 95 (2004) 229–232 231 Table 2 HLA DQB1 frequencies in investigated patient groups (GADab+, GADab−, CP+, and CP−) DQB1*0201 DQB1*0202 DQB1*0301 DQB1*0302 DQB1*0303 DQB1*0304 DQB1*0402 DQB1*0501 DQB1*0502 DQB1*0503 DQB1*0504 DQB1*0601 DQB1*0602 DQB1*0603 DQB1*0604 GADab+ (N = 45) GADab− (N = 146) CP+ (N = 153) CP− (N = 38) Controls (N = 99) 0.467 0.133 0.267 0.289 0.111 0.022 0.067 0.178 0.022 0.000 0.000 0.022 0.067 0.022 0.111 0.267 0.185 0.315 0.205 0.048 0.021 0.048 0.281 0.068 0.062 0.014 0.021 0.164 0.110 0.062 0.288 0.190 0.333 0.170 0.065 0.020 0.059 0.248 0.059 0.059 0.013 0.020 0.170 0.098 0.065 0.447 0.105 0.184 0.447 (6.48)∗ 0.053 0.026 0.026 0.316 0.053 0.000 0.000 0.026 0.026 0.053 0.079 0.303 0.192 0.384 0.111 0.081 0.000 0.040 0.232 0.051 0.051 NT 0.030 0.202 0.131 0.051 Statistically significant difference in frequency of allele DQB1*0302 in group CP− in comparison with healthy control. Data from healthy control used from Cerna et al. [14]. ∗ P < 0.001; OR values in brackets. c 3. Results 4. Discussion We investigated the distribution of HLA DRB1 and DQB1 alleles and the odds ratios of risk and protective alleles in the four patient groups (Table 1). Subsequently, we explored the relationship between HLA risk or protective alleles, and positivity (negativity) for GADab and values of CP, respectively. In the GADab+ group, we found only one predisposing allele (DRB1*03, Pc = 0.0002). In the GADab− group, we did not find any association with HLA alleles. In the CP− group, we detected two risk alleles, DRB1*04 (Pc = 0.0001) and DQB1*0302 (Pc = 0.0002). In the CP+ group, we did not detected any HLA association. None protective alleles were ascertained in all these groups (see Tables 2 and 3). Odds ratios of the predisposing alleles were following: in the GADab+ group, OR = 4.90 (CI 95% = 2.21; 10.94) was calculated for allele DRB1*03; in the CP−group, OR = 5.80 (CI 95% = 2.36; 14.39) was calculated for allele DRB1*04; and OR = 6.48 (CI 95% = 2.43; 17.54) for allele DQB1*0302. We analyzed the frequencies of HLA class II alleles in patients with diabetes mellitus manifested after 35 years of age. We found two risk HLA alleles in the CP− group and one in the GADab+ group of patients. The frequencies of the DRB1*04 and DQB1*0302 alleles were significantly increased in the CP− group (Pc < 0.001 for both alleles). The odds ratio was higher for DQB1*0302 (OR = 6.48) than for DRB1*04 (OR = 5.80), what suggests DQB1*0302 as main risk allele in the CP−group. The frequency of DRB1*03 allele was significantly higher in the GADab+ group (Pc < 0001). Its odds ratio was the lowest in the study (OR = 4.90). Our results are similar to data published by Caillat-Zucman et al. [7] or Krokowski et al. [8]. The only difference is in the frequency of the known risk allele DQB1*0201. The frequency of this allele was increased in our GADab+ group, but it did not reach statistically significant difference when compared with controls. Table 3 HLA DRB1 frequencies in investigated patient groups (GADab+, GADab−, CP+, and CP−) DRB1*01 DRB1*03 DRB1*04 DRB1*07 DRB1*08 DRB1*09 DRB1*10 DRB1*11 DRB1*12 DRB1*13 DRB1*14 DRB1*15 DRB1*16 GADab+ (N = 51) GADab− (N = 111) CP+ (N = 122) CP− (N = 43) Controls (N = 99) 0.157 0.569 (4.90)∗ 0.314 0.118 0.098 0.020 0.000 0.255 0.000 0.137 0.020 0.176 0.020 0.198 0.162 0.270 0.252 0.081 0.027 0.045 0.225 0.018 0.135 0.108 0.171 0.054 0.164 0.221 0.230 0.230 0.090 0.033 0.033 0.262 0.016 0.156 0.107 0.197 0.041 0.233 0.465 0.488 (5.80)∗ 0.163 0.070 0.000 0.047 0.140 0.000 0.093 0.000 0.093 0.047 0.222 0.212 0.141 0.363 0.050 0.010 0.020 0.232 0.101 0.222 0.050 0.232 0.050 Statistically significant difference in frequency of allele DRB1*03 in group GADab+ and allele DRB1*04 in group CP− in comparison with healthy control. Data from healthy control used from Cerna et al. [14]. ∗ Pc < 0.001; OR values in brackets. 232 P. Novota et al. / Immunology Letters 95 (2004) 229–232 Our study showed that there is a difference in HLA class II allele frequencies between GADab+ group and CP−group. GADab positivity is strongly associated with the HLA DRB1*03 allele and CP negativity is linked to the DQB1*0302 and HLA DRB1*04 alleles. These results are supported by Gambelunghe et al. [9] who stated that GADab is closely associated with the presence of HLA DRB1*03DQB1*02 haplotype. Finally, we conclude that the DRB1*04 and DQB1*0302 alleles are associated with decrease of CP levels while DRB1*03 is a significant genetic marker of autoantibody (GADab) development. Acknowledgements This study was financially supported by the Research Program of Ministry of Health of Czech Republic, IGA MZ CR: NB/7432-3/2003. References [1] Newman B, Selby JV, King MC, Slemenda C, Fabsitz R, Friedman GD. Concordance for type 2 (non-insulin-dependent) diabetes mellitus in male twins. Diabetologia 1987;30:763–8. 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Antibodies to glutamic acid decarboxylase reveal latent autoimmune diabetes mellitus in adults with a non-insulin-dependent onset of disease. Diabetes 1993;42:359–62. [7] Caillat-Zucman S, Garchon HJ, Timsit J, Assan R, Boitard C, DjilaliSaiah I, Bougneres P, Bach JF. Age-dependent HLA genetic heterogeneity of type 1 insulin-dependent diabetes mellitus. J Clin Invest 1992;90:2242–50. [8] Krokowski M, Bodalski J, Bratek A, Boitard C, Caillat-Zucman S. HLA class II-associated predisposition to insulin-dependent diabetes mellitus in a Polish population. Hum Immunol 1998;59:451–5. [9] Gambelunghe G, Forini F, Laureti S, Murdolo G, Toraldo G, Santeusanio F, Brunetti P, Sanjeevi CB, Falorni A. Increased risk for endocrine autoimmunity in Italian type 2 diabetic patients with GAD65 autoantibodies. Clin Endocrinol (Oxf) 2000;52:565–73. [10] Verge CF, Stenger D, Bonifacio E, Colman PG, Pilcher C, Bingley PJ, Eisenbarth GS. Combined use of autoantibodies (IA-2 autoantibody, GAD autoantibody, insulin autoantibody, cytoplasmic islet cell antibodies) in type 1 diabetes: Combinatorial Islet Autoantibody Workshop. Diabetes 1998;47:1857–66. [11] Olerup O, Zetterquist H. HLA-DR typing by PCR amplification with sequence-specific primers (PCR-SSP) in 2 h: An alternative to serological DR typing in clinical practice including donorrecipient matching in cadaveric transplantation. Tissue Antigens 1992;39:225–35. [12] Olerup O, Aldener A, Fogdell A. HLA-DQB1 and -DQA1 typing by PCR amplification with sequence-specific primers (PCR-SSP) in 2 h. Tissue Antigens 1993;41:119–34. [13] World Health Organization. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Geneva, Switzerland: Department of Noncommunicable Disease Surveillance, WHO; 1999. [14] Cerna M, Fernandez-Vina M, Ivaskova E, Stastny P. Comparison of HLA class II alleles in gypsy and Czech populations by DNA typing with oligonucleotide probes. Tissue Antigens 1992;39:111. Original paper No. 7 GENETIC RISK FACTORS IN AUTOIMMUNE DIABETES MELLITUS, THEIR SIGNIFICANCE AND FUNCTION Novota P, Cejkova P, Cerna M, Andel M Cas Lek Cesk. 2004; 143(3):159-63. Review in Slovak 124 Original paper No. 8 HLA, NFKB1 AND NFKBIA GENE POLYMORPHISM PROFILE IN AUTOIMMUNE DIABETES MELLITUS PATIENTS Kolostova K, Pinterova D, Novota P, Romzova M, Cejkova P, Pruhova S, Lebl J, Treslova L, Andel M, Cerna M Exp Clin Endocrinol Diabetes 2007 115:124-129 130 124 Article HLA, NFKB1 and NFKBIA Gene Polymorphism Profile in Autoimmune Diabetes Mellitus Patients Authors K. Katarina1, P. Daniela1, N. Peter1, R. Marianna1, C. Pavlina1, P. Stepanka2, L. Jan2, T. Ludmila3, A. Michal3, C. Marie1 Affiliations 1 2 3 Key words 䉴 T1DM 䊉 䉴 HLA 䊉 䉴 NFKB1 䊉 䉴 NFKBIA 䊉 䉴 LADA 䊉 Funding sources: The project was supported by MSM 0021620814 received 1. 3. 2006 first decision 12. 7. 2006 accepted 26. 7. 2006 Bibiliography DOI 10.1055/s-2007-949589 Exp Clin Endocrinol Diabetes 2007; 115:124–129 © J.A. Barth Verlag in Georg Thieme Verlag KG · Stuttgart · New York · ISSN 0947-7349 Correspondence K. Katarina Medical Faculty Charles University · Department of Molecular and Cell Biology · Ruská 87 · 10034 Prague 10 · Czech Republic Tel.: + 420/26 71 02 66 2 Fax: + 420/26 71 02 65 0 [email protected]. cuni.cz Department of Molecular and Cell Biology, 3rd Medical Faculty, Charles University, Prague Czech Republic Department of Pediatrics, Centrum for Diabetes, metabolism and nutrition research of 3rd Medical Faculty, Charles University, Prague Czech Republic Second Internal Clinics, Centrum for Diabetes, metabolism and nutrition research of 3rd Medical Faculty, Charles University, Prague Czech Republic Type 1 diabetes mellitus (T1DM) is one of the long-time studied autoimmune disorders. The triggering of the autoimmune process has been ascribed to various genes active in the regulation of the cytokine gene transcription including the Rel/NF-B gene family. In our study the gene polymorphism of HLA class II, NFKB1 (nuclear factor of kappa light polypeptide gene enhancer in B-cells 1) and NFKBIA (inhibitor of nuclear factor kappa B) was tested. Patients were divided into the subgroups in relation to the disease type: T1DM in children, T1DM in adults, and Latent Autoimmune Diabetes in Adults (LADA). HLA-DRB1*04 and HLA-DQB1*0302 have been detected as risk factors for T1DM in adults and particularly in children (P < 0.0001, OR = 22.9 and 46.5 respectively). HLA-DRB1*03 has been found as a single risk factor for LADA (P < 0.0001, OR = 4.9). We detected 15 alleles for the NFKB1 gene polymorphism (CA-repeats) in the Czech population. The alleles were ranging in size from 114-142 bp corresponding to 10–25 CA repeats. Frequency of the A7 allele of NFKB1 gene has been significantly increased in T1DM adults (P < 0.01). There was no difference in A and a G allele frequency of NFKBIA gene between the control group and patients, but the association of the AA genotype of NFKBIA gene has been found for LADA (P < 0.05). Summarizing our results we concluded that there is a high probability of association of gene polymorphism from Rel/NFB family with an autoimmune diabetes course. Due to the results obtained in the epidemiological study we have been looking also for the function significance of the genetic predisposition. No significant changes have been observed by real time PCR testing of HLA-DRB1*04 gene and NFKB1 gene expression between T1DM diabetic group with different HLA, NFKB1, NFKBIA genetic background. Abbreviations & Introduction & Abstract & LADA Latent autoimmune diabetes in adults NFB Nuclear factor kappa B NFKB1 Nuclear factor of kappa light polypeptide gene enhancer in Bcells 1 gene NFKBIA Inhibitor of nuclear factor kappa B gene T1DM Type 1 diabetes mellitus Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease characterized by a loss of tolerance towards own antigenic structures of beta-pancreatic cells. The destruction of beta cells subsequently leads to the loss of insulin production. T1DM is not a homogeneous disease, since several of its clinical features are different in children up to 6 years of age as compared to older patients (Csorba & Lyon, 2005). There are more factors, which trigger the autoimmune response in susceptible individuals; however, they are only partially known so far. One of the predisposing factors is the genotype background, ascribed mainly to genes of the HLA. Out of extensive genetic and epidemiological studies, the Caucasoid population is known to have a significant association of insulin-depend- Katarina K et al. HLA, NFKB1 and NFKBIA Gene Polymorphism … Exp Clin Endocrinol Diabetes 2007; 115:124–129 Article 125 Table 1 Characteristics of the tested groups Features 80 Female Age at disease onset (years) Duration of DM (years) Insulin therapy Fasting C peptide (pmol/L) Anti-GAD (ng/ml) * Group 1 Group 2 Group 3 Group 4 T1DM children n=179 60 % 7.7 (1–16) 4.9 (1–16) yes T1DM adults n=75 52 % 22.1 (20–45) 17.0 (1–50) yes Control n=153 * * * * LADA n=31 44 % 47.0 (25–64) 15.0 (3–32) yes 478.0 (4.4–1522) 392.0 (5–2800) * * * no * * data not available. ent diabetes mellitus with the increased frequencies of haplotypes HLA-DRB1*04-DQA1*0301-DQB1*0302 and DRB1*0301DQA1*0501-DQB1*0201. The strength of associations is ethnically variable. The HLA genotype DRB1*03-DQB1*0201/DRB1*04DQB1*0302 confers a 25-fold increase in the risk of T1DM. (Jaini et al., 2002; Rewers et al., 2003; Shawkatova et al., 2000). It is well known that T1DM results from the breakdown of peripheral tolerance. (Wheat et al., 2004) that is ended with cytokine-induced beta-cell death. Since the nuclear factor kappa-B (NFB) plays an important role in cytokine-induced gene activation, it is a very attractive candidate for T1DM predisposition. NFB is a transcription factor which interacts with kappa inhibitory proteins (IB) to regulate gene expression (Curran et al., 2002) of a variety of processes including innate and adaptive immune responses, cell growth, apoptosis, tissue differentiation and inflammation. (Baldwin, 2001). The NFB transcription factor complex has two alternative DNA binding subunits, NFKB1 and NFKB2. NFKB1 encodes two isoforms, the cytoplasmatic non-DNA-binding p105, and the 50 kDa DNAbinding p50 (Heron et al., 1995). To exert its effect, p50 binds to p65 (encoded by NFKB2) to form biologically active heterodimers, which activate transcription in promoter sequences of inflammatory genes (e.g. IL-12, TNF-, IFN-), but alternatively p50 able to form homodimers that block gene transcription by binding to NFB response sites in nuclues. Recent studies have investigated role of NFB in the pathogenesis of various human diseases including immune deficiency, carcinogenesis and atherogenesis. Lately the possible link between NFB and the development of insulin resistance, type 2 diabetes (Arkan et al., 2005; Cai et al., 2005; Chen, 2005; Evans et al., 2002) and in diabetic polyneuropathies (Haslbeck et al., 2005) has also been suggested. This study investigated common variants within the genes coding for NFB and IB, NFKB1 [4q24] and NFKBIA [14q13] to test the probable genetic predisposition to autoimmune diabetes. The NFB complex is inhibited by IB proteins (NFKBIA or NFKBIB), which inactivate NFB by trapping it in the cytoplasm. Phosphorylation of serine residues on the IB proteins by kinases (IKBKA or IKBKB) marks them for destruction via the ubiquitination pathway, thereby allowing activation of the NFB complex. NFKBIA is encoding for IkB, which binds preferentially to p65. After degradation of IkB, p65 tranlocates to the nucleus where it can form heterodimers with p50, released from p105 and following NFB heterodimer complex binds to decameric DNA sequences and activates transcription of NFB regulated target genes (Barnes & Karin, 1997; Bierhaus et al., 2001; Hayden & Ghosh, 2004). A polymorphic dinucleotide CA microsatellite repeat, with 18 described alleles, has been identified close to the coding region of the human NFKB1 gene. NFKBIA genotypes (A/G in 3’UTR) have been determined in previous studies as wild-type 424 bp; variant 306 and 118 bp; and heterozygote 424, 306 and 118 bp after restriction digestion (Curran et al., 2002). Following the results obtained in our epidemiological studies we have also focused on the probable functional significance of the tested gene polymorphisms. It has been demonstrated that variations of the density of HLA class II molecules on APCs influence the intensity and the nature of T cell response (Lechler et al., 1985). Due to the fact that NFkB participates even if indirectly on the regulation of HLA transcription we have finally decided to compare the mRNA level of the HLA-DRB1*04 allele and NFKB1 gene on APCs of peripheral blood leucocytes in diabetic patients in connection to the genotype background tested previously. Methods & Subjects The 267 individuals with a previous diabetes mellitus diagnosis, 159 ethnically matched controls for NFKBIA analysis and 58 controls for NFKB1 genotyping were involved into the study. Criteria of the current WHO definitions for diagnosing diabetes were applied (World Health Organization Definition, 1999), considering patients’ clinical features and laboratory data, including the presence of anti-islet autoantibodies (autoantibodies to glutamic acid decarboxylase - GAD65) and serum C-peptide (CP) level. All of the patients had insulin therapy. LADA was defined by a minimum 6 months long phase after diagnosis without insulin therapy. All subjects were informed and consented to participate before the study. The following parameters were recorded for each patient at the time of study: sex, age, disease duration, complications and family history (see Table 1). Both affected and control populations were recruited from individuals residing in the same geographical location in the Czech region and were from Caucasian descent. Genotyping DNA was extracted from peripheral blood leukocytes using of DNA blood mini isolation method (Qiagen, Hilden, Gemany). Genotyping. of the NFKB1 dinucleotide repeat with use of fluorescently labeled primers was previously described by Ota et al. (Ota et al., 1999). Polymerase chain reaction (PCR) amplification was performed in a reaction mixture containing 25–50 ng genomic DNA, 0.5 mM each primer, 0.2 mM dNTPs, 1xPCR buffer, 2.5 mM MgCl2 and DNA polymerase. PCR conditions were 4 min at 94˚C; 30 cycles of 30 s at 94˚C, 30 s at 52˚C and 30 s at 72˚C; followed by 5 min at 72˚C. NFKB1 alleles were then detected by Katarina K et al. HLA, NFKB1 and NFKBIA Gene Polymorphism … Exp Clin Endocrinol Diabetes 2007; 115:124–129 126 Article Table 2 The relative risk values and more additive statistics for T1DM associated HLA – alleles in the tested groups Groups LADA T1DM children T1DM adults Controls Alelle frequencies HLA-DRB1*03 P Relative risk Odds ratio HLA-DRB1*04 P Relative risk Odds ratio HLA-DQB1*0201 P Relative risk Odds ratio HLA-DQB1*0302 P Relative risk Odds ratio n=27 % 29.6 0.0001 3.25 (1.69–6.23) 4.94 (1.83–13.49) 18.5 n.s. – – 25.0 n.s. – – 14.3 n.s. – – n=188 % 25.0 0.0001 1.51 (1.25–1.81) 3.30 (1.78–6.17) 43.8 0.0001 3.12 (2.35–4.15) 22.87 (10.89–48.87) 21.7 n.s. – – 30.8 0.0001 8.98 (4.76–16.93) 46.48 (16.15–140.23) n=62 % 21.0 n.s – – 34.6 0.0001 3.42 (2.32–5.04) 10.47 (4.53–24.65) 21.8 n.s. – – 29.0 0.0001 3.02 (2.12–4.30) 9.49 (3.91–23.59) n=99 % 10.1 fragment analysis on the ALF express II detection system (Amersham Pharmacia Biotech, Uppsala, Sweden). NFKBIA amplification was performed in a 20 l final volume containing genomic DNA, 1xPCR buffer, 3.75 mM MgCl2, 0.2 mM dNTPs, 0.5 mM each primer and DNA polymerase. PCR conditions were initial denaturation at 94˚C for 5 min, followed by 30 cycles of 94˚C for 30 s, 52˚C for 30 s and 72˚C for 30 s, with a final extension of 2 min at 72˚C. Following amplification, 10 l of product was digested with HaeIII at 37˚C overnight. Samples were then loaded into an ethidium bromide stained 2 % agarose gel for genotype determination. Genotypes of 268 patients and 159 matched controls were determined as type (AA), 424 bp; variant (GG), 306 and 118 bp; and heterozygote (AG), 424, 306 and 118 bp (Curran et al., 2002). HLA class II typing was performed according to the standard protocol of Genovision (SSP-PCR) (Genovision, Oslo, Norway). PCR conditions were initial denaturation at 94˚C for 5 min, followed by 30 cycles of 94˚C for 30 s, 52˚C for 30 s, 72˚C for 30 s, with a final extension of 10 min at 72˚C. Gene expression testing According to the genotyping study the independent sets of children and adults T1DM patients and sex- and age-matched healthy controls were chosen for the expression studies. 28 T1DM patients included in this study were adults, and 55 T1DM patients were children. The adult control set of patients was created in cooperation with blood transfusion stationary. The age of adults was 36,4 ± 11,5 (mean ± SD). The children - age matched control set was obtained from a phenylketonuria study. The age of children control set was 11, 4 ± 8,2. (mean ± SD). The control sets were matched by HLA-DRB1*04 appearance. 6.5 26.3 5.1 – – Biosystems, Foster City, CA, USA). The cDNA was amplified using specific primers for HLA-DRB1*04 designed by Primer express® as well as of Taqman®MGB (HLA-DRB1*04 gene forward primer 5´ACACCCGACCACGTTTCTTG 3´, HLA-DRB1*04 gene forward primer 5´TCCGTCCCGTTGAAGAAATG 3´, HLA-DRB1*04 Taqman®MGB probe 6-FAM-CACTCATGTTTAACCTGCT). Testing of NFKB1 expression was done with assay on demand set of primers and probes. As an internal control the human beta actin was used (Applied Biosystems, Foster City, CA, USA). The 2 − Ct [2-delta delta Ct] method was applied for relative quantification (Livak & Schmittgen, 2001). Statistical analysis Allele and genotype frequencies, phenotype frequencies, and the frequency of an allele in the chromosomal pool of population were determined by direct counting for all groups of patients and controls. The genotype frequencies were tested for confirmation to Hardy-Weinberg equilibrium. For statistical establishment of significant differences genotype and allele distributions were compared between two populations using 2 analysis, followed by Bonferroni correction for multiply comparisons. The strength of the observed associations was estimated by calculating odds ratios [relative risk (RR)] using the method by Woolf. The results from real time PCR were compared between the groups by one-way ANOVA testing and also by nonparametric Kruskall-Wallis statistics. The observed groups were also compared by Dunn’s multiple comparison tests. P-value < 0.05 was considered significant. Real time RT-PCR analysis Results & The HLA association study Peripheral blood with EDTA was collected by venipuncture, and APCs were immediately separated by immunomagnetic separation by Dynabeads (Dynal HLA-class II, 210.04, Dynal, Oslo, Norway). Total RNA was extracted using the RNA blood mini kit (Qiagen, Hilden, Germany). RNA was reverse transcribed by Taqman® real time PCR reagents. The quantitative real time RT-PCR was performed in duplicates using the Taqman® PCR Kit in 96well microtiter plates on the ABI PRISM 7000 Sequence Detector Systems, according to the manufacturer’s instructions (Applied The HLA association study was performed to compare the risk values between the tested groups. HLA-associations have shown strong age-dependence (see Table 2). The relative risk values for HLA-DQB1*0302 were almost three times higher in the group of children with diabetes than the relative risk values in T1DM of adults. On the other side HLA-DRB1*03 was associated with the group of LADA patients two times stronger than with the group of children diabetics. HLA-DRB1*04 was significantly increased in both T1DM groups compared to controls and the power of Katarina K et al. HLA, NFKB1 and NFKBIA Gene Polymorphism … Exp Clin Endocrinol Diabetes 2007; 115:124–129 Article 127 Table 3 NFKB1 Frequencies of detected NFKB1 allele polymorphism in the tested groups Lenght Control Czech Control Australia LADA T1DM children T1DM adults n=34 % 1.5 1.5 2.9 x x 20.6 2.9 2.9 14.7 2.9 14.7 27.9 4.4 4.4 x n=55 % x x x 0.9 x 19.1 10.0 1.9 4.5 0.9 10.9 38.4 4.5 8.1 x n=67 % x x x x 1.5 23.1 6.0 2.2 11.2 13.4* 12.7 24.6 2.5 0.7 x (Curran et al., 2002) gene A01 A02 A03 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 (bp) 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 n=58 % x x x x x 23.2 10.3 4.3 7.8 1.7 8.6 35.3 2.6 2.6 1.7 n=109 % 2.9 2.9 x x x 23.5 x x 14.7 2.9 14.7 2.9 2.9 5.9 x * significant P value (P < 0.05) association was comparable between these two groups. The results of the study just confirm the previous results from the HLA typing studies, but were needed in the relation to the further NFKB1 and NFKBIA polymorphism testing. Distribution of NFKB1 polymorphism in Czech population 15 alleles for the NFKB1 gene polymorphism (CA-repeats) in the Czech population were detected so far. The alleles were ranging in size from 114-142 bp corresponding to 10–25 CA repeats. The complete results can be seen in Table 3. The most frequent allele size in the patients and the control group was 136 bp (35.3 %) corresponding to 22 CA repeats. The differences in allele frequencies, compared to healthy controls, were observed only in the group of T1DM adult patients. The frequency of A7 allele (size 132 bp, 20 repeats) was significantly increased in comparison with the control groups with a relative risk value of 1.88 (OR = 10.69, P < 0.01, CI = 95 %). Distribution of 3´ UTR NFKBIA polymorphism in Czech population The genotype frequencies of the NFKBIA gene polymorphism in the control and patient groups are presented in Table 4. There was no difference in A and G allele frequency between the control group and patients, the difference was observed in distribution of genotypes between the patient groups. There is an evidence that the heterozygous genotype of NFKBIA is protective for diabetes onset in adulthood, according to the results in LADA group (nonsignificantly) and T1DM adults (RR = 0.56, OR = 0.44, P < 0.01, CI = 95 %). In the mentioned groups the frequency of the heterozygotes is lowered and the frequency of the homozygous genotypes rises. A significant difference was observed for AA genotype in LADA group, with a relative risk value of 2.23 (OR = 2.68, P < 0.001, CI = 95 %) The AA and GG genotype frequencies in T1DM adults were increased, but with the border significance. Expression studies of HLA-DRB1*04 and NFKB1 The results obtained in mRNA quantification studies can be seen in Table 5 for HLA-DRB1*04 and in Table 6 for NFKB1 gene. HLA-DRB1*04. gene expression was tested first among different groups of patients. The significant difference was found only in mRNA expression levels of HLA-DRB1*04 between the group of T1DM children and the group of T1DM adults (P = 0.034). The expression of HLA-DRB1*04 was significantly higher in T1DM adult patients. The comparison was done with two specific control groups (positive for HLA-DRB1*04). Since there was no difference between the control groups with different age, we compared the control groups as one control set. No difference in the expression of HLA-DRB1*04 has been observed after analysis among different HLA clas II, NFKBIA and NFKB1 genotypes. No difference in the expression of NFKB1 gene has been observed among different types of diabetic patient groups and even after analysis based on the found HLA clas II, NFKBIA and NFKB1 genotypes. Discussion & Development of type 1 diabetes requires coordinated expression of genes responsible for initiation and progression of the disease, what is regulated by small number of transcription factors including the Rel/NF-kB family. In our study we have compared different groups of patients with autoimmune diabetes mellitus: 1) T1DM children 2) T1DM adults 3) LADA. Based on the HLA - typing studies, we have concluded that HLA – DR3 is significantly associated only with LADA (RR = 3.25) and children diabetes (RR = 1.51). HLA – DQB1*0302 has three times stronger relative risk for diabetes in childhood (RR = 8.98) than for diabetes in adults (RR = 3.02). The results are in concordance with previous testing in Czech population (Cerna et al., 2003; Cinek et al., 2001; Novota et al., 2004). Surprisingly, there was only a border significance of HLA-DQB1*0201 gene association in our T1DM children group. This is a difference from the results published in Czech population study by Cinek (Cinek et al., 2001). Generally, the found difference could be explained by different number of patients involved into the study what could change significance level. Based on NFKB1 and NFKBIA polymorphism studies, we have found out that A7 (132 bp) allele of NFKB1 gene presents a risk Katarina K et al. HLA, NFKB1 and NFKBIA Gene Polymorphism … Exp Clin Endocrinol Diabetes 2007; 115:124–129 128 Article Table 4 Frequencies of NFKBIA genotypes in the tested groups NFKBIA genotype Control Czech LADA T1DM children T1DM adults GG (00) AG (10) AA (11) n=159 % 27.0 55.6 16.9 n=31 % 22.6 45.2 32.3* n=173 % 31.8 55.5 12.7 n=64 % 35.9 35.9* 28.0 * significant P value (P < 0.05) Table 5 Statistical analysis of the data from relative mRNA quantification of the HLA-DRB1*04 gene Table 6 Statistical analysis of the data from relative mRNA quantification of the NFKB1 gene Group 1 2 3 Group 1 2 3 DRB1*04 T1DM children T1DM adults Controls NFKB1 T1DM children T1DM adults Controls Number of values Minimum Maximum Mean Std. Error 55 28 10 24 20 20 0,390 2,510 1,132 0,070 0,230 4,390 1,845* 0,221 0,600 1,410 1,050 0,105 Number of values Minimum Maximum Mean Std. Error 0,458 2,460 1,120 0,127 0,150 3,370 0,991 0,274 0,400 3,000 1,335 0,155 * significant P value (P=0.034) for our group of T1DM adults (RR = 1.88). In previous studies no association with NFKB1 A7 allele was affirmed. Several reports on the association study about NFKB1 and NFKBIA with T1DM, T2DM, celiac disease, rheumatoid arthritis, systemic lupus erythematosus, breast cancer in a variety of ethnic groups (United Kingdom, Denmark, Spain, Australia) exist (Curran et al., 2002; Gylvin et al., 2002; Karban et al., 2004; Rueda et al., 2004; Smyth et al., 2006). No T1DM association with any allele of the NFKB1 microsatellite marker could however be demonstrated in Danish T1DM families as reported previously (Gylvin et al., 2002; Karban et al., 2004). In contrast, the frequency of the A10 (138 bp) allele was significantly increased in patients with T1DM (0.17) compared with the normal controls (0.02) (Hegazy et al., 2001). However, the discrepancies in the frequencies of NFKB1 alleles in control sets of populations with different genetic origin show that it is not possible to compare the frequency of the risk alleles among various ethnics. The exact mechanism underlying the NFKB1 related disease susceptibility remains unknown. (Borm et al., 2005). It is known that 3´UTR is a regulatory region essential for the appropriate gene expression of many genes. Any variation in 3´UTR of NFKBIA gene could alter the function of IB. A longlasting sustained activation of NFkB in the absence of decreased IkB in mononuclear cells from patients with type 1 diabetes has been reported (Bierhaus et al., 2001). The significant association in NFKBIA polymorphism testing was found only for the AA homozygous genotype in 3´UTR in the LADA group. Similarly the heterozygous NFKBIA conformation seems to have significant advantage for T1DM adults; otherwise it would be such frequent. So far there is no evidence in literature about functional significance of our tested NFKB1 and NFKBIA polymorphisms. This was a reason why we decided to test NFKB1 and HLA-DRB1*04-gene expression on the mRNA level. There was not, however, any difference in the HLA-DRB1*04 and NFKB1 gene transcription between groups with different NFKBIA and NFKB1 genotypes. The mRNA transcript levels of HLA-DRB1*04 in circulating peripheral blood mononuclear cells differ significantly only in diabetic patients with different diabetes onset. We hypothesize that the increased HLA-DRB1*04 mRNA expression could be a protecting factor in a group of T1DM adults. Summarizing our results we conclude that there is a high probability of association Rel/NF-B family genes with an autoimmune diabetes course, but the function of the genetic variations needs to be examined further. Acknowledgments & This survey was funded by the Research Program of Czech Ministry of Education and Youth: Identification code: MSM 0021620814 (Prevention, diagnostics and therapy of diabetes mellitus, metabolic and endocrinal affections of organism). The authors would also like to thank Anezka Koubova, Miroslava Zaoralova and Ivana Spoljaricova for excellent technical assistance. References 1 Arkan MC, Hevener AL, Greten FR, Maeda S, Li ZW, Long JM, WynshawBoris A, Poli G, Olefsky J, Karin M: IKK-beta links inflammation to obesity-induced insulin resistance. Nat Med 2005; 11: 191–198 2 Baldwin AS Jr.: Series introduction: the transcription factor NF-kappaB and human disease. J Clin Invest 2001; 107: 3–6 3 Barnes PJ, Karin M: Nuclear factor-kappaB: a pivotal transcription factor in chronic inflammatory diseases. 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HLA, NFKB1 and NFKBIA Gene Polymorphism … Exp Clin Endocrinol Diabetes 2007; 115:124–129 Article 129 8 9 10 11 12 13 14 15 16 17 18 19 mune diabetes mellitus: comparison with Czech children with type 1 diabetes and patients with type 2 diabetes. Eur J Immunogenet 2003; 30: 401–407 Chen F: Is NF-kappaB a culprit in type 2 diabetes? Biochem Biophys Res Commun 2005; 332: 1–3 Cinek O, Kolouskova S, Snajderova M, Sumnik Z, Sedlakova P, Drevinek P, Vavrinec J, Ronningen KS: HLA class II genetic association of type 1 diabetes mellitus in Czech children. Pediatr Diabetes 2001; 2: 98– 102 Csorba TR, Lyon AW: Abnormal proinsulin congeners as autoantigens that initiate the pathogenesis of Type 1 diabetes. Med Hypotheses 2005; 64: 186–191 Curran JE, Weinstein SR, Griffiths LR: Polymorphic variants of NFKB1 and its inhibitory protein NFKBIA, and their involvement in sporadic breast cancer. Cancer Lett 2002; 188: 103–107 Evans JL, Goldfine ID, Maddux BA, Grodsky GM: Oxidative stress and stress-activated signaling pathways: a unifying hypothesis of type 2 diabetes. Endocr Rev 2002; 23: 599–622 Gylvin T, Bergholdt R, Nerup J, Pociot F: Characterization of a nuclearfactor-kappa B (NFkappaB) genetic marker in type 1 diabetes (T1DM) families. Genes Immun 2002; 3: 430–432 Haslbeck KM, Schleicher E, Bierhaus A, Nawroth P, Haslbeck M, Neundorfer B, Heuss D: The AGE/RAGE/NF-(kappa)B pathway may contribute to the pathogenesis of polyneuropathy in impaired glucose tolerance (IGT). Exp Clin Endocrinol Diabetes 2005; 113: 288–291 Hayden MS, Ghosh S: Signaling to NF-kappaB. Genes Dev 2004; 18: 2195–2224 Hegazy DM, O’Reilly DA, Yang BM, Hodgkinson AD, Millward BA, Demaine AG: NFkappaB polymorphisms and susceptibility to type 1 diabetes. Genes Immun 2001; 2: 304–308 Heron E, Deloukas P, van Loon AP: The complete exon-intron structure of the 156-kb human gene NFKB1, which encodes the p105 and p50 proteins of transcription factors NF-kappa B and I kappa B-gamma: implications for NF-kappa B-mediated signal transduction. Genomics 1995; 30: 493–505 Jaini R, Kaur G, Mehra NK: Heterogeneity of HLA-DRB1*04 and its associated haplotypes in the North Indian population. Hum Immunol 2002; 63: 24–29 Karban AS, Okazaki T, Panhuysen CI, Gallegos T, Potter JJ, Bailey-Wilson JE, Silverberg MS, Duerr RH, Cho JH, Gregersen PK, Wu Y, Achkar JP, Dassopoulos T, Mezey E, Bayless TM, Nouvet FJ, Brant SR: Functional 20 21 22 23 24 25 26 27 28 29 annotation of a novel NFKB1 promoter polymorphism that increases risk for ulcerative colitis. Hum Mol Genet 2004; 13: 35–45 Lechler RI, Norcross MA, Germain RN: Qualitative and quantitative studies of antigen-presenting cell function by using I-A-expressing L cells. J Immunol 1985; 135: 2914–2922 Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001; 25: 402–408 Novota P, Cerna M, Kolostova K, Cejkova P, Zdarsky E, Novakova D, Kucera P, Novak J, Andel M: Diabetes mellitus in adults: association of HLA DRB1 and DQB1 diabetes risk alleles with GADab presence and C-peptide secretion. Immunol Lett 2004; 95: 229–232 Ota N, Nakajima T, Shirai Y, Emi M: Isolation and radiation hybrid mapping of a highly polymorphic CA repeat sequence at the human nuclear factor kappa-beta subunit 1 (NFKB1) locus. J Hum Genet 1999; 44: 129–130 Rewers A, Babu S, Wang TB, Bugawan TL, Barriga K, Eisenbarth GS, Erlich HA: Ethnic differences in the associations between the HLA-DRB1*04 subtypes and type 1 diabetes. Ann N Y Acad Sci 2003; 1005: 301– 309 Rueda B, Lopez-Nevot MA, Ruiz MP, Ortega E, Maldonado J, Lopez M, Martin J: CA microsatellite polymorphism of the nuclear factor kappa B1 gene in celiac disease. Eur J Immunogenet 2004; 31: 129–132 Shawkatova I, Fazekasova H, Michalkova D, Martinka E: Association of type I diabetes mellitus with HLA alleles]. Bratisl Lek Listy 2000; 101: 624–625 Smyth DJ, Howson JM, Payne F, Maier LM, Bailey R, Holland K, Lowe CE, Cooper JD, Hulme JS, Vella A, Dahlman I, Lam AC, Nutland S, Walker NM, Twells RC, Todd JA: Analysis of polymorphisms in 16 genes in type 1 diabetes that have been associated with other immune-mediated diseases. BMC Med Genet 2006; 7: 20 Wheat W, Kupfer R, Gutches DG, Rayat GR, Beilke J, Scheinman RI, Wegmann DR: Increased NF-kappa B activity in B cells and bone marrowderived dendritic cells from NOD mice. Eur J Immunol 2004; 34: 1395–1404 World Health Organization Definition Diagnosis and Classification of Diabetes Mellitus and its Complications. Geneva, Switzerland: Department of Noncommunicable Disease Surveillance. WHO, 1999 Katarina K et al. HLA, NFKB1 and NFKBIA Gene Polymorphism … Exp Clin Endocrinol Diabetes 2007; 115:124–129 Original paper No. 9 EXTRAPITUITARY PROLACTIN PROMOTER POLYMORPHISM IN CZECH PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS AND RHEUMATOID ARTHRITIS Fojtikova M, Cerna M, Cejkova P, Ruzickova S, Dostal C Ann Rheum Dis. 2007 May;66(5):706-7 137 1 LETTER Extrapituitary prolactin promoter polymorphism in Czech patients with systemic lupus erythematosus and rheumatoid arthritis Markéta Fojtı́ková, Marie Černá, Pavlı́na Čejková, Šárka Růžičková, Ctibor Dostál ................................................................................................................................... Ann Rheum Dis 2007;000:1–2. doi: 10.1136/ard.2006.061788 P rolactin (PRL) and its production by lymphocytes have been suggested to play a distinct role in the pathogenesis of systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA).1 2 PRL acts as a cytokine and influences the maturation and differentiation of immune cells.3 4 Extrapituitary PRL synthesis is regulated by an alternative promoter,5 which contains a single-nucleotide polymorphism (SNP) at the region 21149 G/T. Higher PRL mRNA expression is associated with the G allele in lymphocytes.6 High frequency of the G allele was described in patients with SLE,7 but was not confirmed in other works.8 We investigated 21149 G/T SNP in 156 patients with SLE and 173 patients with RA, and in 123 healthy individuals (control group). Patients with SLE consisted of: 134 (85.9%) women and 22 (14.1%) men, with a mean age of 43.4 years. SLE diagnosis was determined using the American College of Rheumatology classification criteria.9 Patients with RA consisted of: 132 (76.3%) women and 41 (23.7%) men, with a mean age of 57.4 years; all fulfilled the RA diagnostic criteria.10 The control group consisted of 40 (32.5%) women and 83 (67.5%) men, with a mean age of 38.7 years. The study was approved by the ethics committee of The Third Medical Faculty, Charles University, Prague, Czech Republic. The PCR—reverse fragment length polymorphism method was used for 21149 G/T SNP detection. During PCR, the 137 base pairs (bp) region of the PRL extrapituitary promoter was amplified by using the following primers: forward 59GCAGGTCAAGATAACCTGGA and reverse 59CATCTCAGAGTTGAATTTATTTCCTT. For reverse fragment length polymorphism, ApoI restriction endonucleases were used. The genotypes identified were TT homozygote characterised by 120 and 17 bp, GG homozygote characterised by 85, 35 and 17 bp, and GT heterozygote characterised by 120, 85 and 35 bp + 17 bp DNA fragments. Results were evaluated by x2 test with Bonferroni correction. There was no difference in genotype and allele frequencies in the SLE group compared with healthy Czech individuals (table 1). Our results support previous Italian findings,8 but differ from the UK report.7 However, with respect to the specific organ manifestation of SLE (table 1), we detected an association between the G allele and articular involvement (Pc = 0.0086, OR 2.56, 95% CI 1.51 to 4.33). Based on age when SLE was diagnosed, we observed a GG genotype frequency of 44.8% in the 21–40 years subgroup compared with 15.8% and 24.0% in the ,20 years and .40 years subgroup, respectively (Pc = 0.023, OR 2.94, 95% CI 1.43 to 5.96). Additionally, we correlated the presence of alleles and genotypes of 21149 G/T SNP with antibodies against antinuclear antibody, double-stranded DNA, Sm, RNP, Ro and La, but no connection was found (data not shown). Annals of the Rheumatic Diseases ar61788 Module 4 28/2/07 10:57:25 Significantly higher heterozygote GT genotype was detected in the RA group compared with the controls (Pc = 0.039, OR 1.82, 95% CI 1.14 to 2.94). We observed no differences in homozygote genotypes or in allele frequencies between patients with RA and controls (table 1). Across the groups, no gender differences in genotype distribution or allele frequencies were identified (data not shown). In conclusion, the presence of the G allele and GG genotype of the PRL extrapituitary promoter 21149 G/T SNP is associated with certain clinical features of SLE. The GT genotype is a predisposing genetic factor for RA. Further investigation on this is required. ACKNOWLEDGEMENTS This study was financially supported by the research programme of the Ministry of Health of the Czech Republic—IGA MZ CR: NR/8191-3/ 2004. ....................... Authors’ affiliations Markéta Fojtı́ková, Ctibor Dostál, Institute of Rheumatology, Na Slupi 4, Prague, Czech Republic Marie Černá, Pavlı́na Čejková, Department of Cell and Molecular Biology, Third Faculty of Medicine, Charles University in Prague, Prague, Czech Republic Šárka Růžičková, Department of Molecular Biology and Immunogenetics, Institute of Rheumatology, Na Slupi 4, Prague, Czech Republic Competing interests: None declared. Correspondence to: Dr M Fojtı́ková, Department of Cell and Molecular Biology, Third Faculty of Medicine, Charles University in Prague, Ruská 87, 100 00 Prague 10, Czech Republic; [email protected] Accepted 13 January 2007 REFERENCES 1 Leańos-Miranda A, Cárdenas-Mondragon G. Serum free prolactin concentrations in patients with systemic lupus erythematosus are associated with lupus activity. Rheumatology 2006;45:97–101. 2 Neidhart M, Gay RE, Gay S. Prolactin and prolactin-like polypeptides in rheumatoid arthritis. Biomed Pharmacother 1999;53:218–22. 3 Matera L. Endocrine, paracrine and autocrine actions of prolactin on immune cells. Life Sci 1996;59:599–614. 4 Grimaldi CM. Sex and systemic lupus erythematosus: the role of the sex hormones estrogen and prolactin on the regulation of autoreactive B cells. Curr Opin Rheumatol 2006;18:456–61. 5 Gellersen B, Kempf R, Telgmann R, DiMattia GE. Nonpituitary human prolactin gene transcription is independent of Pit-1 and differentially controlled in lymphocytes and in endometrial stroma. Mol Endocrinol 1994;8:356–73. 6 Stevens A, Ray D, Alansari A, Hajeer A, Thomson W, Donn R, et al. Characterization of a prolactin gene polymorphism and its associations with systemic lupus erythematosus. Arthritis Rheum 2001;44:2358–66. Abbreviations: PRL, prolactin; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus; SNP, single-nucleotide polymorphism www.annrheumdis.com Topics: 2 Fojtı´ková , Černá , Čejková , et al Table 1 Occurrence of genotype and allele frequencies of 21149 G/T single-nucleotide polymorphism of the extrapituitary prolactin promoter in Czech patients with systemic lupus erythematosus (SLE) and rheumatoid arthritis and controls, and in patients with SLE according to specific organ involvement 21149 G/T SNP of the extrapituitary prolactin promoter Genotype Subject groups GG SLE Pc1 Specific organ involvement Renal With Without Pc2 With Without Pc2 With Without Pc2 With Without Pc2 With Without Pc2 With Without Pc2 Neuropsychiatric Cardiac Pulmonary Articular Dermal RA Pc3 Controls Allele frequency GT TT Number of cases % % % 156 34.60 NS 44.90 NS 63 93 31.10 37.60 NS 30.30 35.80 NS 28.60 36.40 NS 32.00 35.10 NS 41.00 15.40 NS 36.20 32.30 NS 32.40 NS 39.80 52.40 39.80 NS 51.50 43.10 NS 45.70 44.60 NS 48.00 44.30 NS 43.60 48.70 NS 45.70 43.50 NS 56.10 0.039* 41.50 33 123 35 121 25 131 117 39 94 62 173 123 G T 20.50 NS 0.57 NS 0.43 NS 17.50 22.60 NS 18.20 21.10 NS 25.70 19.00 NS 20.00 20.60 NS 15.40 35.90 NS 18.10 24.20 NS 11.50 NS 18.70 0.56 0.57 NS 0.56 0.57 NS 0.51 0.59 NS 0.56 0.57 NS 0.63 0.40 0.0086 0.59 0.54 NS 0.60 NS 0.61 0.44 0.43 NS 0.44 0.43 NS 0.49 0.41 NS 0.44 0.43 NS 0.37 0.60 0.0086` 0.41 0.46 NS 0.40 NS 0.39 Pc, P corrected values; RA, rheumatoid arthritis; SLE, systemic lupus erythematosus; SNP, single-nucleotide polymorphism. Pc values were calculated by the x2 test from 262 contingency tables of the separate genotype occurrence or allele frequency of (1) each patient with SLE and control group (Pc1), (2) presence or absence for the clinical feature (Pc2) and (3) each patient with RA and control group (Pc3). Bonferroni correction for multiple comparisons was used. *OR 1.82, 95% CI 1.14 to 2.94. OR 2.56, 95% CI 1.51 to 4.33. ` OR 0.39, 95% CI 0.23 to 0.6. 7 Stevens A, Ray DW, Worthington J, Davis JR. Polymorphisms of the human prolactin gene—implications for production of lymphocyte prolactin and systemic lupus erythematosus. Lupus 2001;10:676–83. 8 Mellai M, Giordano M, D’Alfonso S, Marchini M, Scorza R, Giovanna Danieli M, et al. Prolactin and prolactin receptor gene polymorphisms in multiple sclerosis and systemic lupus erythematosus. Hum Immunol 2003;64:274–84. 9 Hochberg MC. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum 1997;40:1725. 10 Arnett FC. Revised criteria for the classification of rheumatoid-arthritis. Bull Rheum Dis 1989;38:1–6. www.annrheumdis.com Annals of the Rheumatic Diseases ar61788 Module 4 28/2/07 10:57:25 Topics: Original paper No. 10 POLYMORPHISM OF THE PROLACTIN EXTRAPITUITARY PROMOTER IN PSORIATIC ARTHRITIS Stolfa J, Fojtkova M, Cejkova P, Cerna M, Sedova L, Dostal C Rheumatol Int. 2007 Sep;27(11):1095-6 140 Rheumatol Int (2007) 27:1095–1096 DOI 10.1007/s00296-007-0360-3 LE TTER TO THE EDIT OR Polymorphism of the prolactin extrapituitary promoter in psoriatic arthritis Jilí Ktolfa · Markéta Fojtíková · Pavlína Bejková · Marie Berná · Liliana Kedová · Ctibor Dostál Received: 24 January 2007 / Accepted: 20 April 2007 / Published online: 13 June 2007 © Springer-Verlag 2007 Psoriatic arthritis is characterized as seronegative arthritis that aVects patients suVering from psoriasis [1]. Aetiology of this condition is still not clear and therapeutic approaches are not always successful. However, good response to bromocriptine therapy decreasing prolactin levels in patients with psoriatic arthritis has been demonstrated in some reports [2, 3]. Prolactin acts as a cytokine and plays a role in pathogenesis of systemic autoimmune diseases such as rheumatoid arthritis and systemic lupus erythematosus [4, 5]. Moreover, high serum prolactin levels were observed in group of patients with psoriasis and link between keratinocytes hyperproliferation and prolactin has been proposed [6]. The peptide hormone prolactin is produced from pituitary lactotrophs and extrapituitary tissues such as lymphocytes as well. Extrapituitary PRL production is regulated by an alternative promoter located 5.8 kb upstream from the pituitary one [7, 8]. A functional single nucleotide polymorphism (SNP) G/T at the position ¡1149 of this extrapituitary promoter has been observed and in lymphocytes higher PRL mRNA expression found to be connected with G allele [9]. In our work we studied ¡1149G/T SNP PRL in group of 83 Czech patients with psoriatic arthritis (PsA). We genotyped ¡1149G/T SNP in 83 PsA patients and 123 healthy individuals (control group). PsA patients: 43 (51.8%) females, 40 (48.2%) males, average age 54.1 years. J. Ktolfa · M. Fojtíková (&) · L. Kedová · C. Dostál Institute of Rheumatology, Na Slupi 4, 128 50 Prague 2, Czech Republic e-mail: [email protected] P. Bejková · M. Berná Department of Cell and Molecular Biology, Third Faculty of Medicine, Charles University in Prague, Ruská 87, 100 00 Prague 10, Czech Republic Control group: 40 (32.5%) females and 83 (67.5%) males, average age 38.7 years. This study was approved by the Ethical Committee of the Third Faculty of Medicine, Charles University, Prague. PCR-RFLP methodology was used for ¡1149G/T SNP detection. PCR: The 137 bp region of the PRL extrapituitary promoter was ampliWed by employing following primers: Forward primer 5⬘ GCAGGTCAAGATAACCTGGA and reverse primer 5⬘ CATCTCAGAGTTGAATTTATT TCCTT. The PCR reaction was run under these conditions: initial temperature 94°C for 2 min, then 32 cycles with 94°C for 17 s, 55°C for 17 s, 72°C for 17 s, and Wnal temperature 72°C for 1 min. RFLP: ApoI restriction endonuclease was used. The three genotypes were identiWed as indicated: the homozygote TT was characterized by 120 + 17 bp, the homozygote GG by 85 + 35 + 17 bp, and the heterozygote GT genotype by 120 + 85 + 35 + 17 bp DNA fragments. Results were evaluated by Chi square test with Bonferroni correction. The genotypes and alleles frequency of ¡1149G/T SNP PRL extrapituitary promoter were similar in group of PsA patients and control group (Table 1). We did not Wnd any diVerences in genotype and allele distribution between healthy female and male and between female and male patients with psoriatic arthritis, respectively (data not shown). Moreover, we correlated genotype and allele distribution of ¡1149G/T SNP with clinical feature of psoriatic arthritis, such as age of onset psoriasis and radiological type of psoriatic arthritis. In Table 2 there are results of this correlation, where no signiWcant association was detected. In conclusion, ¡1149G/T SNP of the extrapituitary prolactin promoter is not associated with psoriatic arthritis (see Table 1) and its characteristic such as the onset of skin lesion and radiological type of arthritis (see Table 2). Our Wndings could indicate that ¡1149G/T SNP prolactin 123 1096 Rheumatol Int (2007) 27:1095–1096 Table 1 Genotype and allele frequency of the ¡1149G/T SNP prolactin extrapituitary promoter in patients with psoriatic arthritis and healthy controls Genotype and allele PsA N (%) Controls N (%) P corr. GG 31 (37.3) 49 (39.8) NS GT 38 (45.8) 51 (41.5) NS TT 14 (16.9) 23 (18.7) NS G 100 (60.2) 149 (60.6) NS T 66 (39.8) 97 (39.4) NS SNP single nucleotide polymorphism, PRL prolactin, PsA psoriatic arthritis, N number of cases, NS non signiWcant, P corr. P corrected values were calculated by the Chi squared test from 2 £ 2 contingency tables; Bonferroni correction for multiple comparisons was applied Table 2 Genotype and allele frequency of the ¡1149G/T SNP extrapituitary prolactin promoter and clinical features of psoriatic arthritis Type PsA N Genotype Allele frequency GG (%) GT (%) TT (%) G T Erosive 55 36.3 50.9 12.8 0.62 0.38 Non-erosive 28 39.3 35.7 25.0 0.57 0.43 P corr. NS NS NS NS NS Type Ia 58 36.2 50.0 13.8 0.61 0.39 Type IIb 22 40.9 31.8 27.3 0.57 0.43 P corr. NS NS NS NS NS Type PV c SNP single nucleotide polymorphism, PRL prolactin, PsA psoriatic arthritis, PV psoriasis vulgaris, N number of cases, NS non signiWcant, P corr. P corrected values were calculated by the Chi squared test from 2 £ 2 contingency tables; Bonferroni correction for multiple comparisons was used a Type I—onset of skin lesions before 40 years b Type II—onset of skin lesions after 40 years c 3 patients were without skin lesions, but classiWed as psoriatic arthritis extrapituitary promoter does not belong to pathogenetic factors of psoriatic arthritis, and thus conWrm the results of the same analysis in juvenile idiopathic arthritis [10], but 123 not in SLE, that found G allele of ¡1149G/T SNP to be a risk factor [8]. Possible alteration of prolactin levels in psoriatic arthritis might be caused by regulation of prolactin release from pituitary gland. Nevertheless, further investigations for explaning the role of prolactin in psoriasis and psoriatic arthritis pathogenesis are necessary. Acknowledgment Supported by the Research program of the Ministry of health of Czech Republic: IGA MZ CR: NR/8191¡3/2004. References 1. Wright V (1989) What is the best treatment approach for a patient with the mutilating pattern of psoriatic peripheral arthritis. Br J Rheumatol 28:382 2. Buskila D, Sukenik S, Holcberg G, Horowitz J (1991) Improvement of psoriatic arthritis in a patient treated with bromocriptine for hyperprolactinemia. J Rheumatol 18:611–612 3. McMurray RW (2001) Bromocriptine in rheumatic and autoimmune diseases. Semin Arthritis Rheum 31:21–32 4. Giasuddin ASM, El Sherif AI, El Ojali SI (1998) Prolactin: does it have a role in the pathogenesis of psoriasis? Dermatology 197:119–122 5. Leajos-Miranda A, Cárdenas-Mondragon G (2006) Serum free prolactin concentrations in patients with systemic lupus erythematosus are associated with lupus activity. Rheumatology 45:97– 101 6. Nagafuchi H, Suzuki N, Kaneko A, Asai T, Sakane T (1999) Prolactin locally produced by synovium inWltrating T lymphocytes induces excessive synovial cell functions in patients with rheumatoid arthritis. J Rheumatol 26:1890–1900 7. Gellersen B, Kempf R, Telgmann R, DiMattia GE (1994) Nonpituitary human prolactin gene transcription is independent of Pit¡1 and diVerentially controlled in lymphocytes and in endometrial stroma. Mol Endocrinol 8:356–373 8. Stevens A, Ray D, Alansari A, Hajeer A, Thomson W, Donn R, Ollier WE, Worthington J, Davis JR (2001) Characterization of a prolactin gene polymorphism and its associations with systemic lupus erythematosus. Arthritis Rheum 44:2358–2366 9. Stevens A, Ray DW, Worthington J, Davis JR (2001) Polymorphisms of the human prolactin gene—implications for production of lymphocyte prolactin and systemic lupus erythematosus. Lupus 10:676–683 10. Donn RP, Farhan A, Stevans A, Ramanan A, Ollier WE, Thomson W; British Paediatric Rheumatology Study Group (2002) Neuroendocrine gene polymorphisms and susceptibility to juvenile idiopathic arthritis. Rheumatology (Oxford) 41:930–936 CHAPTER 4 CONCLUSIONS 1. Both NOTCH1 and ABL1 genes are afflicted by amplification of 9q34 locus in ETL; however, NOTCH1 seems to be a primary target of genomic DNA amplification. The ABL1 amplification detected by microsatellite analysis and QPCR was positive in 38.5% (D9S290)/30.8% (D9S1847) and 50% cases, respectively. The NOTCH1 amplification was positive in 15.4% (D9S158) and 65.4% cases (detected by QPCR). 2. A. i The HLA alleles associated with T1D in children do not differ from those in adult-onset T1D; however, in childhood they confer higher risk (HLADRB1*03: P < 0.0001, OR = 3.3 in children vs. P < 0.05, OR = 3.1 in adults; HLA-DRB1*04: P < 0.0001, OR = 22.9 in children vs. P < 0.05, OR = 3.1 in adults; HLA-DQB1*0302: P < 0.0001, OR = 46.5 in children vs. P = NS in adults) suggesting a possible role of environmental factors, that increases with age, and at least partly different immunogenetic etiology between autoimmune DM manifested in childhood and in adults. 2. A. ii Adult onset T1D and LADA have a partially different predisposition genes. The HLA-DRB1*04, HLA-DRB1*03 (P < 0.05, OR = 3.1 for both) have been detected as predisposing factors in T1D in adults, whilst the HLA-DRB1*03 allele (P < 0.01, OR = 3.4) has been a single risk factor for LADA. There was the association of AA genotype of NFKBIA gene with LADA (P < 0.001 OR=2.7). 143 We did not confirm the association of the INS-VNTR (–23HphI A/T SNP) with T1D. Similarly, there was no association of the Kir6.2 E23K SNP with T2D. The IDDM1 and IDDM2 diabetic loci have cumulative effect: the simultaneous presence of the INS –23HphI AA genotype and HLA-DRB1*03 allele increases risk of T1D development almost two times (from OR = 3.1 for presence of HLA-DRB1*03 to OR = 5.9 for presence of both markers). 2. A. iii The INS –23HphI SNP A allele and AA genotype seem to be a risk factor for production of C-peptide/insulin by β-cells of pancreas (P < 0.004, OR = 3.3 for A allele and OR = 3.54 for AA genotype). There was no association between the polymorphism and GADA levels. In contrast, the HLA-DRB1*03 allele is a marker of GADA development (P < 0.0001, OR = 4.2). 2. B. Analysis of the HLA-DRB1*04 expression in peripheral blood APCs revealed significantly increased quantity of mRNA in T1D adults when compared to T1D in children; however, no difference in HLA-DRB1*04 and NFKB1 gene expression between T1D patients with different HLA, NFKB1, NFKBIA genetic background was found. 2. C. The analysis of PRL -1149 G/T polymorphism did not reveal any association with systemic lupus erythematosus or psoriatic arthritis in the Czech population, whereas significantly increased heterozygous genotype was detected in patients with rheumatoid arthritis (P < 0.05). Nevertheless, in SLE, G allele has been confirmed a risk factor for joint involvement (OR = 2.56, P < 0.01). 144 REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. Dipple, K.M. and E.R. McCabe, Modifier genes convert "simple" Mendelian disorders to complex traits. Mol Genet Metab, 2000. 71(1-2): p. 43-50. Knight, J.C., Regulatory polymorphisms underlying complex disease traits. 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